Modeling the Interaction Between Railroad Freight Schedule Adherence and Asset Utilization

Yan Dong*Joseph M. Sussman**

Carl D. Martland**

* Transport Dynamics, Inc. 103 Carnegie Center, Princeton, NJ 08610** Department of Civil and Environmental Engineering

Massachusetts Institute of Technology

October 26, 1998

Outline

• Background

• Degrees of Schedule Adherence

• Asset Utilization

• Simulation Model

• Case Studies

• Conclusions

Background

• Recently, an active debate about what kind of operating strategy best fits railroad operating characteristics– scheduled approach

– flexible approach

– mixed approach

• Specifically: how schedule adherence affects asset utilization

• Debate is qualitative as opposed to quantitative

• Very limited research

Degrees of Schedule Adherence -- Three Operating Strategies

• Schedule-adherence (SCH)– railroad operations are conducted according to the operating plan

– run trains according to train schedules

• Flexible short-run scheduling (FSS)– establish a short-run plan based upon expected traffic and resources

– railroad operations are then conducted according to this short-run plan

• Flexible operation (FLX)– railroad operations are conducted according to traffic

– run trains according to traffic volume

Degrees of Schedule Adherence -- Three Operating Strategies (cont.)

Impact of theOperating Plan

Stochasticity &Uncertainty

Degree ofSchedule-Adherence

Centralized vs.Decentralized

Control

PlanningTimeFrame

SCH High Ignore High CentralizedControl

MonthlyQuarterly

FSS Middle Consider Low Mixed 8 hrs

FLX Low Fully Consider Ignore DecentralizedControl

(Close to)Real Time

Asset Utilization

• Trains (# IB & OB trains run, % trains on-time, OB train delays due to lack of power and crew units)

• Cars (# IB & OB cars, % cars on-time, # cars missed 1st connection)

• Road crew & power units (# units in and out of terminal, average yard times, # units deadhead in and out)

• Terminal average yard time

• Terminal connection performance

• Terminal processing time

• Line-haul movement time

• OD trip time

Simulation Model

• A microscopic discrete event-driven simulation model

• Simulate detailed network (terminal & line) operations under different operating strategies– detailed movement of car, train, crew, and power unit

– utilization of terminal/line capacity, terminal resources, and road crews and power units

• Internal and external stochasticity are fully considered – traffic & arrival variation, delays by accidents, maintenance, weather, etc.

– processing variability in terminal and line-haul movement

– flexibility to add terminal resources (inspectors & crews), unscheduled trains, & deadhead in/out road crew and power units

Model Input

• Operating plan

• Terminal resources and capacity

• Line capacity

• Classification terminal block to (bowl) track assignment

• Classification terminal processing capability

• Cutoff time

• Other parameters to specify various internal and external stochasticity

Model Assumption

• SCH– IB arrival reliable but traffic volume variable

– fixed starting time to assemble OB trains based on the operating plan

• FSS– IB arrival & traffic volume between SCH & FLX

– predict traffic, crew, and power units, and determine assembling time

• FLX– IB arrival variable but traffic volume reliable

– start to assemble OB trains whenever traffic ready

Case Studies

• Actual data from a Class I railroad

• Three case studies– terminal

– service lane network

– area network

• Each study contains– Base case (e.g., current operating condition)

– Sensitivity analysis (e.g., internal/external variables changed to see how they affect asset utilization)

– Scenario design and runs (e.g., various operating conditions)

Service Lane Network Case Study: Layout

A

Bc

df

e

101

102103

104

105

Area Network Case Study: Layout

A

B

C

e

g h

f

id

101

102

103

104105

106

107

108

109

Model Execution Time(for simulating one month operation)

Execution Time(in seconds)

SCH FSS FLX

TerminalCase Study

10.33 11.69 11.90

Service LaneNetwork

Case Study

21.93 24.94 24.85

Area NetworkCase Study

27.95 34.94 33.94

Terminal Case Study: Base Case Result

OB OB avg avg total avg DH DH avg DHon conn. yard proc crew in out power out

trains time perf. time time time crews crews time units

SCH 421 410 99.16% 26.54 10.89 9.30 1.20 0 14.64 411 18 0.52% 0.07 0.16 0.80 0.84 0.00 0.16 4

FSS 375 194 91.33% 24.58 11.17 14.33 0 46 13.15 763 6 0.31% 0.27 0.07 0.32 0.00 2.17 0.25 10

FLX 349 177 80.35% 30.39 12.54 15.40 0 70 14.48 1342 7 1.25% 0.34 0.21 0.23 0.00 3.51 0.14 9

Terminal Case Study: Scenario Design

Scn traffic traffic proc proc capcity cutoff system systemvolume variation time variation time resource prep

