Issues in Dynamic Fleet Management Talk at ROUTE 2000 - INTERNATIONAL WORKSHOP ON VEHICLE ROUTING SKODSBORG, DENMARK - AUGUST 16-19, 2000 Geir Hasle Research.

Issues in Dynamic Fleet Issues in Dynamic Fleet ManagementManagement

Talk atTalk at

ROUTE 2000 - INTERNATIONAL WORKSHOP ROUTE 2000 - INTERNATIONAL WORKSHOP ON VEHICLE ROUTINGON VEHICLE ROUTING

SKODSBORG, DENMARK - AUGUST 16-19, 2000SKODSBORG, DENMARK - AUGUST 16-19, 2000

Geir HasleGeir Hasle

Research Director, Department of Research Director, Department of OptimizationOptimization

SINTEF Applied MathematicsSINTEF Applied Mathematics

Oslo, NorwayOslo, [email protected]@math.sintef.no

http://www.oslo.sintef.no/am/http://www.oslo.sintef.no/am/

My talkMy talk

SINTEF Applied Mathematics SINTEF Applied Mathematics (SAM)(SAM)

Fleet ManagementFleet Management– industrial potential, status, requirementsindustrial potential, status, requirements– technologytechnology– research, scienceresearch, science– bridging the gap between science and industrybridging the gap between science and industry

ChallengesChallenges Routing etc. at SAMRouting etc. at SAM Research AgendaResearch Agenda

3

SINTEFThe Foundation for Scientific and Industrial Research at the Norwegian Institute of Technology

Business concept:SINTEF´s goal, in co-operation with NTNU and UiO, is to meet needs of the private and public sectors for research-based innovation and development

The vision:Technology for a better society

Locations:The SINTEF Group have 1800 employees, 400 in Oslo and 1400 in Trondheim.

SINTEF Electronics and CyberneticsSINTEF Electronics and Cybernetics

SINTEF Applied MathematicsSINTEF Applied Mathematics

SINTEF Civil and Environmental EngineeringSINTEF Civil and Environmental Engineering

SINTEF Applied ChemistrySINTEF Applied Chemistry

SINTEF Materials TechnologySINTEF Materials Technology

SINTEF Industrial ManagementSINTEF Industrial Management

SINTEF Telecom and InformaticsSINTEF Telecom and Informatics

SINTEF UnimedSINTEF Unimed

SINTEF Petroleum ResearchSINTEF Petroleum Research

MARINTEKThe Norwegian Marine Technology Research Institute

MARINTEKThe Norwegian Marine Technology Research Institute

SINTEF Energy ResearchSINTEF Energy Research

SINTEF Fisheries and AquacultureSINTEF Fisheries and Aquaculture

President/Vice-president

President/Vice-president

SINTEFs board

SINTEFs board

SINTEFscouncil

SINTEFscouncil

SINTEF-Group turnover in SINTEF-Group turnover in 19991999

Strategic programs from The Research Council of Norway 4,3%

Contracts 92,4% - Industrial and commercial

enterprises 53,0% - Public sector 12,5% - International contracts

10,5% - The Research Council of

Norway (project grants)

9,9% - Other sources 6,5%

Basic grants from The Research Council of Norway 3,3%

SINTEF Applied MathematicsSINTEF Applied Mathematicshttp://www.oslo.sintef.no/amhttp://www.oslo.sintef.no/am

A contract research institute in the A contract research institute in the SINTEF groupSINTEF group

