naval weapons station, concord export capability : a

NAVAL WEAPONS STATIONCONCORD EXPORT CAPABILITY

A SIMULATION MODEL.

David C. Fountain

7

NAVAL POSTGRADUATE SCHOOL

Monterey, California

THESISNAVAL WEAPONS STATION, CONCORD

EXPORT CAPABILITY:A SIMULATION MODEL

by

David C. Fountain

June 1977

Thesis Advisor Robert W. Sagehorn

Approved for public release; distribution unlimited

T 179878

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1. REPORT NUMBER 2. GOVT ACCESSION NO 3. RECIPIENT'S CATALOG NUMBER

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NAVAL WEAPONS STATION, CONCORD EXPORTCAPABILITY: A SIMULATION MODEL

5. TYPE OF REPORT & PERIOD COVERED

Master's Thesislimp 1977

6. PERFORMING ORG. REPORT NUMBER

7. AUTHORf«;

David C. Fountain

8. CONTRACT OR GRANT NUMBERS.)

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Naval Postgraduate SchoolMonterey, California 93 9M-0

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Naval Postgraduate SchoolMonterey, California 93940

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17. DISTRIBUTION STATEMENT (ol tha mbatrmct anfrad In Block 30, II dlllmranl /ram Rapott)

18. SUPPLEMENTARY NOTES

19. KEY WORDS Conrinut on rmrmram aid* It nmcmmamry and Idantlty by block numbar)

SLmulation, Water ports, Transportation, Export capability,Ammunition, TRANSIM

20. ABSTRACT (Conllnxim on ravaraa aid* II nacaaamrr and Identity by block mmtbat)

This paper represents an analysis of the ocean ammunitionexport function of the Naval Weapons Station at Concord,California. The analysis, utilizing the general purposetransportation simulator TRANSIM, defines the system and adaptsit to TRANSIM notation. The objective of the project was toestablish a workable model and use it to predict the maximumexport capability of NWS, Concord. Five simulations were

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NAVAL WEAPONS STATION, CONCORD EXPORT CAPABILITYA SIMULATION MODEL

by

David C. FountainCaptain, United States Army

3. S. , university of Washington, 1967

Submitted in partial fulfillment of therequirements for the degree of

MASTER 0? SCIENCE IN MANAGEMENT

from the

NAVAL POSTGRADUATE SCHOOL

June 1977

WtttYWOX LIBRARY

NAVAl POSTGRADUATE SCHOOL

ABSTRACT

This paper represents an analysis of the ocean

ammunition export function of the Naval Weapons

Station at Concord, California. The analysis,

utilizing the general purpose transportation simulator

rSANSIM, defines the system and adapts it to TRANSIM

notation. Tha objective of the project *as to

establish a workable model and ase it to predict the

maximum export capability of MWS, Concord. Five

simulations were completed; three replicated the

historical data to establish the model's workability

and the other two indicated increased capabilities

possible.

flMM ! KM'

TABLE OF CONTENTS

I. BACK GROUND 11

II. INTRODUCTION 13

III. INTENT OF THE PROJECT 17

IV. ASSUMPTIONS 19

V. i::E NAVAL WEAPONS STATION, CONCORD TIDAL AREA.... 21

A. PLANNING CONSPIRATIONS 21

3. VAN AND RAILCAR MOVEMENT PROCEDURES 22

C. AREA CAPABILITIES 23

D. OPERATING TIMES 26

71. THE T3ANSIM VIS* OF NNS, CONCDRD 23

A. SYSTEM BOUNDARIES 28

B. cASIC ELEMENTS OF THE MODEL 29

C. ALTERNATIVE APPROACHES 31

D. CAIA ELEMENTS 3 3

1. Capacity Elsaants 33

2. Time Elements 3 4

E. SYSTEM SUMMARY 37

711. THE DERIVATION OF THE PARAMETERS 3 9

A. PARAMETERS HELD CONSTANT 39

E. PARAMETERS TO 3S VARIED IN SIMULATIONS 39

1. Distribution of Vessel Classes 41

2. 7essel Class Berth Times 4 3

3. Vans and Railcars per Vessel Class.. 47

4. Unloaling Times of Vans and Railcars 48

5. Int erarri va 1 Time for Trains 49

6. Interarrival Time of Vessels 49

7. In tec arrival limes for Trucks 5

3. Number of Railcars per Trains 51

VIII. RESULTS 52

IX. CONCLUSIONS 57

X. RECOHMENDATION 59

Appendix A: OPERATING RULES OF THE MODEL 60

Appendix E: SAMPLE OPERATING RULES b4

Appendix C: INITIAL SITUATION IN THE MODEL 72

Appendix D: ITERATIVE PROCESS OF TBAMSIfl 74

Appeniix E: RESULTS OF SIMULATION RUN 1 32

Appendix F: RESULTS OF SIMULATION RUN II 93

Appendix G: RESULTS OF SIMULATION RUN III 104

Appendix H: RESULTS OF SIMULATION RUN IV 115

Appendix I: RESULTS OF SIMULATION RUN V 126

Appendix J: VESSEL EXPORT TONNAGES IN CLASS GROUPINGS

FOR JANUARY THROUGH JUNE 196 3 137

Appendix X: THE TRIANGULAR DISTRIBUTION 139

LIST OF REFERENCES 140

BIBLIOGRAPHY 141

INITIAL DISTRIBUTION LIST 143

LIST OF FIGURESy

LIST 0? FIGURES

1. Generalized Systeoi 14

2. Hap of N'rfS, Concord Tidal Area 25

3. Detailed Schematic 30

4. Vessel Class Distribution... 40

5. Berth Ii;nes by Vessel Class 42

6. Actual vs. Simulated Tons per Vessel Class 44

7. Actual and Theoretical Numbers a: Vans and Railcurs

per Vessel Class 4 6

8. Composite Results 55

9. Actual Numbers of Vessels in Each Class for Each

Simulation 56

10. Sample Operating Pules Input 65

11. Sample Operating Rules Input (cont.) 56

12. Sample Operating Rules Input (cont .

)

67

13. Sample Operating Rules Input (cont .) 68




17. Initial Data in the Model 73

13. Sample IRANSIH Iterations 76

19. Sample IRAN SIM Iterations (cont.) 77

20. Sample TRANSIT Iterations (com.) 73

21. Sample 1RANSIM Iterations (cor.t.) 79

22. Sample TRANSI.1 Iterations (cor.t.) 80

23. Sample TRANSIT Iterations (cor.t.) 51

24. Occupancy of All Berths (Sim. I) 3'J

25. Total Truck Cargo Exported (Sim. I) 85

26. Total Rail Cargo Exported (Sim. I) ^6

27. Ship Time at All Berths (Sim. I) 37

23. Ship Delay Times (Sim. I) 38

29. Number of Vans in Storage Ar^as (Sim. I) 39

30. Storage Times of Vans (Sim. I) 90

31. Number cf Trains at Storage Area (Sim. I) 91

32. Storage Times of Ruilcars (Sim. I) 92

33. Occupancy of All Berths (Sim. II) 35

34. Total Truck Cargo Exported (Sim. II) 36

35. Total Rail Cargo Exported (Sim. II) 97

3o. Ship TiTe at All Berths (Sim. II) 98

37. Ship Delay Times (Sim. II) 99

33. Number cf Vans in Storage Areas (Sim. II) 100

39. Storage Times of Vans (Sim. II) 10 1

uo. Numo^r cf Trains at Storage Area (Sim. IT) 102

41. Storage Times of Railcars (Sim. II) .....103

42. Occapancy of All Berths (Sim. Ill) 106

43. Total Truck Bargo Exported (Sim. Ill) 107

44. Total Rail Cargo Exported (Sim. Ill) 108

45. Ship Tine at All Berths (Sim. Ill) 109

46. Ship Delay Times (Sin. Ill) 110

47. Number cf Vans in Storage Areas (Sim. Ill) 111

48. Storage Times of Vans (Sim. Ill) 112

49. Number cf Trains at Storage Area (Sim. Ill) 113

50. Storage Times of Railcars (Sim. Ill) 114

51. Occupancy of All Berths (Sim. IV) -117

52. Total Track: Cargo Exported (Sim. IV) 118

53. Total Rail Cargo Exported (Sim. IV) 119

54. Ship Tine at All Berths (Sim. IV) 120

55. Ship Delay Times (Sim. IV) ...121

56. Number cf Vans in Storage Areas (Sim. IV) 122

57. Storage Times of Vans (Sim. IV) 123

53. Number cf Trains at Storage Area (Sim. IV) 124

59. Storage Times of Railcars (Sim. IV) 125

60. Occupancy of All 3erths (Sim. V) 128

61. Total Truck Cargo Exported (Sim. V) 129

62. Total Rail Cargo Exported (Sim. V) 130

63. Ship Tiiie at All Bertns (Sim. V) 131

64. Ship Delay Times (Sim. V) 132

o5. Number cf Vans in Storage Areas (Sim. V) 133

66. Storage Times of Vans (Sim. V) 134

67. Number cf Trains at Storage Area (Sim. V) 135

68. Storage Times of Railcars (Sim. V) 136

69. Vessel Export Tonnages in Class Groupings for

January through June 1963 133

70. A Triangular Distribution 139

10

I. BACKGROUND

The Naval Weapons Station (NWS), Concord is located in

California about thirty-five miles northeast of San

Francisco and seventy miles southwest of Sacramento.

Although the station comprises three activities at sites

near Concord, Pittsburg, and Vallejo, this study concerned

itself with only the Concord activity.

Of the -many missions assigned to NWS, Concord, only two

have relevance to this study. The two missions that related

directly to the sxport capability are the following:

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te and maintain an oceanto transship ordnance materialArmed Forces. .

.

logistics au i port terminalcort of Pacific Fleet Ammunitionorted at the Station... "1-1].

Although NWS, Concord is a Wavy owned an i operated

installation, by its rather unique position as the only

major West Coast ammunition ocean terminal, it is required

to support the other services and is thereoy critical to the

Department of Defense. The facility is strictly Naval but

it does report for administration and accounting purposes to

the Oakland Terminal of the Military Traffic Management

Command, the Department of Defense agency responsible for

worldwide traffic management. NWS, Concord is the

ammunition counterpart of Oakland, the primary West Coast

1 1

general cargo port. (The Oakland facility is about

twenty-five miles southwest of Concord.)

The facility at Concord is divided into the Inland Ar ?a

and the Tidal Area. The Inland Area is concerned with

research, administration, military housing, and similar

activities, while the Tidal Area is concerned with the

operational aspects of the transshipment of ammunition.

12

INTRODUCTION

The analysis of NWS, Coacor.l's capabilities was

accomplished by using as a basis the simulation model

TRANSIT. TRANSIrt is a jeneral purpose? transportation system

simulator u2veloped originally in 1366 by the Department of

Engineering, University of California at Los Angeles (UCLA)

for the Undersecretary of Transportation in the U.S.

Department of Commerce. The continuing effort of the

Project THANSIH office at UCLA is to update and simplify the

program, most recently published as TR AM SIM IV.

TRANSIH is a modular approach to a transportation system

model which can be utilized to model transportation

networks, transfer facilities, and intermodal relationships

including a Tarine/port complex. It is capable of utilizing

either deterministic or pro baoi listic input data. Specific

elements of TRANSIH as it applies to NWS, Concori are

discussed in Chapter Six.

The input of ammunition was by two modes, truck and

rail, and is recognized when the truck or the rail car

"passes through the gate" and becomes available for handling

by NWS, Concord. The ammunition is inspected, catalogued,

and placed in storage until required for loading aboard a

vessel. Empty vessels (partially loaded vessels from other

ports were considered empty vessels throughout tne analysis)

enter the system through a harbor, and exit to the harbor

after loading. A jeneral schematic 3f the system elements

is shown in Figure 1.

13

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14

The study considered only the export of ammunition

aboard merchant marine type cargo vessels and did not

provide for the handling of any waterborne imported cargo.

This specifically excluded ship discharge operations and

operations of the rJ. S. Navy vessels commonly referred to

as AEs f which wer« found to be low productive factors in the

historical data. Since the AEs are homeported at Concord,

this restriction may not have been completely realistic.

