agent based models for enterprise wide optimization...

Agent Based Models for Enterprise Wide Optimization and Decision SupportOptimization and Decision Support

Raj SrinivasanRaj SrinivasanDept of Chemical & Biomolecular Engg

National University of SingaporeProcess Systems & Modeling

Institute of Chemical & Engg Sciencesy g p gg

[email protected]

CMU, 1 Dec 20091

Process Systems Engineering• Chemical cluster design & optimization

• Enterprise-wide Opt & DS

CEO• Agent-based modeling • Dynamic simulation• Disruption management

Enterprise wide Opt & DS

Supply Chain

Enterprise

• Refinery (re)scheduling

• Disruption management

Planning / Scheduling

Supply Chain

• Inherent safety / Sustainability studies

• Schedule robustness metrics

Process Supervision

Process Optimization

S ft / l t

studies• Waste minimization / Energy /

Water optimization

Unit Control

p• Soft sensors / alarm mgmt• Process transitions mgmt• Fault diagnosis• Fault tolerant control

Plant Operator• Image based control of particulate processes

2

A Energy Company

Electricity

R fi i

Jet Fuel

Oil & Gas Transportation

Refining

Productionp

Storage & Transportation

Petrol & Diesel

Petrochemicals

3

Supply Chain Management

Crude Oil Operations Scheduling

4

All for the want of a nail…“For want of a nail, the shoe was lost,For want of a shoe, the horse was lost,For want of a shoe, the horse was lost,For want of a horse, the rider was lost,For want of a rider, a message was lost,, g ,For want of a message, the battle was lost,For want of a battle, the kingdom was lost,And all for the want of a nail!”

George Herbert, in Outlandish Proverbs (1640)

5

Integarted Models of Supply Chains & Enterprises

6

PSE 101: Unit Operations

Unit-leveld li d i i l ti t l ti i timodeling, design, simulation, control, optimization

“Physicochemical phenomena”Physicochemical phenomena Virtual units through Objects / Aspects 7

PSE 102: Process PlantsPlant-wide

design, simulation, controlsynthesis planning schedulingsynthesis, planning, schedulingsupervision, maintenance, risk

“Network of units” 8

From PSE to PSE2

PSE2 101: Entities & FacilitiesEnterpriseSuppliers Customers

LSPsLSPs

“Network of Networks”9

Process Flow Diagrams

Methanol product

Plant-widedesign, simulation, control

synthesis planning schedulingsynthesis, planning, scheduling,supervision, maintenance, risk mgmt

10

Supply Chain

Raw material sourcing Primary production Secondary production Warehouses RetailersCustomers

11

Refinery Supply ChainLogistics

Provider CRefinery BLogistics

NaphthaBrent Crude Provider A

Ethylene

Refinery CLogistics LPG

Chemicals Manufacturer D

Arab CrudeyLogistics

Provider B

Logistics

LPG

Crude Producer

Provider D

Information Flow Material Flow MaterialInformation Flow Material Flow Material

12


• Fundamental Questions– What product (mix) to sell?p ( )– What raw materials are needed and when should they be bought?

• Operate Drivers• Operate – Demand forecasting– Scheduling & planning

Drivers Optimize / manage logistics,

inventories, other supply chain resources

• Design– Facility / network planningFacility / network planning– Transportation network design

13

Business Processes of a RefineryTypical Supply Chain

Sales Operations

StStorage Procurement Logistics14

Crude Procurement Process

Posting PostingPosting

EXCHANGE

1. Procurement initiation

2. Market dataFetch quotes from postings on

the exchange

LEGEND

...............