Geometry

Modeling

Optimisation

Simulation

Focus:Applied research

PlanningDecision Support

Focus:Applied research

PlanningDecision Support

Basis:Knowledge Based Systems

Operations ResearchComputing science

Basis:Knowledge Based Systems

Operations ResearchComputing science

Application types:Resource optimisationDesign/configuration

Discrete

Application types:Resource optimisationDesign/configuration

Discrete

Main business areas:Transportation

Area managementOil business

Manufacturing

Main business areas:Transportation

Area managementOil business

Manufacturing

SINTEF Applied Mathematics Department of OptimisationSINTEF Applied Mathematics

Department of Optimisation

Approach:Generic Tools

ReuseMethodology

Approach:Generic Tools

ReuseMethodology

Transportation of goods in Transportation of goods in NorwayNorwayand EUand EU

12.000 companies (EU 1/2 mill.)12.000 companies (EU 1/2 mill.) Annual turnover 30 billion. (EU 1.200 billion.)Annual turnover 30 billion. (EU 1.200 billion.) Many SMEsMany SMEs 36 % empty driving36 % empty driving Capacity utilization with load: 60 %Capacity utilization with load: 60 % Logistics costs 12% of product costs (EU 7%)Logistics costs 12% of product costs (EU 7%) EU: 13 million trucks, 800 billion ton-kilometers EU: 13 million trucks, 800 billion ton-kilometers

(1990)(1990) Germany: freight income some 60 billion DM (1990)Germany: freight income some 60 billion DM (1990)

Industrial use of VRP Industrial use of VRP ToolsTools

Excess travel, huge potentialExcess travel, huge potential Swedish report* 1999 (commercial road Swedish report* 1999 (commercial road

transport)transport)– large end users, food & beveragelarge end users, food & beverage– generation of static routesgeneration of static routes– vendors claim operational toolsvendors claim operational tools– very high potential for savingsvery high potential for savings

* A. Henriksson, P. Liljevik: ”Dynamisk ruttplanlegging i verkligheten”* A. Henriksson, P. Liljevik: ”Dynamisk ruttplanlegging i verkligheten”

Minirapport MR 123, TFK - Institutet för transportforskning, Stockholm October 1999Minirapport MR 123, TFK - Institutet för transportforskning, Stockholm October 1999

Increasing need for VRP Increasing need for VRP ToolsTools

focus on focus on – timetime– costcost– utilizationutilization– customer servicecustomer service– lead time reductionlead time reduction– reactivityreactivity

regulations, environmental concernsregulations, environmental concerns e-commerce, home shoppinge-commerce, home shopping

Reasons for mismatchReasons for mismatch lack of awareness in industrylack of awareness in industry lack of data and infrastructurelack of data and infrastructure price (SMEs)price (SMEs) organizational problems, resistanceorganizational problems, resistance practical constraintspractical constraints

– information availabilityinformation availability– physical movementphysical movement

tools not good enoughtools not good enough– functionality, modelling powerfunctionality, modelling power– user friendlinessuser friendliness– integrationintegration– logistical performancelogistical performance

Existing tools - keywords Existing tools - keywords • Large variety: simple TSP - sophisticated VRP solversLarge variety: simple TSP - sophisticated VRP solvers• focus: road transportation of goods, local distributionfocus: road transportation of goods, local distribution• built for operative planning, used for generation of static built for operative planning, used for generation of static

routesroutes• packagespackages• primitive integration, but good import facilitiesprimitive integration, but good import facilities• inflexible and simple or heavy on consultancyinflexible and simple or heavy on consultancy• Windows-platformWindows-platform• good user interfaces, map visualization, manual changesgood user interfaces, map visualization, manual changes

• VRP algorithms?VRP algorithms?• real-time planning?real-time planning?• multiple users?multiple users?• continuous optimization?continuous optimization?

• priced at USD 40.000 and above (high end)priced at USD 40.000 and above (high end)

Some VendorsSome Vendors • Descartes Systems Descartes Systems USAUSA• Caps Logistics -> BaanCaps Logistics -> Baan USAUSA• MicroAnalytics MicroAnalytics USA/GBUSA/GB• Roadnet Technologies (UPS) Roadnet Technologies (UPS) USAUSA• i2 i2 USAUSA• ESRI ESRI USAUSA• Kositzky and Associates Kositzky and Associates USAUSA• Manugistics Manugistics USAUSA• Carrier Logistics Inc Carrier Logistics Inc USAUSA• Insight Inc. Insight Inc. USA/The USA/The

Netherlands/GBNetherlands/GB• Caliper Corporation Caliper Corporation USAUSA• Trapeze Software Group Trapeze Software Group USA/CanadaUSA/Canada