Therefor?, toward the end of the analysis consideration was

given to the probable presence and needs of AEs, and these

vessels are discussed as a part of the system in Chapter

Eight. However, to achieve the maximum export capability of

NWS, Concori, the AEs were considered a negative influence,

as will be explained later, and were excluded in the

building of the model.

The study was restricted in one notable area. All

computer simulations were processed with the computer at

UCLA, where the most current version of TRANSIT is stored on

tape. The complexity of the program did not allow for the

program to be transferred to another computer site without a

significant IBANSIM training program involved in addition to

possible logistical support problems. Using the computer at

UCLA involved using research funis that could only provile

for approximately fifteen runs on the computer with very

limited storage and time allocations.

The general approach of the study progressed

consecutively through the following steps:

1. Becarre familiar with NWS, Concord operation and its

facilities.

2. Gathered raw (lata from the activity.

3. Drafted a rough TRANSIT model to represent the NWS,

Concord operation.

15

4. Organized the raw data and transformed it into

usable parameters.

5. Tested the THANSIS model on the computer for

accuracy, realism, and computer efficiency, and subsequently

reworked the model and parameters until the original

situation was replicated.

6. Varied the parameters to determine limits of NWS,

Concord capability.

16

III. INTENT OF THE PROJECT

The objective of the thesis was to create a simulation

model that could predict the maximum export capability of

the NWS, Concord. One way to hypothesize the ultimate

capability of an activity ./as to find the historical maximum

output the activity and let that be th2 maximum

capability. This analysis used the historical maximum

output of 387,113 long tons in the first half of 1968

(Vietnam War period) as a basis for a simulation model,

emphasizirg high productive factors and de-emphasizing low

productive factors in the activity, to predict greater

capability. The system considered for analysis is defined,

restrictions and assumptions stated, and then the analysis

proceeds. As the study progressed some of the system

boundaries and assumptions were changed or modified and are

noted

.

The Ccncord facility loads ammunition for many ports in

tne Pacific Ocean area, often on the same vessel. Of

course, stowage of the cargo aboard a vessel requires

ordering of the cargo being loaded to insure direct access

by the unloading ports. This model has not provided

specifically for such ordering of cargo, having thought that

this function more properly belongs in the planning phases

of transportation and not the operational loading phase.

The longshoremen load what is sent to them and it is this

aspect of the Conccrd export mission that is examined.

The research indicated that often many records of data

were not available. The intent of the analysis was tc ta'<e

the best available data, both official and unofficial, ani,

17

if appropriate, apply it to the model. Host of the 1968

data was kept at Concord for historical reasons only. Not

all divisions in the Concord facility require or desire such

inf or nation . Consequently some data was from 1968, while-

other data was from 1976 and 1577, and still other lata has

never been gathered.

The TFANSIM program has a capability to analyze a system

in extensive detail. In the instance of a marine port

complex, to model movements of pallets, forklifts, dunnage,

railcars and many other small but identifiable units is

conceivable, but the expense is jreat and the time involved

is in terms of months and years. This simulation of tf*T 3,

Concord was an attempt to model the most significant

elements in the determination of export tonnige moved.

Every effort was extended to keep the model as real as

possible. As the study progressed many decisions regarding

the system boundaries and data elements centered on the

issue of realism versus model scope and efficiency. Tc keep

the scope and efficiency manageaol?, assumptions and

approximations were necessary, but they rfere always

juxtaposed against the iesire for realism to insure the most

reasomblt picduct.

This paper progresses oy defining the operational

process at N*5, Concord, then defining the TRANSIT model in

its final fcrms, followed with a derivation of the specific

data. Pinally the results are discussed ind some

conclusions offered.

1 }

IV. ASSUMPTIONS

To build the model, assumptions had to be male. First

there were broad, general assumptions which held fcr the

entire model and all systems considered. Second, there were

specific assumptions which were lirectly applicable to a

portion of the system, in element of data, or decision

rule in the model. However, specific assumptions were not

necessarily void of any influence on any other system

element cr model decision rule. They could indirectly

effect another element or rule and could thereby influence

results in Tore than one way. The specific assumptions are

listed in Chapter Six while the general assumptions are as

fellows:

1. An adequate labor force was available to maintain

maximum strengta of all possible working elements in the

port simultaneously.

2. All necessary equipment and maintenance facilities

were available to provide sufficient cargo handling

capabilities for ail possible working elements in the port

simultaneously with a nominal deadline rite allowed. The

nominal deadline rate was presumed to be whatever was

acceptable by the activity during 1963 operations.

3. .Sufficient administrative and logistic support was

available to supply the needs of operational forces.

4. No container operations were allowed except for the

occasion il deck sr.owel container whicn must be loaded with

ship's qear.

5. Ihe year 1963 was representative of a balanced

system at NWS, Concord while handling a record amount of

ammunition. A balanced system was defined as one where the

19

inputs equaled outputs, that is, no cargo was created or

destroyed within the system.

20

V. THE NAVAL WEAPONS STATION, CONCORD TIDAL A32A

Before N*"S, Concord can begin the planning for and the

processing of export cargo, higher authorities outside the

facility coordinate land movements of cargo to meet vessels

in various pcrts. Prom taese authorities, notices of vessel

arrivals, cargo loads, and destinations are received as well

as information on cargo arrival by highway and rail. Upon

receipt of this information, NWS, Concord begins planning

for the positioning of the cargo in the storage areas and

for the hindling of the cargo to provide orderly and safe

stowage of the cargo aboard the vessel.

A. PLANNING CONSIDERATIONS

The positioning of the vans is essentially void of any

restrictions since the parking area is centrally located

with respect to the berths and except: for the occasional

trailer-lead of dynamite, the van requires no bunkers or

other protection. On the other hand, the positioning of

railcars considers the separation of dynamite into specific

bunkers and the storage location with reference to the

expected vessel's berth. Specific bunkers are assignei for

dynamite only, as the need arises. The inflexibility of the

routing of railcars due to the layout of the track in the

storage area demands extensive planning for the railcar

storage location. Therefore, proper positioning of the

railcars can prove more efficient when the berth is

accessible directly from its storage location instead of

indirectly through numerous switches. Although a railcar

21

can be moved to any berth from any storage location, seme of

the track is directly accessible to only twe or four of the

berths.

Vessel berth assignments are dependent upon projected

available berths and upon cargo storage location since all

the berths have essentially the same physical

characteristics. Even though planning is done, the berth

assignment, is often changed due to a change in priorities or

delays of cargo or vessels. These changes can cause

previously developed plans to be discarded and inefficient

situations to arise.

B. VAN AND RAILCAR MOVEMENT PROCEDURES

When a truck arrives with cargo it is inspected,

received (a paperwork accounting process) , and sent to a

specific numbered location in the storage area. Often the

prime highway mover transfers the trailer to a U. S. Navy

owned and operated tractor at the receiving point, so that

the trailer is parked in the storage area by NWS, Ccncord

personnel. At other times local drayage firms, familiar

with NWS, Ccncord procedures and facilities, are allowed to

park the trailer. Once parked, the trailer remains

stationary, without tractor, until called for loading aboard

a vessel. (Since most trailers are vans as opposed to

flatbeds, van and trailer will be used interchangably

throughout this paper. Tractor will refer to the prime

mover, ana truck will indicate a combination of tractor and

van or trailer.)

When a train arrives, NWS, Concord railroad personnel

are advised, and arrange for the inspection and receiving of

the rulcars. Then the yarimaster directs switching

22

operations to segregate the cars into specific numbered

bunkers in accordance with the aforementioned plans where

they remain in storage until called for a vessel.

When notification of vessel arrival is obtained,

operations personnel mobilize the labor and equipment to

accomplish the plan-ned loading of the vessel. As specific

cargo is required for loading, notification is provided

through a central locator to either the railroad or tractor

operators tc move a particular railcar or trailer from its

storage location to a specific location on a designated

berth. Once the conveyances have been unloaded, the

railroad and tractor operators are again notified of the

movement requirements. Later, notification is given to the

owner of the trailer or raiicar that it has been unloaded

and is available again for his use. Once the vessel is

loaded with its intended cargo, the vessel is secured, and

provided aith sailing instructions.

C. AREA CAPABILITIES

The following physical characteristics and operational

limitiations of the SWS, Concord facility are noted. The

Tidal Area has three quays with two oerths each for a total

of six cerths. It is possible to simultaneously have a C-5

type vessel at each berth while retaining an ability tc work

all hatches rrcm the quay side of the vessel. Each quay has

at least two parallel bracks entering and one track exiting

the berth areas expanding to four parallel tracks in the

actual vessel berthing area. For safety, no more than

twenty-five loaded railcars are allowed on a quay at any on^

time, with no more than fifteen loaded railcars allowed at a

berth at one time.

23

There are 38 bunkers with a total capacity of 244

railcars. The bunkers are in three sizes, capable of

storing either four, eight, or twelve fifty-foot railcars.

This number of variously sized bunkers allows many storage

configurations, providing compatibility for all classes of

ammunition. The distinctions in ammunition compatibility

which could effect the rail storage capacity are minimal due

to the number of bunkers and their varying size. In

addition to the bunkers, there are two classification yards

that can be used for storage of 287 railcars providing that

the safety requirements, such as the area net explosive

weight liiitat ion and ammunition compatibility require meats,

are met. Therefore the actual railcar capacity is

approximately 531.

The van storage areas consist of two paved and marked

parking lets with aa approximate total capacity of 200 vans.

The ability tc store a surge of vans, possibly 150 more, for

a short period of time is possible when consideration is

given to using unpaved and unmarked open areas and even

roadway shoulders. The only limitations for continued'

efficient operations are localized congestion in a storage

area ana the distance of the storage area from the berths.

Additionally, van cargo is sometimes transferred to U. 5.

Navy owned railcars on station. This provides an added

flexibility if the van storage becomes congested. This

analysis considered the van storage capacity at 200

trailers.

In the instances just mentioned, the implication is uot

that HAS, Concord can store an unlimited number of railcars

or trailers. However, it must be emphasized that the stated

restrictions on the storage capabilities are not the strict

limitations for the entire facility. In fact, in addition

to the areas mentioned ibove, there are additional storage

areas in the Inland Area. These other storage areas have

24

11

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25

been used in the past, specifically in 1968. It is

important to realize that once the normal railcar and

trailer storage capacities, 531 and 200, respectively, have

been achieved, in all probability the efficient movement of

conveyances will decrease. This paper did not analyze how

that efficiency varies but assumed maximum efficency can be

achieved at the normal capacities stated.

The station also has a barge pier and a large number of

barges tor use in the port area. The barges can be loaded

by mobile pier-based cranes at a separate pier that is

accessible to botn vans and railcars. This allows carjo to

be transferred to a vessel at anchor in the stream or to a

berthed vessel from the water side, or simply placed in

temporary storage until required for leading aboard a

vessel.

D. OPERATING TI22S

N'ormal operating hours for the longshoremen labor force

at N*5, Concord are 0000 to 1700 hours with a one-hour break

for lunch. Additionally, fifteen-minute smoke breaKs are

prcvidad after two hours of each four- hour work period.

During periods of two shifts, the second shift is from 2000

to 0500 hours with the same breaks as the first shift.

Other employees that interface with or support the

longshoremen may start slightly earlier or later. Curing

periods of two shifts, all elements are staffed except for

the receiving and inspecting functions which work only

eight-hour days.

At the berths, because of safety requirements, there are

limitations on when switches of railcars and vans can be

made and the size of the switches. Standard operating

26

procedures at NWS, Ccncord provide for switches between the

hours of 1200 to 1245, 1700 to 1945, 0000 to 0045, and 0500

to 0745 when no cargo operations on the vessels occur.

Switches can also be male daring the fifteen-rain ate smoke

breaks. Also, safety restrictions limit the number of cars

in a single switch anywhere in the Tidal Area to twenty-two

railcars

.

Additionally, it is mandatory that all vans on the berth

be removed ficm the track area at the berth prior to any

movement of railcars, and only after the railcar movement is

complete at. the berth may vans be placed back in position at

the berth for unloading. This procedure must be followed

because for unloading, the vans are placed across the

railroad tracks and thereby restrict railcar movements.