Posting gPosting

3. Crude basket

5. List of pickup location

6. Request for bids

OPERATIONS

PROCUREMENT

4. Refined crude basketand pickup date for

each crude

7. Bids received from 3PLs

CBA

OPERATIONS

STORAGE

31 2

AND

OR

Bid deadline over

8. List of best bids foreach crude9. Place order for

crude

10. Orderconfirmed

SALES

11. Information on crudebought

12. Contractawarded to

respective 3PL13. Order

Confirmation14. TransportInformation

15. TransportInformation

LOGISTICS

CBA3PL

OIL SUPPLIER

15

Agent based Models of Supply Chains & Enterprises

16

What is an Agent?An computational entity

– Perceives its environment Element in a MA System• MAS: Loosely coupled

(sensors)– Acts upon it (actuators)– Is autonomous

MAS: Loosely coupled network of agents– Collectively capable of solving

problemsIs autonomous– Pursues goals or carries out

tasks to meet objectivesProactively or reactively

problems– Achieve goals beyond an

individual agent – Proactively or reactively – Relying on Social-ability

• Heterogeneous agents• Interaction Coordination

Cooperation Competition

Planning Negotiationg

Distributed Centralized

g

17

Agents in Refinery Supply Chain

Refinery Departments

PostingPostingPosting

PETROLEUMEXCHANGE

PROCUREMENT

...............

PROCUREMENT

31 2STORAGESALES

OIL SUPPLIERS

LOGISTICS 3PLsCBA

LOGISTICSOPERATIONS3PLs

18

Agent Behavior

Agent: Time or StorageMessage: Start Procurement Cycle

Agent: Sales

Agent: SalesMessage: Request Forecast

Agent: SupplierMessage: Inform Forecast

Agent: SupplierMessage: Inform Available Crude

Message: Query Available Crude

Agent: OperationsMessage: Refine Crude Basket

Agent: OperationsMessage: Refined Crude Basket

Agent: Logistics

Agent: LogisticsMessage: Request 3PL to Bid

Agent: Supplier

PROCUREMENT

Agent: LogisticsMessage: Best Bid from 3PL

Agent: SupplierMessage: Confirmation of Purchase

Agent: SupplierMessage: Send Purchase Order

Agent: LogisticsMessage: Confirmation of PurchasePrimary function: Purchase crude

Agent: LogisticsMessage: Compiled Transport Info

Agent: StorageMessage: Inform Transport Details

Plan: <plan name="procure crude">

Primary function: Purchase crudeGoal: Purchase at fair price, whenever required

Plan: <plan name= procure_crude ><body>new CrudeProcurementPlan()</body><trigger> <messageevent ref="start_procurement_cycle"/> </trigger>

</plan>

19

Agent Interactions

20

Agent Interactions

• Message passing to emulateemulate– Information flow– Material flowMaterial flow

• Communication between agents resultsbetween agents results in ‘discovery’ of supply chain structure’ – Structure established

dynamically not pre-specifiedspecified

21

Supply Chain Flow Diagram

22


23

System Flow Diagrams

Methanol product

Raw material sourcing Primary production Secondary production Warehouses RetailersCustomersa ate a sou c g y p y Warehouses Retailers

24

Simulation Studies

1500

Crude 1 Crude 2 Crude 3 Crude 4 Crude 5

1000

Vol

ume

(kbb

l)

500Cru

de V

000 14 28 42 56 70 84 98 112

Time (days) 25

Throughput – Actual vs. Planned

220

200

y)

Actual Planned

160

180

hput

(kbb

l/day

140

160

CD

U T

hrou

gh

120

1000 14 28 42 56 70 84 98 112

Time (days)26

Decision SupportDecision Support

New policies: Planning, Scheduling, Tactical decisions: Tank conversionTactical decisions: Tank conversion

Strategic decisions: Capacity expansion

27

Effect of Policies on Supply Chain Performance

KPI Base Case New ProcurementPolicy

New ProductionPolicy

Average Crude InventoryAverage Crude Inventory (kbbl) 2232.5 1866.6 2036.8

Average product Inventory (kbbl) 1976.9 1953.8 1417.9 (-28%)y ( )

Average CDU throughput (kbbl) 145.5 144.9 135.0 (-7%)

Product revenue 1065.0 1065.0 1065.0Crude Procurement Cost 942.6 908.8 (-4%) 854.4