• Giro Giro CanadaCanada• DPS International DPS International UKUK• Paragon Software Systems Paragon Software Systems UKUK• Optrak (Andersen Consulting) Optrak (Andersen Consulting) UKUK• Ilog Ilog FF• Diagma Diagma FF• PTV PTV DD• AlfaplanAlfaplan DD• PLSPLS DD• PrologosPrologos DD

Typically claim 10 - 30% cost reductions - static routesTypically claim 10 - 30% cost reductions - static routes

Few VRP Tools in Few VRP Tools in Operation in NorwayOperation in Norway

Coca-ColaCoca-Cola Taxi companiesTaxi companies FalkenFalken NASNAS NKLNKL Tollpost-GlobeTollpost-Globe LinjegodsLinjegods Postal servicePostal service Hydro AgriHydro Agri

Challenges - VRP Challenges - VRP ToolsTools

FunctionalityFunctionality ModellingModelling

– constraintsconstraints– criteriacriteria– uncertaintyuncertainty– dynamicsdynamics– supply-chain coordinationsupply-chain coordination

AdaptabilityAdaptability Power: speed vs. qualityPower: speed vs. quality Large-size problemsLarge-size problems User InterfaceUser Interface IntegrationIntegration Support etc.Support etc.

Dynamic, real-time routing Dynamic, real-time routing - Success stories?- Success stories?

Paragon - TescoParagon - Tesco– “… “… Home shoppers simply log onto the Home shoppers simply log onto the

dedicated area of Tesco's website, select their dedicated area of Tesco's website, select their purchases and identify a two hour time window purchases and identify a two hour time window for delivery to an address of their choosing” ...for delivery to an address of their choosing” ...

TruckstopsTruckstops– “… “… In some UK applications it is even used to In some UK applications it is even used to

recalculate routes during the day, modifying recalculate routes during the day, modifying its original calculations to take account of new its original calculations to take account of new requirements and reflecting data transmitted requirements and reflecting data transmitted back from vehicles by radio” …back from vehicles by radio” …

PriceWaterhouseCoopersPriceWaterhouseCoopers

Goal - VRP TechnologyGoal - VRP Technology

real benefits for industry - real benefits for industry - logistical performancelogistical performance– solve right problemsolve right problem– plan quality vs. response timeplan quality vs. response time– user interaction, user-friendlinessuser interaction, user-friendliness– configurabilityconfigurability– reactivityreactivity– priceprice

Future VRP technologyFuture VRP technology GIS vendorsGIS vendors ERP vendorsERP vendors ASP solutions, thin clients, Internet, wwwASP solutions, thin clients, Internet, www

better tools for strategic/tactical planningbetter tools for strategic/tactical planning supply-chain coordination, integrated supply-chain coordination, integrated

solutionssolutions

dynamic, real time fleet managementdynamic, real time fleet management

Dynamic Fleet Dynamic Fleet Management - Management - PrerequisitesPrerequisites

ICT infrastructureICT infrastructure– order dataorder data– fleet datafleet data

access to high quality traffic dataaccess to high quality traffic data– speed predictionsspeed predictions– ““organic” electronic road networkorganic” electronic road network

Better understanding of routing policiesBetter understanding of routing policies Better VRP algorithmsBetter VRP algorithms

Issues in VRP researchIssues in VRP research Large, ill-structured problemsLarge, ill-structured problems rich modelsrich models

– uncertaintyuncertainty– dynamicsdynamics– multiple criteriamultiple criteria

reactivityreactivity– disruption?disruption?– slackslack– policiespolicies

plan quality vs. response time plan quality vs. response time performanceperformance

decompositiondecomposition human issueshuman issues

Stochastic and dynamic VRPsStochastic and dynamic VRPs

what does “dynamic” mean?what does “dynamic” mean?– problem changes dynamicallyproblem changes dynamically– Psaraftis (1995): “... information on the problem is made known to Psaraftis (1995): “... information on the problem is made known to

the decision maker or is updated concurrently with the the decision maker or is updated concurrently with the determination of the set of routes.”determination of the set of routes.”