Nominal time for the movement of trains is fort /-five

minutes in the berth area. This allows only for the

positioning of the cars at th'3 oerth and does not include

time for retrieving the desired cars from their various

storage locations. The retrieval time will depend on the

nutcber of cars involved, their relative location, and their

grouping. The nominal time for a van movement is twenty to

thirty minutes one-way. This provides time for locating the

van, connecting it to the tractor, raising the dolly,

positioning the van, lowering tae dolly, and disconnecting

the van.

27

VI. THE T3ANSIM VI£tf OF NSS, CCMCOH')

This chapter portrays the system just described frcm the

view of the simulator. The modifications of the system and

the specific assumptions are specified.

A. SYSTEM ECDNDARIES

Originally the boundaries coincided with the

geographical perimeter of the NWS, Concord Tidal Area. This

allowed thre- openings in the perimeter for the entry and

exit of cargo. Cargo entered the system by rail and truck

and left aboard vessels. The empty vessels originated at

the system boundary at the juays and after loading at the

berths departed the system. However, provision had to be

made for anchorage of vessels awaiting an empty berth.

Therefore, the system boundary was extended to include a

harbor or sufficient size in which to anchor all the vessels

that might require waiting.

Also it was intended that the trains and trucks would

enter at the boundary gate, the physical device the

conveyances actually passed through to enter the Tidal Area.

Due to the lack of data and in consideration of th Q

simulation objective, the function was deemed irrelevant.

The primary elements in the determination of export

capability were the number of vessels, the tonnage loaded,

and the capability of the storage areas to accumulate

sufficient cargo to keep the vessels supplied with the

required cargo while not exceeding the normal storage

23

capacity. As a result the system boundaries were drawn in

to the truck and railcar storage areas.

After the railcars and trucks were unloaded at the

berths, they left the system. It was assumed that there

existed enough storage space for empty conveyances both

within the perimeter of the Tidal Area and on adjacent roads

and tracks and/or that the local carriers would pickup th^ir

empty trailers and railcars so that the storage of empties

woull not impact on the tonnage exported.

BASIC ELEMENTS 0? I!HE MODEL

The rRANSIM program accounts for the location and

activity of traffic units. Traffic units are the smallest

identifiable elements in the system. In this model, the

traffic units originally were long tons, vans, railcars, and

vessels. The greatest problem in the exercise was the

organization of the raw data into a form that woull pnoviae

efficient use of the TRANSIT model within the time and

funding constraints dictated. The first computer run

providing detail in terms of tonnage (long tons) used an

inordinate amount of computer capability, and required

reconsideration of the validity of the four traffic units.

Dropping long tons from the primary elements was the

first modification of the original plan and resulted in

subsequent data approximations and assumptions. The

smallest traffic units became the conveyances themselves,

specifically the trucks, the vessels, and the railcars. It

seemed reasonable to assume an average cargo load per

conveyance from which gross tonnages could easily be

calculated when needed.

29

^VESSELSg -A

§ GO

UHHss—uin

Q

m

EH«Q

Maen

H

VESSELS OJQa:crX

SHIPS[

CDn

y\ 5en

30

Even though each van and each railcar had the same

average tonnage as any other van and railcar, respectively,

it was essential for the model to identify each van and

railcar to determine times each element spent at a

particular activity. Throughout the model, vans were

identified individually. However, for trains a further

consolidation was deemed necessary after the early test runs

continued tc show an inordinate demand on computer time and

memory uhile the model was maintaining individual

accountability of raiicars. Therefore, trains were composed

of groups of ten raiicars.

This one consolidation caused the most concern in the

entire model development. It meant the complete sacrifice

of any attempt to model the switching restrictions (2 2 cars

per switch) or berth railcar capacities (25 raiicars per two

vessels <<ith no more that 15 raiicars per vessel) and/or the

allowable switching times. To achieve both the most

efficient computer use and desired realism was not possible.

Efficient computer use required grouping the railcar units.

Realism required that nilcars oe individually identified to

facilitate the movement of raiicars in both the accounts

specified and at only the allowed switching times. Realism

had to yield to efficient (in terms of cost) computer use in

the model.

C. ALTERNATIVE APPROACHES

The objective of the project was achievable in two ways.

The model could be structured to generate cargo from the

storage areas at a prescribed rate and load the vessels to a

predetermined capacity at which time they would sail. Then

a ccmoarison of simulated berth times with actual oerth

31

times would render possible conclusions. Alternatively, the

vessels cculd be called in berth for a prescribed time to

load a predetermined amount of cargo. Then comparisons

could be made between the simulated accumulation of cargo in

the storage area with the physical ability of the storage

areas to efficiently handle the cargo. The latter approach

was chosen for the following reasons:

1. Berth time data was available while cargo arrival,

storage, and handling times could only be guessed, because

historical data was not available and current data was

insufficient

.

2. The second alternative seems more realistic in that

plans are usually made first for the vessel's cargo while

the handling and storage capabilities of the port are

secondary conditions, if considered at all.

Using total berth time as a driving force allowed the

model tc be less ietailed than the switching time

restrictions, switching size limitations, and the berth

railcar capacity limitation required. The ten-railcar

traffic unit appeared to b3 a plausible approximation. Two

of the consolidated units were 91 percent of maximum .switch

size. If only one vessel was at a juay, a consolidated unit

was 67 percent of the berth railcir capacity, and if two

vessels were in adjacent berths, a more likely situation

when trying to maximize tonnage output, two units were

eighty percent of capacity. If one consolidated unit of ten

raiicars was associated with a vessel, none of the safety

restrictions would be violated and, assuming that a

requirement for all switches and berth railcar storage

capacities to be maximized all the time is not realistic,

the number cf raiicars available for unloading on the berths

was tolerable.

32

Regarding the only other variable, switching time

restrictions, the allowance for the switching time had to be

realized in a reduction of the unloading rate. Inplicit in

this arrangement was the assumption that the units of: ten

railcars was neither too Large nor too small to nave a

significant effect on the tonnage loaded, and that in the

long run the cargo loaded during switching times would

balance the lack of cargo during cargo loading times.

D. DATA ELEMENTS

Raw ^ta was ootainable either from records or

interviews fcr most elements within the Tidal Area. The

data base consisted of either capacity elements or time

elements. Capacity elements included vessel loading space,

van and train storage capacities, and tonnages on vans,

railcars, and vessels. Time elements were vessel leading

and vessel total berth times, interar rival times of trucks,

trains and vessels, inspection and receiving times of vans

and railcars, movement times of vans and railcars both from

receiving tc storage and from storage to berths for

unloading, and unloading times of vans and railcars.

1 . Ca£acity_ Elements

Storage and berthing capabilities were taken from

existing facilities in April 1977. These were the same as

in 1963, except foe the first forty-five days of 1968, whan

two of the berths were still under construction. (All 1968

data in the rrodel has been prorated to provide an equivalent

full year's capability.) In the past, vessels at NSJS,

Concorl have occasionally loaded cargo, usually in very

small tonnages, while at anchor 'in the stream and have

33

loaded vessels from the harbor side of a vessel while at a

berth. The stream operation was not considered in the mod^l

but the barge operation on a berthed vessel, while not

specifically modeled is allowed in the way the model

evolved. Therefore six vessels, one per berth, were

considered loadable at any one time.

The mcdel assumed a nominal storage capacity of 530

railcars and that safe storage could be provided all types

of ammunition. For the model the van storage capacity was

200. Storage of :argo on barges was not included in the

model

.

In 1968 NWS, Concord exported record number of

long tons of ammunition and this was thought to be the most

appropriate year in which to base the model to determine the

port's ultimate capability. The official manifest tonnages

of 196d vessels were available and were used in the model.

The data indicated a broad spectrum of tonnages loaded

aboard vessels, from eight tons to over ten thousand tons.

In order tc categorize vessels, they iiere divided into ten

classes, each class with a range of one tnousand tons. This

allowed for a distrioution of tonnage by class and also a

distribution of vessel frequency by class.' For each class

an average tonnag? was calculated and used as the tcnnage

for that class of vessel.

Truck and rail tonnages were not available for 1963.

This data was taken from 1976 records and averaged to obtain

a nominal tcnnage per conveyance. The implicit assumption

is that the tonnage per conveyance would not change

significantly over time.

2. Time Elements

34

Much of the time data was not recorded; in fact,

only the total berth times of the vessels were recordel.

However, from the berth time data, vessel interarrival times

were calculated. Although nor recorded, the working time of

a vessel was the only other time data that had a possibility

of validation. Records did not indicate the precise working

and non-wcrking times on a vessel. Since only a guess,

based on normal working hours, could be made on vessel

loading tines, all vessel tonnage capacities and loading

rates were based on total berth time. To determine the port

capability two shifts were assumed.

Because of the adjustments of the system boundaries

to the storage araa, it was not necessary to represent

inspection times, receiving times, or movement times of vans

and railcars into their respective storage areas. The

critical issue was to show an arrival of vans and railcars

into a storage area where they could be available for

unloading to a vessel. But no records of truck or train

interarrival times were available. To model cargo arrivals,

NtfS, Concord employees w-^re interviewed to obtain their

'•best guess." For vans, the arrival process occured over an

eight-hour span in the day shift, while the railcars were

usually moved in a group at one time during the day or in

groups of railcars just a few times a day.

The simulation allowed trains to arrive only at 0800

hours, precluding multiple time arrivals during the lay.

Since the usual practice in ports is to have cargo on hand

prior to a vessel's arrival, this assumption was not

unrealistic. However, to simulate the arrival of iifferent

numbers of railcars during a day the number of train

arrivals was said to obey a distribution of one train

arrival 50 percent of the lays and either no train arrivals

or two train arrivals, each 25 percent of the days.

Depending on expected vessel berth occupancies, the number

35

of trains, with ten cars per train, could be

prooabilistically varied to satisfy the vessel cargo

requirements. The overriding influence was to balance

inputs with outputs and ensure sufficient railcars in

storage to allow the system to operate in apparent realism.

Truck arrivals were simulated differently. It

seemed appropriate to represent truck arrivals by a Pcisson

distribution. Except for a possible buildup early in the

receiving period, the Poisson distribution would represent a

random arrival pattern. However, the stipulation was

included that trucks could be received only between 0800 and

1600 hours tc simulate the real-life situation of trucks

being received for only one eight hour shift. The Poisson

distribution worked on a twenty-four hour basis but specific

rules were included in the program to cause trucks that

arrived between 1600 and 0300 hours to leave the system with

no ether action. The mean interarrival time was dictated by

balancing the output of trucks with the input of trucks for

the system. The mean interarrival time was calculated by

dividing the allowed eight hour receiving period by the

average number of trucks to be input daily into the system.

Again, the effort was directed to ensuring sufficient

arrivals in the period to match the demands of cargo by the

vessels.

As a check of this hypothesized model, the timing of

cargo input, was considered a function of tonnage, as already

inferred. Tc provide for cargo input, cargo coming into the

system was assumed to equal the cargo coming out of the

system ov^r a long period of time. Specifically the model

was structured to have the cargo entering by trucks and

trains equal the cargo loaded on vessels in i six month

period. with tee knowledge of the tonnage exported and the

tonnage per truck and train, only the arrival rates had to

be determined for the trucks and trains. However, the ratio

36

of vans to railcars was not available. A "best guess" by

some of the experienced employees at NWS, Concord was the

best information available. A ratio of three railcars to

every truck was determined to best represent the actual

situation.

The cnly unresolved times were van and railc^r

movement times from storage to berth and the railcar and van

unloading times. Again no specific records were available

and the basis of the model was conversation with experienced

NWS, Concord employees. The best solution was an attempt to

represent the vessel berth time in terms of cargo leaded.

Assuming the ratio of railcars to trucks remained the same

as the receiving rate (three to one) it was possible to

calculate a vessel loading rate.

The model did not count the movement time from

storage to berth. In effect, this time is mad.^ a part of

the loaling time of the vessel. Without including the

switching limitations described earlier, the movement time

has no meaning in any case.

SYST"iv SUGARY

In suimary the model is set up with three independent

variables: vessels, vans, and railcars, and several

dependent variables, which cannot be statistically verified.