Crude Inventory Cost 13.4 11.2 (-16%) 12.2P d t I t C t 11 9 11 7 8 5 ( 28%)Product Inventory Cost 11.9 11.7 8.5 (-28%)

Operating Cost 35.1 35.0 32.59Product deficit Penalty 0.0 0.0 0.0

Demurrage cost 14.5 0.0 (-100%) 0.0Profit (Million$) 47.76 98.51 (+106%) 157.30 (+60%)

Strategic Decision MakingGasoline

600

800

1000

nd (k

bbl)

Jet Fuel

200

300

nd (k

bbl)

Product demands expected to go up

0

200

400

0 120 240 360 480Time (days)

Fore

cast

Dem

an

0

100

0 120 240 360 480Time (days)

Fore

cast

Dem

an

expected to go up

CDU Expansion? YesTime (days)

Diesel

400

600

800

man

d (k

bbl)

Time (days)

Fuel Oil

200

400m

and

(kbb

l)Yes

0

200

400

0 120 240 360 480Time (days)

Fore

cast

Dem

0

200

0 120 240 360 480Time (days)

Fore

cast

Dem

350

400

200

250

300

350

U T

hrou

ghpu

t (kb

bl/d

ay

Investment $100 millionOpportunity: $ 249 7 million

100

150

200

0 120 240 360 480Time (days)

CDU

Before CDU capacity expansionAfter CDU capacity expansion

Opportunity: $ 249.7 millionPayback period: 0.44 year 29

New Types of DecisionsNew Types of Decisions

Risk Identification & ManagementDisruption: Emergency procurement,

Domino effectsDomino effectsOthers: Quantify risk, Supplier contracts

30


• Fundamental Questions– What product (mix) to sell?p ( )– What raw materials are needed and when should they be bought?

• Operate Demand forecasting

Drivers O i i / l i i– Demand forecasting

– Scheduling & planning– Risk management

Disruption management

Optimize / manage logistics, inventories, other supply chain resources

Take advantage of market opportunities– Disruption management• Design

– Facility / network planning

Take advantage of market opportunities in product demands, raw material availabilities

React efficiently to disruptions and– Transportation network design– Risk management

React efficiently to disruptions and other supply, production, or demand uncertainties

31

Supply chains are VulnerableRisk drivers:• Global operations• Single sourcingg g• Specialized

production sites• Outsourcing

L l h i• Lean supply chain• Just-in-time

McKinsey survey (2006):• Two-thirds: risks have increased over theTwo thirds: risks have increased over the

past five years• 41%: company doesn’t spend enough

time or resources on mitigating risk• 25% no formal risk assessment• Almost half lack company-wide

standards to help mitigate risk32


33

ESupply Chain HAZOP Analysis

• Deviation:• No crude arrival

No + Crude arrival

• Causes• Jetty unavailability• Shipper disruption

arrival

• Supplier stock-out• Consequences

• Low stock, out-of-crudeO ti di t d• Operation disrupted

• Demand unfulfilled• Safeguards

• Safety stock• Safety stock• Mitigating Actions

• More reliable shipper34

Consequence Analysis via Simulation

3PL ReliabilityAverage Customer Satisfaction (%)

i h3PL Reliability

Average Profit ($, million)

High Low

Yes 98 95

No 95 91

Safety stock

High Low

Yes 93 38

No 83 27Safety stockNo 95 91

• Current safety stock cannot make up for poor performance of 3PL provider • Additional safeguards or mitigating actions required e g higher safety

No 83 27stoc

• Additional safeguards or mitigating actions required, e.g. higher safety stocks, emergency crude procurement.