– Baita, Ukovich, Pesenti, Favaretto (1998): “... releated decisions Baita, Ukovich, Pesenti, Favaretto (1998): “... releated decisions have to be taken at different times within some time horizon, and have to be taken at different times within some time horizon, and earlier decisions influence later decisions.”earlier decisions influence later decisions.”

– a.k.a. “real-time”, “on-line”a.k.a. “real-time”, “on-line”

““organic” routing plansorganic” routing plans– challengeschallenges

information flowinformation flow physical goodsphysical goods

– good idea? (talk of Carlos Daganzo)good idea? (talk of Carlos Daganzo)

Literature - dynamic Literature - dynamic VRPsVRPs

6 INFORMS sessions since 1995, some 20 6 INFORMS sessions since 1995, some 20 paperspapers

some 50 journal paperssome 50 journal papers

Some papersSome papersPsaraftis (1995): Dynamic vehicle routing: Status and prospectsPsaraftis (1995): Dynamic vehicle routing: Status and prospects

Bertsimas, DJ / SimchiLevi, D (1996): A new generation of vehicle routing research: Robust algorithms, addressing uncertaintyBertsimas, DJ / SimchiLevi, D (1996): A new generation of vehicle routing research: Robust algorithms, addressing uncertainty

Crainic, TG / Laporte, G (1997): Planning models for freight transportationCrainic, TG / Laporte, G (1997): Planning models for freight transportation

Baita, F / Ukovich, W / Pesenti, R / Favaretto, D (1998): Dynamic routing-and-inventory problems: A reviewBaita, F / Ukovich, W / Pesenti, R / Favaretto, D (1998): Dynamic routing-and-inventory problems: A review

Swihart, MR / Papastavrou, JD (1999): A stochastic and dynamic model for the single-vehicle pick-up and delivery problemSwihart, MR / Papastavrou, JD (1999): A stochastic and dynamic model for the single-vehicle pick-up and delivery problem

Savelsbergh, M / Sol, M (1998): Drive: Dynamic routing of independent vehiclesSavelsbergh, M / Sol, M (1998): Drive: Dynamic routing of independent vehicles

Ioachim, I / Desrosiers, J / Soumis, F / Belanger, N (1999): Fleet assignment and routing with schedule synchronization constraintsIoachim, I / Desrosiers, J / Soumis, F / Belanger, N (1999): Fleet assignment and routing with schedule synchronization constraints

Gans, N / VanRyzin, G (1999): Dynamic vehicle dispatching: Optimal heavy traffic performance and practical insightsGans, N / VanRyzin, G (1999): Dynamic vehicle dispatching: Optimal heavy traffic performance and practical insights

Reiman, MI (1999): Heavy traffic analysis of the dynamic stochastic inventory-routing problemReiman, MI (1999): Heavy traffic analysis of the dynamic stochastic inventory-routing problem

Gendreau, M / Guertin, F / Potvin, JY / Taillard, E (1999): Parallel tabu search for real-time vehicle routing and dispatchingGendreau, M / Guertin, F / Potvin, JY / Taillard, E (1999): Parallel tabu search for real-time vehicle routing and dispatching

Powell, WB / Towns, MT / Marar, A (2000): On the value of optimal myopic solutions for dynamic routing and scheduling Powell, WB / Towns, MT / Marar, A (2000): On the value of optimal myopic solutions for dynamic routing and scheduling problems in the presence of user noncomplianceproblems in the presence of user noncompliance

Cheung, RK / Muralidharan, B (2000): Dynamic routing for priority shipments in LTL service networksCheung, RK / Muralidharan, B (2000): Dynamic routing for priority shipments in LTL service networks

Gendreau, M / Laporte, G / Seguin, R (1996): Stochastic vehicle routingGendreau, M / Laporte, G / Seguin, R (1996): Stochastic vehicle routing

Gendreau, M / Laporte, G / Seguin, R (1996): A tabu search heuristic for the vehicle routing problem with stochastic demands and Gendreau, M / Laporte, G / Seguin, R (1996): A tabu search heuristic for the vehicle routing problem with stochastic demands and customerscustomers