Therefore several pilot runs of the program were made,

relying on the author's judgement to recognize an improbable

result and correct it. Apparent errors in the functional

data relationships were found, and production runs were not

made until the model was able to replicate real situations

in the past. The actual operating rules of the model are

defined in Appendix A.

37

The specific assumptions stated above are summarized as

follows:

1. Lccal carriers PICK empty conveyances or

sufficient van and railcar storage space exists such that

empty vans and railcars do not impact on export tonnage.

2. Movement times of conveyances from storage to berth,

and switching limitations can be incorporated into an

average vessel loading time for all classes of vessels.

3. Vans and railcars consolidating pallets of cargo can

properly represent tonnage loaded aboard the vessel.

4. Railcars in groups of ten can be moved as a unit

throughout the system.

5. The historical relationship between vessel tcnnage

and berth tirce is valid for any time period.

6. The cargo mix of virious explosive categories can be

safely stored in the normal areas.

7. The randcrcness of the simulation's Y.onte Carlo

technique provides mean values over a six month period.

8. Hypothesized arrival distributions and interarrival

times are valid approximations of the real situation.

9. The average ratio of railcars to trucks in and out

of the system is three to one.

10. Loading operations with vessels in the stream are

not allowed.

11. The average van and railcar tonnages are constant

over time.

38

VII. THE DERIVATION OF THE PARAMETERS

Numerous assumptions have oeen made in ar. effort to

transform the real situation into a form acceptable for both

the TRANSIM program and efficient computer utilization. It

is only appropriate to be more specific in the methods used

to organize the raw data into parameters. Throughout all

the simulation runs the distribution of vessel classes, the

vessel class berth times, the number of vans and railcars

per vessel class, the unloading times of vans and railcars,

and the interarrivil time of trains were the parameters held

constant. These constant parameters ar? now described,

while the parameters that were changed to create different

effects in the simulation are described later.

A. ?AHA;1ETE3S HELD CONSTANT

1 • Distribution of Vessel C lasses

rcr each vessel that arrived and departed (aade a

turnaround) frcm January 1968 through June 1963, the raw

data furnished the name of the vessel and its manifested

tonnage in lcng tons. This iata was ordered and plotted in

histogram fashion (Appendix J) with ten equal intervals of

tonnage (the intervals were all equal to 1000 tons except

the interval at the highest range of tonnage which was

allowed to include all greater tonnages) plotted against the

number of vessels. The number of tonnage intervals and the

interval lenatn itself were arbitrarily designated. rhe

39

lowest tcnnage interval (0 to 1000 long tons) was assigned

class 1, and the classes progressed consecutively higher to

class 10 (9000 long tons and above). By dividing the number

of ships in each class by the total number of ships (76) , a

distribution of vessels by tonnage class was achieved

(Figure 4) . In the TRANSIM program this distribution was

called the "shiprule."

One qualification of the raw data stated above must

be clarified. Recall that the only data used was for ships

that completed a turnaround within the six month period.

This eliminated any vessels on berth at the beginning of the

period and also those that failed to complete operations by

the end of the period. Remember also that ASs are not

included and that vessels involving any discharge operation

are not included.

VESSEL CLASS PERCENT OF VESSELS

01

02

03

04

05

06

07

03

09

10

6.6

2.6

2.6

7.9

13.

a

30.3

21.1

7.9

1. 3

1.3

Figure 4 - VESSEL CLASS DISTRIBUTION

HO

2 . Vessel Class Berth Times

The same raw data above also contained the vessel's

arrival and departure times from its berth. From that data

total berth times, to the nearest quarter hour, were

calculated for each ship and plotted against the vessel

class in which that ship belonged. Within the vessel

classes, there appeared no correlation between vessel berth

tiers and tonnage loaded. However, there was a trend of

increasing berth time with corresponding increase of vessel

class, which was expected.

The berth times in each vessel class were simply

averaged to crtair a mean. To give the same randomness to

the simulation that the raw data for each class did reflect,

a ten percent spread on either side of the mean was

arbitrarily assigned to a triangular distribution (Appendix

K) . (The triangular distriDution is a device incorporated

in Tr?AMSIM that assigns probabilities). The variance was

strictly arbitrary and was adjusted for the large class

vessels, after test runs indicated invalid results in which

the higher class vessels were not able to achieve their

prescribed tonnage in the allotted time. Therefore, vessel

classes eight, nine, and ten had increased variances to

rectify this problem (see page 43 for discussion of leading

times). Figure 5 gives the distribution of berth times for

the various vessel classes.

41

VESSEL CLASS TIME ON 3ESTH

LEAST AVERAGE MOST

01

02

03

04

05

06

07

08

09

10

103 114.3 125

137 152.6 167

165 183.4 20 1

170 189.2 203

134 204. 1 224

186 2 6.1 226

190 211.4 232

192 213.9 240

198 220.0 23

221 245.5 340

Figure 5 - 3E?.TH TIMES 3Y VESSEL CLA35

42

3- Vans and Rail cars per Vessel Class

It was desirable to make the tonnage for each class

of vessel an even multiple of trie railcar tonnage to avoid

the necessity of accounting for partial loads in vans and

railcars, which would be another burden upon computer

storage and a complicating element in the model. Therefore

the vessel tonnages, although based on the average tonnage,

were approximations.

To determine the tonnage unit required, the average

tcnnages per van and railcar were found. The only

reasonable record cf van and railcar tonnages was from 1976.

It is logical to expect the average tonnage of vans and

railcars to be relatively constant over time. The one

possible cause for variance might be the nature of

ammunition required due to a particular type of conflict

(napalm bombs are much lighter than explosive bombs)

.

Var. and railcar tonnages were based on data

generated in the first six months of 1976. The average long

tens per van and the average long tons per railcar were

calculated to be 15.9 and 53.2 respectively. After many

trials at what might have been even multiple combinations

that seemed reasonable, 18 long tons was chosen as the

average weight of a van with 54 long tons the average weight

of a railcar. Consequently each average vessel tonnage was

rounded to the nearest multiple of 54 long tons.

Although these tonnages were included in the

computer incut data, they were not actually used in the

simulation due to the elimination of the long ton traffic

unit. Figure 6 snows the actual average long tons per

vessel class and the average used in the simulation.

43

VESSEL CLASS

01

02

03

04

05

06

07

03

09

10

LONG TONS

SIMULATED ACTUAL

540 540

1674 1670

2160 2154

3726 3733

4536 4551

5400 5409

6430 6462

7398 7422

3154 3163

10152 10132

Figure 6 - ACTUAL VS. SIMULATED TONS PEP VESSEL CLASS

44

The number of railcars and vans per vessel class was

an easy calculation once the ratio of railcars to vans was

established. Applying the 3:1 ratio assumed above, the

solution of two simultaneous equations provided the number

of vans and railcars. The equations were:

1.) CARGO TONNAGE ABOARD VESSEL =

(NUMBER 0? VANS) (18 LONG TONS PER VAN) +

(NUMBER OF RAILCARS) (54 LONG TONS PER RAILCAR)

2.) NUMBER OF RAILCARS = 3 (NUMBER OF VANS)

The general feeling of personnel at NWS, Concord was

that the tonnage received in railcars in relation to the

same for trucks had decreased in the past few years.

Without data this was impossible to verify and it is only

hoped that the data, if available, would prove relatively

consistent with what has been hypothesized. When it became

apparent that the grouping of railcars into units of ten was

a more efficient .vay to model the system, the result was a

necessary rounding of the number of railcars, or trains, per

vessel tc the nearest multiple of ten and consequently an

effect en the van/railcar ratio to achieve the desired

tonnage per vessel class. The results of this consideration

is summarized in figure 7.

45

VESSEL CLASS VANS BAIL-CARS

ACTUAL THEORETICAL ACTUAL THEORETICAL

01 3 3 10 9

02 3 9 30 2 3

03 9 40 37

04 27 13 60 6 3

05 12 27 30 75

06 30 ^Q 90 90

07 30 36 1 10 108

C9 51 42 120 123

09 33 4 5 140 136

10 54 57 170 169

Figure 7 - ACTUAL AND THEORETICAL NUMBERS OF VANS AN'D

RAILCARS PER VESSEL CLASS

46

Consideration was given to making the distribution

of railcars and vans per vessel random, but was not

implemented for three reasons. First there wo nil be no

assurance that the length of time or number of vessels

generated in the simulation would be sufficient to cause the

average tonnage in a vessel class to achieve its desired

level. Secondly, the vessel berth time was an independent

and overriding factor on tonnage loaded, that is, even

though the simulation ma/ have generated a higher than

average number of railcars and vans for a vessel, there was

no way they could be loaded on the designated vessel once

its berth tine nad elapsed. Finally, the distribution would

have been a guess, and that would only compound the

uncertainty already incorporated in this part of the model.

This determination of traffic units per vessel was

necessarily uncertain and, regrettably, the detail which was

initially desired sacrificed.

4 • Unloading Ti mj?s of Vans and Ra ilea r

s

As previously discussed, the actual time of loading

the cargo from vans and railcars onto the vessel was not

documented in records at NHS , Concord. In any instance,

this would have been difficult to correlate wita the total

berth tiie cf the vessel which also included significant

amounts of non-productive time. Therefore by trial and

error, considering the number of traffic units necessarv to

achieve a desired vessel tonnage class, the ratio of trucks

and vans unloaded, and the oerth time of the vessel,

unloading rates were obtained. The nominal times used were

ninety minutes for a van and two hours for a railcar. To an

experienced port operator these times may seem high. But

recall that this rate of unloading considers not only the

sixteen hours of available productive time in a twenty-four

47

hour period hut also, shift changes and breaks, meal times,

vessel rigging times, cargo compartment opening and closing

times, blccking and bracing times, vessel time between

docking and commencement of operations and time between

ceasing c: operations and sailing time, cargo delays,

eguipment breakdowns, and short hatches. The figures seem

more plausible when the average tons loaded on a vessel per

hour of berth time for the January 1968 through June 1968

period was 25.3 long tons. In the pilot runs of the

simulation these times were analyzed very closely to insure

that the cargo was loaded within reasonable times of the

vessel departure. The tines were adjusted somewhat, along

with vessel berth times (see page 41) to insure the

simulation was as realistic as possible.

Dependent upon the type of cargo in the vans or

railcars the unloading rate could vary. Based on interviews

with operating personnel at NWS, Concord a spread on either

side of the mean loading times of 45 percent for vans and 30

percent for railcars was assigned. In the pilot runs this

randomness vas monitored and provided reasonable realism to

the simulation.

5 • Interarri val T im e for Trains

The basic idea for rail arrivals was to have an

average cf one train a day arrive and to vary the number of

railcars in the train to make the number of railcars

variable. However, it was a simple task to also vary the

number af trains per day and achieve more realism.

Therefore, still basing the train input on a mean

interarrival time of one per day, the number of trains was

allowed to vary from through 2 per day on a stepwise

uniform listributicn.

48

B. PARA3ETZES TO BE VARIED IN SIMULATIONS

The variable elements cf data include the interarrival

time of vessels, the interarrival times of trucks, and the

number of railcars per train.

1 • Interarrival Time of Vessels

The average interval between ship arrivals was

calculated fcom the nunuoer of vessels in the six months

compared with the total time in hours. The original result,

based on seventy-six vessels, was a mean interarrival time

of fifty-seven hours. The distribution of arrivals was

specified as Poisson. The Poisson distribution was used

throughout the simulation runs but the mean interarrival

time was changed depending on the number of vessels desired

for berthing in each run.