• Probability of crude arrival delay: High 3PL reliability (0 05) Low 3PL reliability (0 10)• Probability of crude arrival delay: High 3PL reliability (0.05) – Low 3PL reliability (0.10)• Safety stock: crude (100 kbbl) – product (20%)• # Simulation runs per scenario: 300• Demand variability across cycles: 25%

Si l ti h i 120 d• Simulation horizon: 120 days

35

Process Control & Supervision

Controller Malfunction

Feedback Controller

Sensor FailureProcess DisturbanceH E

Dynamic ProcessActuator Sensorsu y

Sensor FailureProcess DisturbanceHuman Error

Actuator Faults Structural Failures

Diagnostic System

36

Disruptions in Crude Procurement Process

PostingP ti

EXCHANGE

1. Procurement initiation

2. Market dataFetch quotes from postings on

the exchange

LEGEND

...............

Posting PostingPosting

3. Crude basket

5. List of pickup location

6. Request for bids

OPERATIONS

PROCUREMENT

4. Refined crude basket

p pand pickup date for

each crude


CBA

OPERATIONS

STORAGE

31 2

AND

OR


Bid deadline over

8. List of best bids foreach crude9. Place order for

crude

10. Orderconfirmed

SALES

11. Information on crudebought

12. Contractawarded to

respective 3PL13. Order

Confirmation14. TransportInformation

15. TransportInformation

LOGISTICS

CBA3PL

OIL SUPPLIER

37

Supply Chain DashboardA DCS for the Supply Chain######

Customer Satisfaction 100

95 98 100

40

60

80

100

Perc

enta

ge

Revenue from Sales

288

241 238

298

150

200

250

300

350

ales

(K$)

Operating Cost

245 230 243

298

150

200

250

300

350

atin

g C

ost (

K$)

pp y

Brent Crude GasolineRaw Materials Shipment Actual Demand Forecast DemandM d Di l

0

20

Mar '08 Apr '08 May '08 Jun '08

P

0

50

100

Mar '08 Apr '08 May '08 Jun '08

Sa

0

50

100

Mar '08 Apr '08 May '08 Jun '08

Ope

ra

800 800Raw Materials Shipment Actual Demand Forecast Demand Arrival Date Type Status Due 24 Jul 08 Due 31-Jul-08

1 27-Jul-08 B & O & A On time Gasoline 250 Gasoline 2802 3-Aug-08 B & K On time CDU Throughput Jet Fuel 63 Jet Fuel 953 9-Aug-08 B & O & A Scheduled Diesel 125 Diesel 1254 16-Aug-08 B & O & A Scheduled Kuwait Crude Total throughput 200 Jet Fuel Fuel Oil 122 Fuel Oil 154

Brent 90Kuwait 110 Products ShipmentD b i 0 Shi t D t T St t

Mode Diesel

0

400

800

800

1200 400

0

400

800

Dubai 0 Shipment Date Type StatusOman 0 1 G + D WIPArab Light 0 2 J + F WIP

Dubai Crude Diesel

24-Jul-0824-Jul-080

400

0

400

800

1200

0

400

800

0

Oman Crude Fuel Oil

Arab Light Crude

0

0

400

800

1200

0

400

0

g

Today's Date

20-Jul-08 0

400

800

38

Disruption Management

Supply ChainKPI

Corrective A ti

2000

2500

100

150

200

250

300

350

1

4

7

KPIs Actions

Monitoring of KPIs

100

200

300

400

500

1000

1500

0

500

1000

1500

2000

0 10 20 30 40

0

50

100

150

200

250

300

0

50

100

0 10 20 30 40 50 60 70 80 90 100Seeking Optimal Rectification:

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

0

0 10 20 30 40

0

0 20 40 60 80 100

Seeking

Rectification optionsAlarms

Root Cause Diagnosis

Seeking Rectification Actions

Diagnosis

Disruption Details 39

Scenario 1: Transportation DelayInventory Profile

200025003000 • Ship’s scheduled date of arrival:

115

0500

10001500

0 20 40 60 80 100 120

• Ship’s delayed date of arrival: 121

• Amt of Crude on Board: 1560Throughput Details

200

250

300

350 Stock-out for 2 days

• Amt of Crude on Board: 1560 kbbl

• Disruption detected on: 112 (f i f

0

50

100

150

200

0 20 40 60 80 100 120

(from inventory, future shipments, throughput KPIs)