Haughton, MA (1998): The performance of route modification and demand stabilization strategies in stochastic vehicle routingHaughton, MA (1998): The performance of route modification and demand stabilization strategies in stochastic vehicle routing

Yang, WH / Mathur, K / Ballou, RH (2000): Stochastic vehicle routing problem with restockingYang, WH / Mathur, K / Ballou, RH (2000): Stochastic vehicle routing problem with restocking

Haughton, MA (2000): Quantifying the benefits of route reoptimisation under stochastic customer demandsHaughton, MA (2000): Quantifying the benefits of route reoptimisation under stochastic customer demands

Secomandi, N (2000): Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demandsSecomandi, N (2000): Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands

Shieh, HM / May, MD (1998): On-line vehicle routing with time windows - Optimization-based heuristics approach for freight Shieh, HM / May, MD (1998): On-line vehicle routing with time windows - Optimization-based heuristics approach for freight demands requested in real-timedemands requested in real-time

Kilby / Prosser / Shaw: Dynamic VRPs: A Study of Scenarios (forthcoming)Kilby / Prosser / Shaw: Dynamic VRPs: A Study of Scenarios (forthcoming)

Approaches - uncertainty, Approaches - uncertainty, dynamics dynamics

ignoreignore deterministic model - and repairdeterministic model - and repair

– crisp, optimized plans are brittlecrisp, optimized plans are brittle– is disruption costly?is disruption costly?– add slack, how?add slack, how?

stochastic modelstochastic model– investigation of policiesinvestigation of policies– still need dynamic decision-makingstill need dynamic decision-making

lessons to be learnt from factory schedulinglessons to be learnt from factory scheduling

Dynamic VRP DSSDynamic VRP DSS dependent on high quality updated dependent on high quality updated

informationinformation– fleet statusfleet status– order statusorder status

““organic” route planningorganic” route planning– concept of current planconcept of current plan– when do we commit?when do we commit?– when do we include changes?when do we include changes?– locking parts of planlocking parts of plan– do we need to worry about disruption?do we need to worry about disruption?– dependence on type of operation / business rulesdependence on type of operation / business rules

delivery vs. pickupdelivery vs. pickup

– applicable algorithmsapplicable algorithms– (how much) do we save by taking a dynamic approach?(how much) do we save by taking a dynamic approach?

ApproachesApproaches insertion heuristics + iterative insertion heuristics + iterative

improvementimprovement constraint propagation constraint propagation

MP formulations?MP formulations?

Minimal disruption possibly an additional Minimal disruption possibly an additional goal criterion componentgoal criterion component

Routing at SAMRouting at SAM

SPIDERSPIDER GreenTripGreenTrip HAMMER - vessel routing with inventory HAMMER - vessel routing with inventory

constraintsconstraints Bus schedulingBus scheduling eCSPlain, EU FP VeCSPlain, EU FP V Distributed problem solvingDistributed problem solving ProposalsProposals

SPIDERSPIDER

a VRP Solver C++ program librarya VRP Solver C++ program library– UNIXUNIX– WindowsWindows– COM componentCOM component

instantiates to a module for optimised instantiates to a module for optimised transport managementtransport management– plan-administrasjonplan-administrasjon– VRP optimisationVRP optimisation– cheapest path calculationscheapest path calculations

adaptable to wide variety of applicationsadaptable to wide variety of applications distribution through sw vendorsdistribution through sw vendors

GreenTripGreenTrip Esprit 20603, January 1996-March 1999, > 40 person-Esprit 20603, January 1996-March 1999, > 40 person-

yearsyears ConsortiumConsortium

– Tollpost-Globe (N) Tollpost-Globe (N) – Pirelli (I)Pirelli (I)– Ilog (F)Ilog (F)– University of Strathclyde (GB) University of Strathclyde (GB) – SINTEF (N)SINTEF (N)

RTD effort in methods, algorithms, and generic sw for RTD effort in methods, algorithms, and generic sw for optimised fleet managementoptimised fleet management

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The goal of GreenTripThe goal of GreenTrip