With the available barth time for six berths over

18C days egual to 25,920 hours, and the historical data base

giving an av=rag° of 197.5 hours berth time per vessel, the

eguations used to calculate the mean interarrival time were:

1.) NUKBEE 0? VESSELS =

TOTAL BERTH OCCUPANCY TIME

AVERAGE BERTH TIME PER VESSEL

(OCCUPANCY RATE) (AVAILABLE BERTH TIMS)

AVERAGE BERTH TIME PER VESSEL

(OCCUPANCY RATS) (25920 HOURS)

19 7.5 HOURS PER VESSEL

49

2.) interarrival time =

total time

number of vessels

2« Interarri val Times for Trucks

The distribution for truck arrivals was also assumed

to be Poisson. For the original runs the mean interarrival

time was forty-five minutes. with this distribution the

number o: trucks entering, leaving, and stored in the

storage area seemed reasonable in the test runs. However,

when greater tonnage requirements existed due to increased

berth occupancy it was noted that the relationship between

truck arrivals anl vessel classes on berth was extrenely

critical. When vessels that required great numbers of vans

were in a berth, there were insufficient numbers of vans in

storage to satisfy the demand. Therefore, in later runs,

the mean arrival rate was arbitrarily increased and

decreased depending on the supply of trucks, resulting in a

non-hoaogen ecus Pcisson distribution. This gave added

realism to the model because the nature of truck

transportation anl truck detention rates are such that

trucks are used when the interface time with another mode is

imminent cr short. To calculate the mean time when the

vessel leading requirements were varied, the following

equations, with the historical data base giving an average

of 25.197 vans per vessel (assuming the 3:1 railcanvan

ratio, adjusted only fcr rounding due to the grouping of

railcars) , were used:

1.) SOMBER OF VANS PER DAY =

(25.197 VANS PER VESSEL) (NUMBER OF VESSELS)

NUMBER 0? DAYS PER PERIOD

50

2.) VAN INTERARRIVAL TIMS =

(3 RECEIVING HOURS PER DAY)

NDMBEH OF VANS PER DAY

3. ^iinber of Railcars £er Tra in

The number of railcars per train was a direct result

of balancing inputs with outputs. The average number of

trains (ten railcars per train) per vessel was 8. 5^3, from

the historical data base (assuming again the 3:1 railcar:van

ratio, adjusted only for rounding due to the grouping of

railcars). The following eguation provided the number of

trains required:

8UMBES 0? TRAINS =

(3.593 TRAINS PER VESS EL) ( NUMBER OF VESSELS)

NUHBEB OF DAYS PER PERIOD

If the result contained a fraction, for example 5.4, then

cars per train was given a distribution of 60 percent of the

trains with five groups of ten railcars and 40 percent of

the trains with six groups of ten railcars which provided a

mean of 5.4 groups of ten railcars per train.

51

VIII. RESULTS

Five simulation runs of 130 day periods were made. The

first taree runs, I r II, and III, were made in orler to

replicate the original situation. Simulation runs IV and V

were made with the parameters varied to attempt to find the

upper limit cf NWS, Concord's capability. The results of

the study are included as Appendices E through I and are

summarized in Figures 8 and 9.

Simulation runs I, II, and III were the runs that

attempted to validate the model's accurancy, comparing

percent of berth occupancy, total export tonnage, and number

of ships cf the historical lata with the simulation results.

The historical data indicated a total of 76 ships exported

approximately 337,000 long tons with a 62.5 percent berth

occupancy for the first six months of 1963. Although there

was a small variance in the three runs' results they

bracketed the original historical data very satisfactorily.

Simulation run II was almost identical with the historical

data

.

The remaining data generated in the simulation could not

be validated historically but could be compared with actual

facilities in an attempt to judge the result's plausibility.

Particular attention was given the vans and raiicars in

storage. Simulation run I generated a seemingly high number

of vans and raiicars in storage. To a degree, this was to

be expected in comparison with simulation runs II and III in

that the cargo was arriving at the same rate but fewer ships

were leaded.

52

However, further detailed analysis indicated an

adjustment in the van arrival rate might be necessary

because the number of vans was steadily increasing.

Therefore, simulation runs II through V had variable van

arrival rates applied to the model to reflect fewer arrivals

when vans in storage approached capacity and more arrivals

when vans in storage were low. Henceforth, on.the average,

the storage reguir ements were consistently within the van

storage capacity with the normal capacity being exceeded

only in cr.e run by 26 vans which was felt to be tolerable.

Railcars in storage did not indicate any specific

necessary adjustments, but proved somewhat inconsistent.

The inconsistency was noticeable in the trends of berth

occupancy, tcnnage exported, and the number of vessels, none

of which had any correlation with the numbers of railcars in

storage. For simulation runs I and V there are significant

differences from runs II, III, and IV in the average number

of railcars in storage.

There was another trenl that had to be considered in the

seeking of a maximum export tonnage. The percentage of

ships turned around without any delays waiting for berth

generally decreased as the berth occupancy and tonnage

increased. This appeared to be logical in that unless

vessels were following an extremely precise schedule, there

would undoubtedly be delay times somewhere in their voyage.

Every tine a vessel is delayed, that vessel is not producing

revenue or moving any cargo for the charter price cf the

vessel. An idle vessel can be extremely costly. The

results indicated that high berth occupancy, although having

produced large tonnages, also produced more vessel delays.

The results indicated that the greatest export tonnage

in a 180 iay period is about 610,000 long tons. This

reguired 127 vessels of the assumed distribution with 99.

R

53

percent b^rth occupancy. However, this operation could be

extremely costly due to tae number of vessels in a queue

awaitinq a berth. Such a high berth occupancy rate is not a

very probable operation because too small an allowance is

provided for vessel switching times and other delays that

are bound to occur in the real situation. It was notable

that such a high oerth occupancy put demands on the storage

facilities that were felt to be tolerable.

A more reasonable export tonnage mark to be sought is

about 500,000 long tons which required Q 7 vessels of the

assumed distribution with 77.5 percent berth occupancy.

The only unfavorable factor appears to be the ship delays.

The cost of ship delays may be significant, possibly

somewhere between the cost of the historical operation in

1968 and the hypothetical 610,000 export tons operation.

However, a 77.5 percent berth occupancy allows fcr the

possible discharge of vessels, the handling of A2s, and/or

the vacant time that would probably occur.

An element of concern could be expected when analyzing

storage requiremants for such a tonnage exported. Again,

th° data was not conclusive, out consideration had to be

given requirements posed by this simulation that showed the

normal raiicar storage capacity would be seriously

deficient, requiring 1.8 times the nominal capacity on the

average and with a peak of three times the nominal capacity.

54

STATISTIC

II

SIMULATION RUN

III IV

Berth Occupancy 52.7 60.3 69.3 99.8 77.5

Van Exports (No.) 1650 1929 2253 3132 2496

Van Exports (Tons) 29700 34722 40554 56376 44928

Rail Exports (No.) 547 597 659 1026 840

Rail Experts (Tons) 325080 322380 356860 554040 453600

Total Exports 354780 357102 397414 610416 498528

Number of Ships 69 77 90 127 97

% Ships W/0 Delay 87.9 64.9 66.3 0.0 44.3

Max. Vans Stored 345 226 185 150 196

Ave. Vans Stored 177 98 55 55 94

Max. Cars Stored 1420 500 65 520 1530

Ave. Cars Stored 722 114 136 105 900

Ave. Time Van Stored 36 6.0 204. 5 96 .0 72. 2 153.9

Ave. Time Car Stored 454. 3 78.5 75.0 42. 5 404. 9

Figure 8 - COMPOSITE RESULTS

55

VESSEL CLASS SIMULATION ?UM

II III rv

01

02

03

0'4

05

06

07

08

09

10

TOTAL

8 8 6 8 5

1 4 4 1

2 1 5 4

7 4 3 5 12

10 14 18 25 16

23 22 25 35 22

14 22 20 30 27

4 5 8 12 9

1 3 1

1

69 77 90 127 97

Figurs 9 - ACTUAL NUMBERS OF VESSELS IN EACH CLASS FOP

EACH SIMULATION

56

IX. CONCLUSIONS

This thesis has attempted to both define a simulationmodel for an ammunition water port, specifically NWS,

Concord, and to utilize that model to determine a potential

capability cf the port. The analysis has been limited in

scope but is useful in modeling a port in general terns.

Two conclusions can be drawn from the analysis. First, NWS,

Concorl can export a significant increase in tonnage, up to

approximately 610,000 long tons, and second, the impact on

th€ storage areas is net conclusive.

To increase the actual export tonnage loaded aboard

vessels is a distinct possibility but it has some very

definite ccsts. Such an operation excludes any discharge

operations and precludes the servicing of any AEs. In 1968,

AE's occupied a berth at NWS, Concord 7.5 percent of the

time, while vessels involved in discharge were present 11

percent of the time. If it is possible to perform the

responsibilities incurred with these vessels at another

port, the increased tonnage provided by merchant rcarine

cargo type vessels instead, could be significant. However,

to insure sufficient vessels are on hand to receive the

increased tonnage, would necessitate a great increase in the

vessel lelay times awaiting berth. \ cost-benefit analysis

would be appropriate to determine the break even point

between the increased capability and the increased cost from

non-productive vessels. An indication of the trend of delay

times and tennage capabilities can be obtained by comparing

simulation runs II, IV, and V.

The increased tonnage just mentioned is a realization of

57

the capability provided by the number of vessels, berth

space, and vessel leading rites. However, to conclule

anything about the ability of the storage areas to handle

such amounts of tonnage is not so easy. It is difficult to

determine with the results nerein what might happen in the

storage areas. There are no .definitive trends that would

indicate whether the storage areas are sufficient under the

conditiors analyzed.

The extreme variances in the results do indicate that

the vessel class :iix, the sequence of vessel class arrivals,

and even the railcar/van ratio loaded aboard a vessel are

extremely iipcrtant. That the model could not be detailed

in this area may oe the reason for the indecisive results.

The assumptions made regarding the distribution of vans and

railcars aboard a vessel have a direct effect on the

drawdown or buildup of conveyances in the storage area.

Any conclusions about the storage area need to havs more

runs made and possibly a better data base regarding cargo

types loaded aboard a vessel and the frequency of such cargo

types.

It is worthwhile to have the model developed as it does

at least provide a vehicle to analyze an ammunition port

with a rather standard operation. The model can be used for

comparative analysis between ports and, with more

replications, may provide more conclusive results. TRANSIM

is a tool that has proven relatively easy to use, at least

in this rather general view, and is worthy of more use in

this ar^a.

5d

X. RECOMMENDATIONS

Further study of this port could prove both interesting

and worthwhile. Future analysis of the sane system might

explore a number cf areas.

To increase the numoer oi runs, if funds were available,

for a broader statistical base would be considerable

value. Different s^eds to the random number generator could

be used and the variable parameters could be changed to

generate possibly different and/or more conclusive results.

Although it would require a greater expense, the system

could be redefined to include some of the more detailed

operations or those that had to be excluded. This might

provide more realism in that some of the assumptions could

be delete!. Such detail, however, would require a broader

raw data base on which to base the new operations.

Obviously one area that can be studied is the

relationship of tne storage requirements with cargo input

and vessel leading. It is suggested that this might be a

reasonable study, with limited funds, that involves

primarily an analysis of the constant parameters. A number

of runs changing these data elements might prove most

illuminating .

59

APPENDIX A

OPERATING RULES 0? THE MODEL

The operating rules of the model are defined in the

order cargo night flow through the system. Some duplication

is necessary to provile for the movement of cargo by th? two

modes of van and railcar. The index to tne left of the

discussion of each 9lement or rule refers to the sample

program in Appendix 3.

1. Trains (ten railcars each) are generated randomly

by a given interarrival time distribution. .

Example: 25 ? 50 P 1 DAY 25 P 2 DAY

Interpretation: 25 percent of the days no trains

will arrive; 50 percent of the days one train

will arrive; 25 percent of the days two

trains will arrive.

2. The multiples of ten railcars per train ire

generated randomly by a given distribution.

Example: SRR = 50 ? 3 50 P a

Interpretation: 50 percent of the train arrivals

have 30 railcars and 50 percent of the train

arrivals have 40 railcars.

3. At the storage area a train substitutes into a

number of railcars.

Example: rSAIN 5 RULE NRR RAILCAR

RAILCAR S CARGORRA

CARGORRA R RRGAT3

Interpretation: At the storage area a train

substitutes into a number of railcars

just determined (by the previous two

which substitute into rail cargo unit-

are routed from the storage area

checkpoint (RRGATE)

.

steps)

which

to a

4. Trucks are generated randomly by a given

interarrival time distribution.

Example: TABLE TA^TRCK POISSOU 45 BIN

Interpretation: A truck will arrive according to

a Poisson distribution with mean in terarrival

time of 45 minutes.