• Product to be delivered on: 120Product Inventory

200250

300350

• Date of Stock-out: 119• Amt of crude Shortfall: 758 kbbl

050

100150

200

0 20 40 60 80 100 120 140

Unchecked DisruptionDisruption Managed

40

Scenario 1: Transportation Delay

Can’t unload into Tank 6

Parcel 7 delay from Day 115 to 121

as it is charging CDU 3

-30 U3-50 U3-50 U3100 P7100 P7

Tank 6 will run out of crude at time 11

Existing schedule is infeasible!41

All for the want of a nail…“For want of a nail, the shoe was lost,For want of a shoe, the horse was lost,For want of a shoe, the horse was lost,For want of a horse, the rider was lost,For want of a rider, a message was lost,, g ,For want of a message, the battle was lost,For want of a battle, the kingdom was lost,And all for the want of a nail!”

George Herbert, in Outlandish Proverbs (1640)

42

Spec Chem Mfg EnterpriseRefinery

B Oil

ZDTPRefinery

Isotanker

Supplier

Base Oil

Lube Additive Plant SulfonatesDepot

Drums

ppMMA

Dispersants

Reaction

Blending

Lube Additive Package

Packaging

Others

Consumer

Supplier(ethyleneamines, etc) Plant

Plant 1 Plant 2 Plant 3Plant 1

HQ Customer

Plant 2 Plant 3

43

Enterprise Model

C tRaw Material &

il liCustomers

Central Sales

Base Oil Suppliers

Department

Plant 1 Plant 2 Plant 3Plant 1 Plant 2 Plant 3

•Scheduling •Operation•PackagingSt

•Scheduling •Operation•Packaging

•Scheduling •Operation•Packaging

•Storage•Procurement•Economics•Logistics



44

Enterprise-level Coordination

2

13

4

6

5

Scheduler

Operations

P k i

Storage

Procurement

E iPackaging

Logistics

Economics

45

Integrated Enterprise Model – ILAS

46

Case Studies

• CS 1 & 2: Job assignment policy• CS 3: Reassignment during plant disruption• CS 4: Scheduling policyg p y• CS 5: Procurement policy• CS 6: Strategic decision• CS 6: Strategic decision• CS 7: Dealing with unreliable 3PL

47

Case Study 1: Job Assignment Policyy

Equal Assignment

Expected Completion

Date

13 69 15 68 + 15%

• Base case: Equal Assignment» Orders are assigned to 3 plants equally

• New policy: Expected Completion Date

μ ± σ

Overall Profit (M$)13.69 15.68

± 0.86 ± 0.87

Overall Customer Satisfaction

66% 85%

± 12% ± 14%

15%

+ 29%

• New policy: Expected Completion Date» Orders are assigned to plant which can

deliver at the earliest• All orders are accepted, no matter how

Overall Plant Utilization

89% 90%

± 3% ± 4%

Total Tardiness (days)

294.53 75.62

± 178.58 ± 99.16

- 74%

plate the delivery will be

» for fair comparison

Expected Completion Date results in:• higher profit• higher customer satisfaction and lower tardiness Coordination between HQ and plants improves overall performance.

Demand 1x; 360 days simulation; 100 simulation runs; ± 250 orders48

Case Study 2: Job Assignment Policy (cont.)( )

+ 2.3%

• Another policy: Customer Location» Assign order to the plant nearest to the

customer if it can meet due date

Expected Completion

Date

Customer Location

O ll P fit (M$)9.01 9.22

μ ± σ

- 4%

» If it can’t, try the next nearest plant» If no plant can meet order’s due date, the

order will not be taken (i.e. missed order)