Produce a Produce a cost-effectivecost-effective tool to tool to optimise routing of vehicles thatoptimise routing of vehicles that– is genericis generic– takes into account multiple business takes into account multiple business

constraintsconstraints– permits efficient (re)configuration permits efficient (re)configuration – integrates easily in existing IT integrates easily in existing IT

infrastructure infrastructure

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GreenTrip Technical GreenTrip Technical ApproachApproach

OO ProgrammingOO Programming Constraint ProgrammingConstraint Programming Iterative Improvement TechniquesIterative Improvement Techniques Applications ModellingApplications Modelling Automated Systems Automated Systems

(Re)Configuration (Re)Configuration

33

The GreenTrip ConsortiumThe GreenTrip Consortium

SINTEFSINTEF

Tollpost-Globe

Tollpost-Globe PirelliPirelli

ILOGILOG UoSUoS

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CASE : TOLLPOST-GLOBECASE : TOLLPOST-GLOBE

Pick up orders : 600Pick up orders : 600 Regular and non-regular customersRegular and non-regular customers Deliveries : 2.400Deliveries : 2.400 Time windows - Customer serviceTime windows - Customer service Two days are not the sameTwo days are not the same some 100 vehiclessome 100 vehicles Different vehicles (size, volume, equipment)Different vehicles (size, volume, equipment) Depot with automatic sorting / registrationDepot with automatic sorting / registration

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CASE : TOLLPOST-GLOBECASE : TOLLPOST-GLOBE

Electronic road and address data are Electronic road and address data are available via the GIS Transportation available via the GIS Transportation DemonstratorDemonstrator

Mobile communication installed in 15 Mobile communication installed in 15 vehiclesvehicles

GPS installed in 5 vehiclesGPS installed in 5 vehicles some 100.000 customers in the Oslo some 100.000 customers in the Oslo

regionregion

goal: dynamic fleet management systemgoal: dynamic fleet management system

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The Pirelli (Cables) CaseThe Pirelli (Cables) Case

Logistics network simulatorLogistics network simulator Assessment of logistical performanceAssessment of logistical performance Detailed analysis of alternative structural Detailed analysis of alternative structural

changeschanges scenarios 6 months operation, 10.000 ordersscenarios 6 months operation, 10.000 orders

GreenTrip - GGT GreenTrip - GGT Systems ArchitectureSystems Architecture

GISGISRoad dataRoad data

Application ServerApplication Server

Application-Application-ModellingModelling

ApplicationApplicationModelModel

Legacy Legacy SystemsSystems

VRP Solver

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The VRP Solver - ObjectsThe VRP Solver - Objects

PlansPlans LocationsLocations VisitsVisits VehiclesVehicles RoutesRoutes DimensionsDimensions ConstraintsConstraints

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VRP Solver - AlgorithmsVRP Solver - Algorithms

ConstructionConstruction– SavingsSavings– SweepSweep– Nearest ...Nearest ...

Improvement, move Improvement, move operatorsoperators– 2-opt, Or-opt2-opt, Or-opt– RelocateRelocate– ExchangeExchange– CrossCross

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VRP Solver - Search ControlVRP Solver - Search Control

Basic heuristicBasic heuristic– Greedy Search (First Improvement)Greedy Search (First Improvement)– Steepest Descent (Best Steepest Descent (Best

Improvement)Improvement) Meta-heuristicsMeta-heuristics

– Tabu SearchTabu Search– Guided Local SearchGuided Local Search– Guided Tabu SearchGuided Tabu Search

41

GreenTrip - ResultsGreenTrip - Results

VRP Solver -> ILOG DispatcherVRP Solver -> ILOG Dispatcher GGT -> GreenTrip AS “Dynamic planner”GGT -> GreenTrip AS “Dynamic planner” ““best-until-now” results on OR best-until-now” results on OR

benchmarksbenchmarks Industrial Test CasesIndustrial Test Cases PublicationsPublications

– some 20 scientific paperssome 20 scientific papers– reports - “VRP Solving and IIT Survey”reports - “VRP Solving and IIT Survey”