5. At a checkpoint trucks will be route! dependent on

the time cf day.

Example: ELEMENT TRORIG

TRUCK2N 3 TRUCKGBN + CARGOTRA

CARGOTRA B 30 3 AM

160 p:i STORAGSTR

2 40 PM

Interpretation: At the point TRORIG, trucks will

be routed to the truck storage area if

between 0300 and 1600 hours on the day

clock; otherwise they will leave the system.

5 . At the storage area van cargo units are routed to

THGATE.

Example: ELEMENT STORAGSTR

CARGOTRA R TRGATE

Interpretation: Vans enter the storage area and

will flow out through a "gate" as vans of

cargo (CARGOTRA)

.

7. Vessels are generated randomly by a given time

distribution.

Example: TABLE TARVSH POISSON 57 HR

Interpretation: A vessel will arrive according to

a Poisson distribution with a me^n

interarrival time of 57 hours.

3. The vessels that arrive enter the system as cne of

61

ten classes with a given distribution.

Example: SHIPRULS = 6.6 P VSSSEL01

7.9 p VESSEL04

21.1 P VSSSEL07

Interpretation: 6.6 percent of vessel arrivals

will be class 1; 7.9 percent of the vessel

arrivals will be class 4, and 21.1 percent of

vessel arrivals will be class 7.

9. The vessels of a particular class are given a

random total berth time according to a given distribution.

Example: TABLE BERTH05 TRIANGLE ? 184 HR

204.1 HR 22 4 HR

Interpretation: A vessel of class 5 will have a

berth time according to a triangular

distribution where zero percent of th= time

it will oe on berth 134 hours or less, 50

percent of the time it will be on berth 204.1

hours, and zero percent of the time it will

be on berth 224 hours or more.

10. The vessel is assigned a berth.

Example: ELEMENT HARBOR

SHI? S RULE SHIPROLS

VESSEL** 8 BERTH1 IF VESSEL** EVESSEL**

AT BERTH1 E^ OvJ

VESSEL** R 3ERTH2 IF VESSEL-* EVESSEL**

AT BSRTH2 EQ OS

ETC.

VESSEL** R 3SRTH6 IF VESSEL** EVESSEL**

AT BERTH6 EQ :W

WAIT

Interpretation: The arriving vessel goes to berth

1 if there is no vessel of any class at berth

one; otherwise it screens berth two, three,

four, etc., until it finds an empty berth.

If all berths ace full it waits until a berth

empties and then occupies it.

62

This page intentionally left blank,

63

APPENDIX B

SAMPLE OPERATING ROLES

This appendix contains a sample of the operating rules

used by TRANSIM for the N»S, Concord aiodel. The indexed

portions of the figures are explained in Appendix A.

64

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71

APPENDIX C

INITIAL SITUATION IN TH3 MODEL

rhe model was provided an initial situation in order to

have seme operations in progress at the start cf the

simulation. Figure 15 is i sample of the initial situation

provided to TRANSIM. The railcar and van storage areas had

500 raiicazs (that is, 50 x 10) and 150 vans respectively-

Each berth had an empty vessel prepared to load. The vessel

classes chosen were representative of those classes most

often i o 3 i e i a t the port

.

72

DA Y HR a MTN 3 =t. STroAKFRR TYP- CARGQRRA UN IT S 55_D AYDAYDAYday

HR 3 MIN EL STORAGE'R T YPE CA^GOTRA UNITS 1 50HR 5HR 6

MINMINMIN

1 EL 5ERTH23 EL 3£P T H3JL_Ii 53 ^TH5

day ; hr e;ay o hr aDAY h5 33Ar r hg j

MIN 3 EL BEPTH1MIN n EL 3E=TH*MIN : £L 3EFTH6MIN r, = L TR CR I G

DAY HR 3 MIN EL HA!=3 OR

TY=>E VES5EL05 UNITSTYPE VESSEL.?* UNITSTY PE VES SELDc UNITS

UNITSUNITSUNI ts

TY^E VSSSEL07TYPE VESSELC3TYPE VESSELTlTY°E TRUCKGENTYPE SHIP

DAY m=> 3 MIN 3 EL STDR*GE=?R TYPE TRAINTARVSH

J TAPCAIL

Figure 17 - INITIAL DATA IN THE MODEL

73

APPENDIX D

'ERATIVE PROCESS OF TRANSIT

This appendix provides a lemonstration of the pcocasses

th€ computer followed during the simulation. The output

shown has been extracted fro.?, one of the test runs used to

verify ths logic jf the model. There were some errors in

this un an 1 the reader, therefore, is cautioned to p<

attention only to those portions iescribed, which are

accurate images of the modal.

Items of interest are provided at the following svent

1 1 j e s :

15-14-51 A ship originates in the harbor and becomes a

vessel (#60635) delayed in the harbor awaiting a

berth because all berths are full.

15-13-11 A truck enters and immediately leaves the system

because of its arrival between 1600 an i Q,00

hours.

15-13-33 .a unit of railcars (#63187) completes

consolidation with the current carorder (#59134).

Tiis completes the loading function of this unit

it berth 4 and generates a completed order (NRRR

$60625) at berth 4. it the same time th-: next

caiorder (59124) moves to the loading element

element (MOVE 4) at berth 4 where it matches up

with the current railcar cargo (CARGORRA 463137)

:rc;, the 3RGAT2. Also the next railcar carjo

(CARGORRA 460934) moves :nn storage to the 3RGATE

74

awaiting the next carorder. (Author's note: in a

later program "MOVE" was changed to "LOAD" tc more

accurately reflect the actual loading function

being performed. The words have the sane meaning

throughout this portion of the extracted output.)

16- 3- A train cf 10 cars arrives, and becomes r^ilcar

cargo (CA5G0RRA) in storage.

16- 8-30 A truck arrives luring receiving hours and becomes

van carjo (CARGOTRA) which passes through the

TRGATE to the loading (MOVE 3) function at berth

3 where it begins consolidation with the current

vancrder. (In this case the truck storage area

wis empty of vans explaining why the van went

immediately to a berth. If the van storage area

was not depleted, the van would have been delayed

in storage similar to the railcars arriving at

16- 8- 0.)

17- 0-2 1 riie one- empty class 3 vessel (#55903) at berth

1 consolidates all completed van (RRTR) and

railcar (ORRR) shipments into a loaded vessel

(=57 153) and leaves tae system. Then the class 1

vessel (#60635) moves from the harbor to berth 1

where it becomes an ampty vessel with requirements

for 3 vanorders and 1 carorder ready for matching

with van and r.iilcar cargo at the respective

. i t^s.

75

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81

APPENDIX E

RESULTS OF SIiirjLATION RUN I

Simulation ru^ r wis run attempting to achieve 62.5

percent berth occupancy. The vessels arrivals were Pcisson

distributed with i mean interarrival time or 57 hours in

orier to jof. -rate about 7o vessels .a 130 lay period.

Train arrivals were liven a distribution of no trains

arriving 25 percent of the lays, one train 50 percent of the

days, a n 1 two train trains arriving 25 percent c f the days.

Arriving trains ha i 30 cars 50 percent of the ti;n a and 40

cars the remainder of +..--r time. Therefore, on the average,

35 raiicars arrived ^-ic':. lay.

Truck arrivals were Poisson listributed with a mean

interarrival time cf 45 minutes. H'^^ average number of

truck arrivals per sight hour receiving period wis expected

to be 10.6.

: ..i results for Simulation run I art shown in the

accompanying figures and are summarized is follows:

1) ecu nancy rate was 3.16/6 = 52.7 percent.

2) Van cargo exported was (1650) (18) = 29700 long tons.

3) Sail cargo exported was (547) (54 0) = 32 5080 long

tens.

a) Total cargo ^xoort?! was 325080 +29700 = 354,780 long

tons.

5) Namber of vessels turned around was 69.

6) Percent of vessels ielayed wis 100 - 57.9 = 12.1

82

percent.

7) Average/maximum number of vans in the storage area

was 177.0/345.

8) Average van storage time was about 366.0 hours.

9) Average/maximum number of railcars in the storage

area was 722/1420.

10) Average railcar storage time was ^bout 454.3 hours.

3 3

N*£ 1NCGRD EXPORT CAPABILITY SIX MONTH 1 3MA Y77

SUMMARY 1

XtPAiNCY OF ALL -i£R7hS (T3 T AL NUMBER OF 5HIP5 IN P:' T)

NUM3ER IN 5 ERVI CE

T^TAL NUN JMAX IMJM

£R IN ,^£p:«6 ( CN DAY

- O7 3 £ P. I C D

U OTHER TIMES*)A V ERA G ;

M I N I MJ A

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3 VcR

igure 24 - OCCUPANCY OF ALL BERTHS (SIM. I)

5 4

NWS Ca.NCCaO ZXPCkT capability SIX MCN'H

SUMMARY 13T.TAL -AIL CARGO EXPORTED (UNJT5 OF 54.: LCNG'GNS)

: TAL NUM3ER IN SFRVI Zz , C ~L i.YEC .1. _QE __ICLI_

~E" VL NUMBER IN REPOR"v: X T MUM 4-3 ( ON DAY

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Figure 25 - TOTAL TRUCK CARGO EXPORTED (SIM

NaS concord export capaeility SIX -10NTH

SUMMARY 12TOTAL TRUCK CARGO EXPORTED (UNITS OF 13 LONG""" 3N )

-:T4 L NUMBER IN SERVICE. CEL^YED* 0<5 IDL^

T,~~-L NUM3E D IN cEP n S'

VAX IMUM 1 - " (CM CiA

Y

,1V- F AG - 71 • 1 5MN IMUM ( IN C * Y

A ND

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5 OTHER T IMES )

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7C .31

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figure 26 - TOTAL RAIL CARGO EXPORTED (SIM. I)

NaS cncokd ^xpc^t cap4gil: t y

SUMMARY X5Sn I P

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Elapses I M = ( INCLUDI NG JcLAYS )

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T 1

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igure 27 - SHI? TIMS AT ALL BERTHS (SIM. A)

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38

NWS CONCORD EXPORT CAPAEILITY SIX MONTH

SUMMARY i

NUMBER OF VANS IN STORAGE *

_T_3_TAL NU'^^c IN SERVICE. CELAY^D, CP ICL~

TOTAL NUMBER in Rfprp T PERIODMAXIMUM 3^-5 (CN CAY 177 V<iV^~iG- l""T .'

r " _M\ I MUM ( DN DAY A'

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N*S CONCORD EXPORT CAPABILITY SIX MON'H 13".'AY77

SUMMARY 2NUMBER IF TRAINS (1* CAPS d£r TRAIN) AT STORAGE AREA

TOTAL NUMBER IN SERVICE, C FLAYED, nc IDLE

TO'AL NUMBERVAXlMU^ 14-2iVE c AG r 72

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Figure 31 - NUA3ER 0? TRAINS AT STORAGE AREA (SIM. I)

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APPENDIX F

3 2SULTS OF SIMULATION R rJ v: II

Simulation rur II was a run attempting to achieve 62.5

percent berth occupancy. The vessel arrivals were Poisson

distributed with a mean interarrivai time of 57 hours in

order to jenerata about 7b vessels in the 130 day period.

Train arrivals remained the same as for simulation run i

with no trains arriving 25 percent or the days, one train

arriving 50 percent of the days and two trains arriving 25

percent of the days. Arriving trains had 30 cars 50 percent

of the time and 4 cars the remainder of, the tine.

Therefore, on the iverage, 35 riilcars arrived each day.

Truck arrivals were Poisson distribute! with .n e a n

interarrivai time of 4 5 minu' during normal operations.

When the number of vans in the storage area w a: les than

10, the mean interarrivai time was 15 minutes and when the

numoer or vans in tne storage area was gr a t e r than 150, the

mean interarrivai time was 75 minutes. This was the first

of tne five simulation runs to use the non-homogeneous

arrival distribution. The average number of truck arrivals

ner eight hour receiving period was expected to oe 10.6.

Th results of simulation run II are shown in the

accompanying figures and a: j summarized as follows:

1) Occupancy rate was 3.62/6 = 6 0.3 percent.

2) Van cargo exported was (1929) (13) = 34722 long tors.

