Issues in Dynamic Fleet Issues in Dynamic Fleet Management Management Talk at Talk at ROUTE 2000 - INTERNATIONAL WORKSHOP ROUTE 2000 - INTERNATIONAL WORKSHOP ON VEHICLE ROUTING ON VEHICLE ROUTING SKODSBORG, DENMARK - AUGUST 16-19, 2000 SKODSBORG, DENMARK - AUGUST 16-19, 2000 Geir Hasle Geir Hasle Research Director, Department of Research Director, Department of Optimization Optimization SINTEF Applied Mathematics SINTEF Applied Mathematics Oslo, Norway Oslo, Norway [email protected][email protected]http://www.oslo.sintef.no/am/ http://www.oslo.sintef.no/am/
Issues in Dynamic Fleet Management. Talk at ROUTE 2000 - INTERNATIONAL WORKSHOP ON VEHICLE ROUTING SKODSBORG, DENMARK - AUGUST 16-19, 2000 Geir Hasle Research Director, Department of Optimization SINTEF Applied Mathematics Oslo, Norway [email protected] - PowerPoint PPT Presentation
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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
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
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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
(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
AdaptabilityAdaptability Power: speed vs. qualityPower: speed vs. quality Large-size problemsLarge-size problems User InterfaceUser Interface IntegrationIntegration Support etc.Support etc.
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
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.”
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)
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?
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
OO ProgrammingOO Programming Constraint ProgrammingConstraint Programming Iterative Improvement TechniquesIterative Improvement Techniques Applications ModellingApplications Modelling Automated Systems Automated Systems
(Re)Configuration (Re)Configuration
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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
– Tabu SearchTabu Search– Guided Local SearchGuided Local Search– Guided Tabu SearchGuided Tabu Search
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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”
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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
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)
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
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,