42

GreenTrip DisseminationGreenTrip Dissemination

Kilby, Prosser, Shaw: “Guided Local Search for the VRP”, Proc. MIC 97Kilby, Prosser, Shaw: “Guided Local Search for the VRP”, Proc. MIC 97 De Backer, Furnon: “Metaheuristics in Constraint Programming: Experiments De Backer, Furnon: “Metaheuristics in Constraint Programming: Experiments

with Tabu Search on the VRP”, Proc. MIC 97with Tabu Search on the VRP”, Proc. MIC 97 De Backer, Furnon, Kilby, Prosser, Shaw: “Local Search in Constraint De Backer, Furnon, Kilby, Prosser, Shaw: “Local Search in Constraint

Programming: Applications to vehicle routing problems”, CP 97 Scheduling Programming: Applications to vehicle routing problems”, CP 97 Scheduling WorkshopWorkshop

Hasle: “GreenTrip - the Development of a Generic Toolkit for Vehicle Hasle: “GreenTrip - the Development of a Generic Toolkit for Vehicle Routing”, NOAS 97Routing”, NOAS 97

De Backer, Furnon: “Solving vehicle routing problems with Side Constraints De Backer, Furnon: “Solving vehicle routing problems with Side Constraints Using Constraint Programming”, INFORMS 97Using Constraint Programming”, INFORMS 97

De Backer, Furnon: “Modelling pickup and delivery problems in constraint De Backer, Furnon: “Modelling pickup and delivery problems in constraint programming”, INFORMS 98programming”, INFORMS 98

Bouzoubaa, Hasle, Kloster, Prosser: “The GGT: a Generic Toolkit for VRP Bouzoubaa, Hasle, Kloster, Prosser: “The GGT: a Generic Toolkit for VRP Applications and its Modelling Capabilities”, Proc. PACLP 99Applications and its Modelling Capabilities”, Proc. PACLP 99

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GreenTrip PapersGreenTrip Papers De Backer, Furnon, Kilby, Prosser, Shaw: “Solving vehicle routing De Backer, Furnon, Kilby, Prosser, Shaw: “Solving vehicle routing

problems with constraint programming and metaheuristics”, problems with constraint programming and metaheuristics”, Journal Journal of Heuristics, Special Issue on CPof Heuristics, Special Issue on CP

Kilby, Prosser, Shaw: “A comparison of traditional and constraint-Kilby, Prosser, Shaw: “A comparison of traditional and constraint-based heuristic methods on vehicle routing problems with side based heuristic methods on vehicle routing problems with side constraints”, constraints”, Constraints, April 98Constraints, April 98

De Backer, Furnon: “Local Search in Constraint Programming”, in De Backer, Furnon: “Local Search in Constraint Programming”, in META-HEURISTICS: Advances and Trends in Local Search Paradigms META-HEURISTICS: Advances and Trends in Local Search Paradigms for Optimization (Voss, Martello, Osman, Roucairol, 1999)for Optimization (Voss, Martello, Osman, Roucairol, 1999)

Kilby, Prosser, Shaw: “Guided Local Search for the Vehicle Routing Kilby, Prosser, Shaw: “Guided Local Search for the Vehicle Routing problem with Time Windows”, in META-HEURISTICS: Advances and problem with Time Windows”, in META-HEURISTICS: Advances and Trends in Local Search Paradigms for Optimization (Voss, Martello, Trends in Local Search Paradigms for Optimization (Voss, Martello, Osman, Roucairol, 1999)Osman, Roucairol, 1999)

Kilby, Prosser, Shaw: “Dynamic VRPs: A Study of Scenarios” Kilby, Prosser, Shaw: “Dynamic VRPs: A Study of Scenarios” (forthcoming)(forthcoming)

44

Vessel Routing - AmmoniaVessel Routing - Ammonia

Norsk Hydro AgriNorsk Hydro Agri Producer - Consumer Harbours (25)Producer - Consumer Harbours (25) Fleet (10)Fleet (10) Strong Inventory ConstraintsStrong Inventory Constraints External TradingExternal Trading Feasible solutionFeasible solution Earlier approach: MIPEarlier approach: MIP Approach taken: Heuristic Sequencing + Approach taken: Heuristic Sequencing +

LPLP

Producing harbours

Consumingharbours

Harbours with stock inventory

External orders(laycans)

Find the routing plan with the lowest cost so that inventory limits are not exceeded

and all external orders included.