3) "ail cjll:i exported .'as (597) (540) = 322,380 long

tons.

4) T otal cargo exported was 322380 + 34722 = 357,102

long tons.

5) Number: of vessels turned around was 77.

6) Percent of vessels delayed was 100 - 64.9 = 35.1

percent.

7) Average/max imuni number of vans in the storage area

was 98.0/226.

3) Average van storage time was about 2C4.5 hours.


area was 114.3/500.

10) Average rail car storage time was about 78.5 hours.

34

NwS CCNCORD EXPORT CAPAElLITy SIX MONTHS 17MAY77SUMMARY IC

OCCUPANCY OF ALL 5E=~hS (t O t AL NUMBER OF SHIPS IN PO- T

)

-jmJAi^_JsJUVSEP_ll^«ERViC^A_n£LAYLED_i_.CR_XDLE.

TOTAL NUMBER IN REPOP"r PERIOD 77VAX I ^UM 5 (CN DAY "AT 9 0.

A NO 3 6 QTHFO T I ME S )

AVERAGE 3.62VINIMUM C (ON DAY r AT C«

AND 2 OTHER TIMES)

NUMEER PERCENT OF Q=POR" PERIOD

1 THFU I 10.

1

? THCU ? a^LiT3 THRU 3 2 2.0A THRU A C.35 THRU 5 7.06 THRU 6 28*0

over e o.o

igure 33 - OCCUPANCY OF ALL 3ERTH5 (SIM. II)

95

NaS CCNCORO EXPORT CAPAEILITY SIX MONTHS 17MAY77

SUMMARY UTOTAL TRUCK CARGO EXFCR'ED (UNITS OF 19 LONGTON)

T TA. L_ NU M BE R _ IN £E P V I CE. DELAYED. OR IDLE

TOT*L NUMBER IN oerocPT °ERICD 1929MAXIMUM 2 rv

<» (ON DAY 92 AT 330)*VE RAC C 9 3. • A A.

MNIMUM (ON DAY "5 A T C«AND 2 T HER T IMES )

NUMBER PERCENT OF RE°OP"r PERIOD1 .0

1THCU 10* 56,1

1C 1 THRU 20* 31 .62C 1 THRU 300 1.33CI THRU 4V 0.0401 THRU 50 C 0«05G 1 THRU 50r 0.06 C 1 THRU 7C r g , p70 1 THPU 800 CO3C 1 THRU = r »0rt i THRUl r C 3 0.0

DVER1000 .0

J^gurs 34 - TOTAL TRUCK CARGO EXPORTED (SIM. II)

9o

N.vs CCNCCRD -.KPCRT CAPABILITY 51* MONTHS 17V1Y77

SUMMARY 13TOTAL nAlL CARGO EXPORTED (UNIT5 GF 54C LONGTONS)

JIXLTAJ NLLM 5LER_I N. 5 FRVTCF. _ _QELA^ °D. CC IDLE

TCT4L NUMEHR IN PEPORT PERIOD 597MAXIMUM 5f (CN DAY 1^3 a,t 123)WgPAGg Ii^a9 9VINIMU^ f! (ON DAY * A T 0)

NUMBE" PERCENT OF REPORT PERIODC . .6

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igure 35 - TOTAL RAIL CARGO EXPORTED (SIM. II)

97

NrtS CuNCORD lXPCRT CAPABILITY SIX MONTHS 1 7va Y7'

SJVMASY 15SHIP TIME AT ALL 3EPTHS (ALL SHIPS)

5LAP: XJ_MF__LI_n CLUDJ N_G_JD c LA Y_5 )

"2TAL TIME 15195 HPS 26 MIN 22 SECMAXlMJM T I M£ 233 MPS 22 VI N 10 StC

AVc^AoL T I MlMl N IMJM TI ME

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(OCCURRED ON C1YHEGINMMG A T TIME

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NaS CONCORD EXPORT CAPAEILITY SIX MONTHS 1 7MAY77

SUMMARY 1

NUMoEP OK VANS IN STCRAGE A^EA

TOT A L NUMBER IN S ERVl CE, _ C EL AYEP. OP IDLE

TOTAL NUMBER IN REPORT PERIOD A071MAXIMUM 225 (ON CAY 153 AT 1522)AVERAGE 9J3 i* 1_M N I viU M (ON CAY D AT

ANO 52 OTHER "

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NUMBERr

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igure 33 - NUMBER OF VANS IN STORAGE AREAS (SIM. i:

100

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10 3

APPENDIX G

RESULTS Or SlrllJLATTON RON III

Simulation run i*as run attempting to achieve t2.5


distributed with i mean interarrival of 57 hours in order to

c^r^ratj id out 7 6 vessels in t 1 3 d ay pi 10- Thi run

was given a n e w

results.

to increase the randomness cf the

Train arrivals were liven a distribution of no trains

arriving 25 percent oc the days, org train 50 percent cf tae

days, and two trains arriving 25 percent of the days.

Arriving trains had 30 cars 50 percent of the time ana 40

cars th2 remainder of the time. Therefore, on the average,

3 5 rails a es arrived each day.

rruck arrivals were Poisson listributed with a mean

interarrival tine of 45 ninutes during normal operations.

ihen the number of vans in the storage area was less than

10, the j •? an interarrival time was 15 minutes and w > 2 n tne

number or vans in tne storage area was greater than 150, the

mean interarrival time was 75 minutes. The average number

of track arrivals per sight hour receiving period was

expected to he 10,6.

Th? results for simulation run ETI are s:iown in tne

•a cccmpa n l i n i figures aid are summarized as follows:

1) Occupancy rate was 4.16/5 = 69.3 percent.

2) Van cargo exported »as (2253) (13) = 40554 long tons.

104

3) Rail cargo exported was (659) (540) = 35686 long

tons.

4) Total cargo exported was 356860 40554 = 397,414

long tons.

5) Number or vessels turned around was 90.


percent

.

7) Average/maximum number of vans in the storage area

was 54.9/165.

8) Average van storaje ri^e was about 96.0 hours.

Q) average/maximum number of railcars in the storage

area was 136/650.

10) Average railcar storage ti :ue was about 75.0 hoars.

105

NaS CCNCCRD =XFCaT :apa3ilit SI X MONTHS 13MAY7'SLVMACy 1

CCCJ^ANCY OF ALL BERTHS (TOTAL NLM-Jch OF SHIPS IN PCRT)

. I Ai NUMRER _I b__3^PVICE. Of Jf-£LL»

TOTAL NUMBER IN REPORT PERIOD 90MAXIMUM 6 (ON DAY C AT SO.

4XD a-1 OTH ER TIME S_JLAVERAGEMINIMUM

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NUMBER PERCENT OF REPORT PERIOD1.6

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CCNCQRD EXPORT CAPABILITY SIX MONTHS 1PM/

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NUMQER PERCENT OF REPC £LT__E BJL Q_Q_

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figure U3 - TOTAL TROCK CARGO EXPORTED (SIM. Ill)

107

NWS CCNCCRC EXPC^ T CAPABILITY 5 I X MONTHS 16MA Y77

CARGO EXPORTEC (UNITS OF 540 LONGTCNS)

T PIT 41 NUM.E£L3_ _LQ1_£_

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103

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SLVMAKY 15SHIP TIME AT ALL 3E^ThS ( ILL SHIPS)

f: L >PS=Q T I V = L_IA£UJ£LU AYS)

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1 14

APPENDIX H

RESULTS 7 SI.'DILATION RUl] IV

Simulation run IV was run attempting to achieve 90


distributed with interarrival time of 3 7 hours in

order to generate about 117 vessels in the 130 day period.

rrain arrivals were given a distribution of no trains

arriving 25 percent of the days, one train 50 percent of the

days, i a ; two trains arriving 25 percent of the lays.

Arriving trains had 50 cars UO percent of the time and o0

cars the remainder of the time. Therefore, on the average,

56 raiicars arrived each day.

Trici arrivals wer^ Poisson listributed with a mean

interarrival tine of 30 minutes during normal operations.

'.ihsn the number of vans in the storage area was l^ss than

10, the .map. interarrival time was 10 minutes and when the

numoer of vans in the storage area was greater than 150, the

aean intararrival time was 50 minutes. The average number

of truck arrivals per sight

expect el to be 16. 6

.

hour receiving period *is

The results for simulation run TV are shown in the

accompanying fijures and are summarized as follows:

1) Occupancy rate was 5.9 9/6 = 9 9.6 percent.

2) Van cargo exported was (3 132) (18) = 5o37b lona tors.

3) Rail cargo exported was (1026) (540) = 55404G long

tons.

1 15

4) Total cargo exported was 554040 + 56376 = 10,416

long tons.

5) Number of vessels turned around was 127.


percent

.

7) h verage/maxiiuum number of vans in the storage ir^a

was 5 5.2/150.

8) average van storage time was about 72.2 hours.

}) average/maximum number of railcars in the storage

area was 105/520.

10) Average railcar storage time us about 42.5 hours.

1 16

NWS CCNCCRC EXPORT CAPABILITY SIX MONTHS 19MAY77

V M A R Y 1 C

CCCUPANCY CF ALL ci^ThS (TCTAL NL^EEF OF SHIPS IN FCRT)

-IO-EAJ auyfJ r3 TM._£=av.LC=_«- I1E1 _LQ1_

TCTAL NUMBER IN REPORT FERIOC 127MAXIMUM 6 (ON DAY _AT 8 0.

AVERAGE E»<59VINIMLV C (ON CAY AT CO)

NUV6E3

1 T J- R UZ Th£U

—

3 THR U4 thru5 THFL£ Th^'J—

PERCENT GF REPCPT PERIOD0.2

C V

u > ij,

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Figure 51 - OCCUPANCY OF ALL BERTHS (SIM. IV)

1 17

NWS CCNCCRC EXPCRT CARAdlLlTY SIX MONTHS 1 9MA

SLWAPY 12TOTAL TRUCK CARGO iXPC3T-C (UNITS OF 13 LGNGTCN)

TOTAI NiiM - C R r \, c-w\/TrFf p-i ay-^ t re

im -

TCTAL NUNieEPi IN wEPCPT RE^IGC 3152MAXIMUM 211 (GN DAY 146 AT 1253)-WER * G5 13 6 .4 ?M IN I MUV C (GN DAY AT )

NUVbER PER' :nt CF REPORT PEP I

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3»0C.

Figure 52 - TOTAL TRUCK CARGO EXPORTED (SIH. IV)

1 13

N*S CCNCCRC EXFCRT CAPABILITY SIX MONTHS 1=

SLNN1 A rY 12TOTAL KAIL CARGO EXFCRTEC (UMTS OF 54C LONGTCNS)

tct;l NJ^E^ IN serv IJ_E__CELAYEO. Cw IDL 2

TCTAL MJM8ER IN f^EPCST FERICC 102 6.lAXHUM 45 (ON DAY 7 AT 2:52.

ANC _r. TK_L TIMES)WE RAGEMINIMUM

24, 94C (ON DAY 3 AT )

J_____

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1 THRU1 THRU2 T_L_-1 THRU1 THRU1 TH-Ul_J_r__

PERCENT GF . R=LE_i_J- p £ R I _.0.6

1 CC 39.4200 CO30. C 0_04 C 0.05 C 0,06 0.0__. i x.-

703C9

THRU 900THRU10C02 v e r i :

0.00.0

"igure 53 - TOTAL RAIL CARGO EXPORTED (SIM. IV)

119

NaS CCNCCRC EXFCPT CAPABILITY SIX MONTHS 19MA Y

TC T a L time 2 5 1 = HR S 4 M I

N

VAXIMUV TIME 2.2 1 HRS 2 M IN 2 c

SECSEC ( occurred

_3 EHNM NG-

( CCCLPREDBEGINNING

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144 ,

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VINIMUM TIME ICE MRS 2 4 A IN27I 6

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TOTAL NUMBER 27

Figure 54 - SHIP TIME AT ALL BERTHS (SIM. IV)

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rs'aS CCNCCKD EXhCRT CAPABILITY S*I X MONTHS

S L V V A F Y 1

NUMBER CF VANS IN S T J -! A G H AREA

CXAL NUMBER IM-SE^-V-TrF . P.Fl A^-FR. OR U

COO V8 C )

TCTAL NUM3EK IN REPCRT PER ICCv^xiyuv 1:C (QN DAY OATV If. I MUM C (CN DAY AT 5 C »

ANC IC 2 OTHER TIMES)

Figure )6 - NUMBER OF VANS IN STORAGE AREAS (SIM. IV)

122

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SLVMACY 2NLMeER C c TRAINS (10 C A -5 5 ?E^ TRAIN) AT STORAGE AÊA

-TJUAJ NLL&Ei SSRV LCE.J -AX £S« n .5_JLCL5_

TOTAL MUêE^ IN rÊPCRT PERIOD £125VAX MUM 53 (ON DAY Z AT 8 0)AVFRAGF 1.£L*_4jVIMÛM C (ON DAY C AT 8 C.