C1 l l l l h l h hC2 l l m m m m m mC3 l l h h l h l lC 4 m m l l h l h hC 5 m m m m m m m mC 6 m m h h l h l lC 7 h h l l h l h hC 8 h h m m m m m mC 9 h h h h l h l l

Area Network Case Study: Base Case Result Trip Time Summary Table

Orig Dest # trains int_yard avg_line avg_trip # delays avg_delaysch A B 211 2.65 4.46 7.10 5 0.65

1 0.02 0.00 0.03 4 0.29A C 210 3.00 5.58 8.58 4 0.55 0 0.06 0.00 0.06 3 0.32B A 210 2.27 4.47 6.74 2 0.38 0 0.04 0.00 0.04 2 0.38B C 210 1.73 2.45 4.19 1 0.21 0 0.02 0.01 0.02 2 0.21C A 209 3.51 5.58 9.09 1 0.24 1 0.07 0.00 0.07 1 0.31C B 210 1.53 2.45 3.98 1 0.32 1 0.05 0.00 0.05 3 0.48

fss A B 217 2.46 4.47 6.94 24 1.91 3 0.07 0.00 0.06 30 0.46A C 200 3.34 5.58 8.92 26 1.15 3 0.05 0.00 0.05 29 0.71B A 198 2.52 4.47 6.99 30 1.40 4 0.03 0.01 0.03 34 1.28

B C 218 1.65 2.46 4.11 35 1.39 2 0.04 0.00 0.04 38 1.20C A 216 3.33 5.58 8.91 5 0.68 3 0.06 0.01 0.05 5 0.78C B 200 1.68 2.46 4.13 3 0.63 2 0.04 0.01 0.04 4 0.59

flx A B 196 2.66 4.46 7.12 38 2.04 4 0.04 0.00 0.04 24 0.34A C 177 3.55 5.57 9.12 33 1.72 2 0.07 0.01 0.06 21 0.41B A 174 2.69 4.46 7.16 14 0.88 2 0.06 0.00 0.06 29 1.20B C 193 1.81 2.45 4.27 18 0.75 6 0.02 0.00 0.02 35 1.30C A 193 3.54 5.57 9.11 39 1.99 3 0.09 0.00 0.09 60 2.51C B 176 1.80 2.45 4.26 33 2.07 4 0.04 0.01 0.04 50 2.87

Area Network Case Study: Base Case Result

Term IB_trns On_time OB_trns On_time avg_pwr avg_crw avg_yard avg_connsch A 441 354 421 401 8.63 9.83 28.17 96.48%

5 8 1 14 0.88 0.98 0.40 1.20%B 442 370 420 411 9.03 12.40 27.72 97.18% 7 10 0 8 1.16 1.29 0.66 2.50%C 443 357 420 402 10.73 11.96 27.69 96.35% 5 8 1 14 1.09 1.13 0.70 2.76%

fss A 441 314 415 209 9.75 11.23 25.64 87.20% 4 12 4 7 1.95 3.25 0.91 1.32%B 440 302 417 219 9.58 11.04 25.59 86.95% 6 5 5 7 1.93 3.94 1.01 2.15%C 440 304 416 207 9.71 12.28 25.20 87.84%

3 6 4 9 1.05 1.02 0.51 0.74%flx A 444 207 374 207 9.38 11.96 32.30 72.92%

5 13 6 3 1.62 3.10 1.36 1.10%B 440 196 368 201 10.93 16.16 32.23 73.06% 2 7 6 11 2.15 5.17 1.40 2.18%C 441 209 370 196 10.53 12.59 33.20 70.27%

4 13 4 6 2.14 4.77 3.20 6.40%

Terminal Performance Summary Table

Conclusions

• SCH – very reliable customer service – high resource utilization– more resources needed as buffers to recover to the plan

• FSS– very high resource utilization– very high terminal through put (smallest average yard time)– low operating cost– achievable plan development is an issue

• FLX– cost saving train operations – can handle traffic increase easily and robust– customer service & resource utilization are concerns

Conclusions (cont.)

SCH FSS FLX

Resource utilization High Higher Low

Resource requirement High Medium Low

Operating cost Medium Low High

Train cost High Medium Low

Customer service High Medium Low

Robustness (to handletraffic increase, etc.)

Low Medium High

Conclusions (cont.)

• Railroads do not have enough resources and an incentive to apply SCH (even FSS) for all traffic priorities; FSS may not provide good service for high priority traffic; FLX is only appropriate for low priority traffic

• For high priority traffic such as auto and intermodal traffic, use SCH

• For medium priority traffic such as general merchandise traffic, use FSS

• For low priority traffic such as bulk (coal) unit trains, use FLX

• Applying different operating strategies to different traffic priorities as a strategic tool to differentiate rail freight service

• For railroads, customer service requirement, their willingness to pay for service, available resources, and capacity are driven factors determining which operating strategies are used