QuantityTime-window

HAMMER Problem

Fleet of vessels

Combinatorial solution

Vessel View: Harbour View:

Site

Route for Vessel 1

Route for Vessel 2

H1:

H2:

H3:

H4:

Vessel 1

Vessel 2

Harbour View: Which vessels, and in which sequence, will call at each harbour.

Vessel View: Which harbours, and in which sequence, each vessel will visit.

H5:

H6:

H7:

1

2 3

4

5

6

7

Vessel view:

Harbour view:

HAMMER - Linear solution

Load

Time

Stock

Time

max

min

Call

HAMMER - System overview

Initial solver

Feasible solution

Iterativeimprover

Problem data

LP solver

Greedy Propagator

Combinatorial solution

Update

Feasibility check

HAMMER - Working with the HAMMER - Working with the systemsystem

Initialisation of the problemInitialisation of the problem– Harbours, ships, laycans and planning parametersHarbours, ships, laycans and planning parameters

Schedule generationSchedule generation– Initial solver - from scratch or existingInitial solver - from scratch or existing– Iterative improvementIterative improvement

Analysis and user interactionAnalysis and user interaction– plan statistics - slack, unservicedplan statistics - slack, unserviced– manually change planmanually change plan

Lock ship, harbour or time periodLock ship, harbour or time period

Flatberg, Haavardtun, Kloster, Løkketangen. (2000): Combining exact and Heuristic methods for solving a Vessel Routing Problem with inventory constraints and time windows. To appear in Ricerca Operativa, special issue on combined constraint programming and OR techniques

Research Agenda SAM: Research Agenda SAM: VRPVRP

construction heuristicsconstruction heuristics– construct and improveconstruct and improve– restartrestart– greedy + limited backtrackinggreedy + limited backtracking

IIT by local search and meta-heuristicsIIT by local search and meta-heuristics exact methods subproblems / limited exact methods subproblems / limited

problemsproblems hybrid methodshybrid methods dynamic VRPdynamic VRP empirical investigationempirical investigation

Important topics, SAMImportant topics, SAM

configuration of transportation networksconfiguration of transportation networks VRPs and TSPs with side constraints in VRPs and TSPs with side constraints in

road based and maritime transportationroad based and maritime transportation cheapest path problems in large, cheapest path problems in large,

dynamic network topologiesdynamic network topologies

Proposal to Research Council of NorwayProposal to Research Council of Norway

Research Agenda SAM: Research Agenda SAM: Optimisation / CSPOptimisation / CSP

over-constrained problemsover-constrained problems multi-criterion problemsmulti-criterion problems supply-chain coordinationsupply-chain coordination distributed problem solvingdistributed problem solving

Research Agenda: VRPResearch Agenda: VRP rich models, large problemsrich models, large problems dynamic VRPsdynamic VRPs exact methods for limited (sub)problemsexact methods for limited (sub)problems over-constrained problemsover-constrained problems multi-criteria problemsmulti-criteria problems methodology: problem type - algorithmmethodology: problem type - algorithm cooperating VRP solvers, hybrid methodscooperating VRP solvers, hybrid methods decompositiondecomposition

Issues in Dynamic Fleet Issues in Dynamic Fleet ManagementManagement

Talk atTalk at

ROUTE 2000 - INTERNATIONAL WORKSHOP ROUTE 2000 - INTERNATIONAL WORKSHOP ON VEHICLE ROUTINGON VEHICLE ROUTING

SKODSBORG, DENMARK - AUGUST 16-19, 2000SKODSBORG, DENMARK - AUGUST 16-19, 2000

Geir HasleGeir Hasle

Research Director, Department of Research Director, Department of OptimizationOptimization

SINTEF Applied MathematicsSINTEF Applied Mathematics

Oslo, NorwayOslo, [email protected]@math.sintef.no

http://www.oslo.sintef.no/am/http://www.oslo.sintef.no/am/