AND 187 OTHER TIMES)

rNT HF tfi-'PHRT Lac.

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Figure 58 - NUMBER OF TRAINS AT STORAGE AREA (SIM. IV)

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125

APPtNOIX I

RESULTS 0? SIMULATION 2UN V

Simulation run V was run attempting to achieve SO

percent berth occupancy. The vessel Arrivals were Poisson

distributed with a mean in terarrival time of 4 2 hours in

order to generate about 104 vessels in the 130 day period.

Train arrivals were given a distribution of no trains

arriving 25 percent of tha days, one train 50 percent cf the

lays, and two trains arriving 25 percent of the days.

Arriving "-.rains had 50 cars 100 percent of the tine.

Therefore, on the average, 50 railcars arrived each lay.

Truck arrivals were Poisson distributed with a mean

interarrival time of 33 minutes during normal operations.

When the number of vans in the storage area was less than

10, the lean interarrival time was 13 minutes and when the

number of vans in the storage area was greater than 150, the

mean Interarrival time was 53 minutes. The average number

of truck arrivals per eight hour receiving period was

expected to be 14.5.

z results for simulation run V are shown in the

accompanying figures and are summarized as follows:

1) Occupancy rate was 4.65/6 = 77.5 percent.

2) /ir. cargo exported was (2496) (18) = 44928 long tons.

3) Bail cargo exported was (340) (540) = 453600 lona

tons.

4) Total cargo exported was 453600 + 44928 = 458,528

126

long tons.

5) Number of vessels turned around was Q 7.

6) Percert of vessels lelayed was 100 - 44.3 = 55.7

percent

.

7) Average/maximum number or vans in the storage area

was 9 4 . u / 1 9 6

.

-3) Average van storage time was about 153.1 hours.


urea was 500/1530.

10) Average railcar storage time was about 404.9 hours.

127

NWS CCNCORO EXPORT CAPABILITY SIX MONTHS 20MAY77ViAKY 10 '

OCCUPANCY OF ALL BERTH-S (TOTAL NUMBER OF SHIPS IN PCRT )

T G TA I NUMBER—IN S-R-VICE.—OEL-AYEH. OR IDLE

TOTAL NUMBER IN REPORT PERIOD 97MAXIMUM 6 (ON DAY AT 8 0.

-AUO 6-5—&-T4HER T-H4E S->-AVERAGE 4,65MINIMUM (ON DAY OAT 00)

NUMBER PERCENT OF REPORT PERIOD0. 2

1 THRU 1 1.92 THR U 2 _-2-*-0 ______3 THRU 3 16»84 THRU 4 7.65 THRU 5 10.76 THRU 6 50.9

OVER 6 0.0

Figure 60 - OCCUPANCY OF ALL BERTHS (SIM. V)

123

NWS CGNCCRC EXPORT CAPABILITY SIX MONTHS 20MAYSUMMARY 12

TOTAL TRUCK CARGO EXPORTED (UMTS OF 13 LONGTON)

_T_OXAi— NU.VHFR TN SERVICE,. DELAYED. OR IDLE

TOTAL NUMBER IN REPORT PERIOD 2496MAXIMUM 205 (ON DAY 26 AT 16 9)A^ERAG-£MINIMUM C (ON DAY AT 0)

NUMBER PERCENT OF REPORT PERIOD

1 THRU 100 42.7101 THRU 200 57.0201 THRU 30C 0.1—3^>4_T_><py—4 3^ 0^-Q401 THRU 500 0.0501 THRU 600 0.0601 THRU 700 0.07 1 THRU 30 -0 0^-0-S01 THRU 900 0.0901 THRU103C 0.0

OVER1 300 0.0

Figure 6 1 - TOTAL TRUCK CARGO EXPORTED (SIM. V)

129

nN*S CCNCCRC EXPORT CAPABILITY SI X MONTHS 2CMAY77

SLVMAPY 13TOTAL RAIL CARGO EXPCRTEC (UNITS OF 54 LONGTONS)

^N S E RVICE*—T.ELAYED.—OR IDLE

TOTAL NUMBER IN REPORT PERIOD 840MAXIMUM 47 (ON DAY 23 AT 1227)

. AA/-ERAGE 2 V+&ZMINIMUM ( N D A Y AT 0)

NUMBER PERCENT OF REPORT PERIODQ-,-6

1 THRU 100101 THRU 200201 THRU 300-3^-^-THRU—A&G-401 THRU 50C501 THRU 60C601 THRU 700701 THRU 300

59.4O.C0,0

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Figure 62 - TOTAL RAIL CARGO EXPORTED (SIM. V)

1 30

N-S CCNCORC EXPORT CAPABILITY SIX MONTHS 2CMAY77

SLVMAPY 15SHIP TIME AT ALL BERTHS (ALL SHIPS)

._I_J___3_L. !i_—C_lJVCL.UDXN.C_D.tl_A YS L

TOTAL TIME 19522VAXIMUM TIME 237

AVERAGEVIM MU M

TIMET I ME

2C1108

HRSHRS

HRSHRS

2553

Ts44

M INM IN

MINM IN

g1 1

4427

SECSEC (OCCURRED

Q-EGXMvlNGSECSEC (OCCURRED

BEGINNING

ONAX-

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DAYJLXME-

DA YTI ME

17,—7- ax

160.610)

HRS MI N SEC HRS1^IN SEC FREQUENCY PER CENT

96 c Cfi LE ss 0. C96 c THRL 103 0.

1 03 r. THRU 1 20 Q c 5« 212C C THRL 1 32 C C 0.132 C THRL 1 44 c 0.144 C THRL 156 1

1 1 .

1 =;* r> n Th-RU 168 a o o 0»163 c a THRU 130 4 4, 1

iec g j THRU 192 o U 9 9. 3192 c C THRU 204 c 27 27.8PP4. r n THRL ? 1 6 28 29.Q216 o THRL 228 c 21 21 .6228 c THRU 240 2 2. 1

2a? c THRL 252 0. C5 = ? n o THRL 264 l-> r> t n

264 c THRL 276 0.276 c THRL 238 Q c 0. C

MCRE THAN 283 O.C

TOTAL NUMBER 97

Figure 63 - SHIP TIME AT ALL BERTHS (SIM. V)

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N*S CONCORD EXPORT CAPABILITY SIX MONTHS 20MAY77SUVMARY 1

NUMBER CF VANS IN STORAGE AREA

T C TAJ— NLLMBER IN SER-V.LCE-, DELA.YE.O. OR- IDLE

TOTAL NUMBER IN REPORT PERIOD 5299MAXIMUM 196 (ON DAY 104 AT 1559)_—A-VE-RA<i£ 94^42-MINIMUM (ON OAY AT 8 0.

AND 72 OTHER TIMES)

-NU-MaS.fi P-&R-CSNT OF REPORT PERIOD1.6

1 THRU 25 15.326 THRU 50 12,2

—5-1 -T--HRU 75 1-3-, 4-

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.I 54__T-HRU— 1-7-5 1-8.-7-

176 THRU 200 7.32C 1 THRU 225 0.0226 THRU 250 0.02SI- THRU- 2 75 0.-0276 THRU 300 0.0301 THRU 325 0.0326 THRU 350 0.0-35-1 THRU 375 0-.-0-

376 THRU 400 0.0OVER 400 0.0

Figure 65 - NUilBER OF VANS IN STORAGE AREAS (SIM. V)

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SUV MARY 2NUMBER CF TRAINS (10 CA^S PER TRAIN) AT STORAGE AREA

T_QIAi__NUMaER_ L-N—SE-BV-I C £. aELAY£-CU—QR IPLE-

TOTAL NUMBER IN REPORT PERIOD 1747MAXIMUM 153 (ON DAY 140 AT 8 0)

_ AVERAGEMIN IMUM

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-S-O*Q (ON DAY AT 8 0»AND 1 OTHER TIMES)

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Figure 67 - NUMBER OF TfiAINS AT STORAGE AREA (SIM. V)

135

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136

appendix j

vessel export tonnages in class groupings yg1 january

through ju;;:^ 19cb

This appendix is a representation of all vessels in each

class, of the vessels that made complete turnarounds in the

first six months of 1363, snowing the relative distribution

of vessel cargo tonnages within each class and the number of

vessels in the class.

137

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1 3

APPENDIX K

132 TRIANGULAR DISTRIBUTION

Tha triangular distribution is a distribution made

availa-ola to the users of T3ANSIK. The upper and lower

ranges of the event possibilities ara defined to have zero

probability. The only other significant point on the

probability curve is it an arbitrary point between the upper

ind Lowar limits *ith an assigned probability. Connecting

tha threa points p orms a triangle, h>?nc? the name. In™

"curve" is just two connecting straight lines. The figure

belorf jives an example of the triangular density function

for tha berth time listribution o£ a class 01 vessel use-! in

this aod ?1

.

igure 70 - \ TRIANGULAR DISTRIBUTION

13)

LISP OF 23FSEE>I CES

1 • Iisj_tor Brief in gx Naval Weapons Station, Extracted from

the Haval Ordnance Capability Catalog.

1H )

3Ibliograp:iy

1 . Bre liar, , Loo, Statistics, With View Toward

10

1 1

Applications, Houghton Mifflin, 1973.

Cinlar, Erhan , Introduction to Stochastic Processes,

Prentice-Hall, 14 75.

Fishman, G. S., Concjjto _anj Methods Ln r)iocr-3te Event

Digit al Simulation, tfiley, 1973.

Frank, .

.

Transmission,

and Frisch, I. T. , Coinai unicat ion

ml Transportation Net wo rks,

Addison- Wesley, 13 71.

Gas s, S. I., Linear Pr oar 2a^J.nj , McGraw-Hill, 4 th

edition, 1975.

Hadley, G., Linear ? to^rd^jino , Addison-Wesley , 1962.

Larson, H . J., Introduction to Probab ility Theorv and

Statistical Inference, Wiley, 1969.

loder, J. J. and Phillips, C. R., Project /Jinajetont

±Ll- CF^ and ?_:JT , Reinhold, 1964.

raft, C. A., Management of ?avsical 2istrJ.buJ.ian 1R&

lE3.H£22Ii^-^ii2H' Irwin, 1972.

2ZKS£lL'- II General ZlL2'2^2. Il^lll Simulator, User ' s

Uanual, School of Engineering and Applied Science,

UCLA, 1971.

isitor Brief ing^ '^I'i^.L Weapons Station, Extracted from

the Naval Ordnance Capability Catalog.

U1

12. Whitehouse, G. E. , Systems Analysis and C^si^n Using

Network Techniques, Prentice-Hall, 1973.

1 4 2

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No. Copies

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Nav\i Postgraduate School

Monterey, California 93 94

Command and General Stiff College

ATTN: Educational Advisor

2oom 123, Bell Hall

Fort Leavenworth, Kansas

LCD 3. Robert * . Sagehorn, USN Code 54 s

Administrative Sciences Department

Naval Pcstgradjate School

Monterey, California 93 940

LI. Don Bouchoux, USi.' Cole 553H

Operations Research Department

Naval postgraduate School

Monterey, California 93940

CPT. David C. Fountain

LHA , New Cumberland Army Depot

v Cumberland, Pennsylvania 17070

143

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Thesis

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c# l Naval Weapons Station/

Concord export capabil-

ity: a simulation mod-

el. .2667 6

17 CCT £8 3 2 8 8 1

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ThesisF6635c.l

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