Overall Profit (M$)± 0.88 ± 0.90

Delivery Cost (M$)1.98 1.71

± 0.13 ± 0.09

O C 93% 89%

- 13%

- 4%• vs. Expected Completion Date» with missed order allowed


93% 89%

± 3% ± 4%


98% 97%

± 1% ± 1%

Customer Location based job assignment results in:• lower customer satisfaction, higher tardiness

» doesn't always assign to the earliest plant» less buffer from due date

Total Tardiness (days)

2.88 4.47

± 1.26 ± 1.86

No of Missed Orders

26.59 27.01

± 4 21 ± 4 15» less buffer from due date• higher profit

» transportation savings more than offset late penalties

± 4.21 ± 4.15

Demand 1.3x; 180 days simulation; 100 simulation runs; ± 125 orders49

Case Study 3: Reassignment during Plant Disruption

• No reassignment» job-in-progress will be restarted

when the plant is back up and jobs

No Disruption

No Reassignment

With Reassignment

Overall Profit (M$)

7.77 7.60 7.58

± 0 68 ± 0 66 ± 0 67

μ ± σ

in the schedule follow• Job Reassignment policy

» job-in-progress and jobs in the schedule of disrupted plant is sent

( $) ± 0.68 ± 0.66 ± 0.67


99% 98% 99%

± 1% ± 2% ± 1%

Singapore Plant ( )

0.25 37.33 0.14schedule of disrupted plant is sent back to Sales

» Sales reassigns these jobs to the other 2 plants

Tardiness (days) ± 0.66) ± 29.2 ± 0.40

Houston Plant Tardiness (days)

0.21 0.39 0.60

± 0.50 ± 0.80) ± 1.02

Japan Plant 0.18 0.29 0.32• Singapore plant disruption from day Japan Plant Tardiness (days)

0 8 0 9 0 3

± 0.48 ± 0.84 ± 0.94


85% 84% 83%

± 5% ± 4% ± 4%

• Singapore plant disruption from day 75-100

• With no reassignment» High tardiness for Singapore plant

No of Missed Orders

1.27 2.69 3.16

± 1.23 ± 2.06 ± 2.21

» High tardiness for Singapore plant due to disruption

Job Reassignment results in:» Tardiness of Singapore plant significantly decreased

Demand 1.2x, 180 days simulation; 100 simulation runs; ± 125 orders

» Tardiness of Singapore plant significantly decreased» with very small increase in tardiness of the other 2 plants» and slightly more missed order 50

Case Study 4: Scheduling PolicyPEDD PEDD-LJS

Overall Profit (M$)19.14 19.37

± 1.19 ± 1.07

• PEDD (Processing Earliest Due Date)New Job

Job ID: 13PEDD: Day 22

μ ± σ


65% 92%

± 4% ± 2%


97% 97%

± 1% ± 1%

PEDD: Day 22

Utilization ± 1% ± 1%

No of Missed Orders

54.16 56.88

± 5.76 ± 5.93New Job

Job ID: 13PEDD: Day 22

• PEDD with Late Jobs Consideration

PEDD i h L J b C id i l i

If the insertion of the new order causes List No 2 to be late, the new job will be placed one level lower so as to avoid a late job

PEDD with Late Jobs Consideration results in:• significantly improved customer satisfaction (0.92, up from 0.65)• slightly more missed order

» PEDD-LJS completion date is later or at most same

Demand 1.3x; 360 days simulation; 100 simulation runs; ± 250 orders

» PEDD-LJS completion date is later or at most sameA small modification to a policy could have a big impact.

51

Conclusions

• Dynamics are importantSh t t & l t– Short-term & long-term

– Decisions related to operation, control & design

• Agent based models offer a natural paradigm for• Agent based models offer a natural paradigm for modeling the enterprise– Simulation-optimization strategy for designSimulation optimization strategy for design– Control structure for disruption management

• From PSE to PSE2 (= PSE of Enterprise)o S o S ( S o e p se)– Analogy from PSE are useful

• Representation, Modeling & simulation • Control & supervision

52

agent based models for enterprise wide optimization...

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