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Issues in Dynamic Fleet Management

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 Geir.Hasle@math.sintef.no

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Issues in Dynamic Fleet Management

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  1. 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 Geir.Hasle@math.sintef.no http://www.oslo.sintef.no/am/

  2. My talk • SINTEF Applied Mathematics (SAM) • Fleet Management • industrial potential, status, requirements • technology • research, science • bridging the gap between science and industry • Challenges • Routing etc. at SAM • Research Agenda

  3. SINTEF The Foundation for Scientific and Industrial Research at the Norwegian Institute of Technology The vision: Technology for a better society 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 Locations: The SINTEF Group have 1800 employees, 400 in Oslo and 1400 in Trondheim.

  4. SINTEFs council SINTEFs board President/Vice-president SINTEF Petroleum Research SINTEF Applied Mathematics SINTEF Civil and Environmental Engineering MARINTEK The Norwegian Marine Technology Research Institute SINTEF Electronics and Cybernetics SINTEF Applied Chemistry SINTEF Energy Research SINTEF Materials Technology SINTEF Fisheries and Aquaculture SINTEF Industrial Management SINTEF Telecom and Informatics SINTEF Unimed

  5. SINTEF-Group turnover in 1999 Basic grants from The Research Council of Norway 3,3% 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%

  6. SINTEF Applied Mathematicshttp://www.oslo.sintef.no/am Geometry A contract research institute in the SINTEF group Modeling Simulation Optimisation

  7. SINTEF Applied Mathematics Department of Optimisation Focus: Applied research Planning Decision Support Application types: Resource optimisation Design/configuration Discrete Basis: Knowledge Based Systems Operations Research Computing science Main business areas: Transportation Area management Oil business Manufacturing Approach: Generic Tools Reuse Methodology

  8. Transportation of goods in Norwayand EU • 12.000 companies (EU 1/2 mill.) • Annual turnover 30 billion. (EU 1.200 billion.) • Many SMEs • 36 % empty driving • Capacity utilization with load: 60 % • Logistics costs 12% of product costs (EU 7%) • EU: 13 million trucks, 800 billion ton-kilometers (1990) • Germany: freight income some 60 billion DM (1990)

  9. Industrial use of VRP Tools • Excess travel, huge potential • Swedish report* 1999 (commercial road transport) • large end users, food & beverage • generation of static routes • vendors claim operational tools • very high potential for savings * A. Henriksson, P. Liljevik: ”Dynamisk ruttplanlegging i verkligheten” Minirapport MR 123, TFK - Institutet för transportforskning, Stockholm October 1999

  10. Increasing need for VRP Tools • focus on • time • cost • utilization • customer service • lead time reduction • reactivity • regulations, environmental concerns • e-commerce, home shopping

  11. Reasons for mismatch • lack of awareness in industry • lack of data and infrastructure • price (SMEs) • organizational problems, resistance • practical constraints • information availability • physical movement • tools not good enough • functionality, modelling power • user friendliness • integration • logistical performance

  12. Existing tools - keywords • Large variety: simple TSP - sophisticated VRP solvers • focus: road transportation of goods, local distribution • built for operative planning, used for generation of static routes • packages • primitive integration, but good import facilities • inflexible and simple or heavy on consultancy • Windows-platform • good user interfaces, map visualization, manual changes • VRP algorithms? • real-time planning? • multiple users? • continuous optimization? • priced at USD 40.000 and above (high end)

  13. Some Vendors • Descartes Systems USA • Caps Logistics -> Baan USA • MicroAnalytics USA/GB • Roadnet Technologies (UPS) USA • i2 USA • ESRI USA • Kositzky and Associates USA • Manugistics USA • Carrier Logistics Inc USA • Insight Inc. USA/The Netherlands/GB • Caliper Corporation USA • Trapeze Software Group USA/Canada • Giro Canada • DPS International UK • Paragon Software Systems UK • Optrak (Andersen Consulting) UK • Ilog F • Diagma F • PTV D • Alfaplan D • PLS D • Prologos D Typically claim 10 - 30% cost reductions - static routes

  14. Few VRP Tools in Operation in Norway • Coca-Cola • Taxi companies • Falken • NAS • NKL • Tollpost-Globe • Linjegods • Postal service • Hydro Agri

  15. Challenges - VRP Tools • Functionality • Modelling • constraints • criteria • uncertainty • dynamics • supply-chain coordination • Adaptability • Power: speed vs. quality • Large-size problems • User Interface • Integration • Support etc.

  16. Dynamic, real-time routing - Success stories? • Paragon - Tesco • “… Home shoppers simply log onto the dedicated area of Tesco's website, select their purchases and identify a two hour time window for delivery to an address of their choosing” ... • Truckstops • “… In some UK applications it is even used to recalculate routes during the day, modifying its original calculations to take account of new requirements and reflecting data transmitted back from vehicles by radio” … • PriceWaterhouseCoopers

  17. Goal - VRP Technology • real benefits for industry - logistical performance • solve right problem • plan quality vs. response time • user interaction, user-friendliness • configurability • reactivity • price

  18. Future VRP technology • GIS vendors • ERP vendors • ASP solutions, thin clients, Internet, www • better tools for strategic/tactical planning • supply-chain coordination, integrated solutions • dynamic, real time fleet management

  19. Dynamic Fleet Management - Prerequisites • ICT infrastructure • order data • fleet data • access to high quality traffic data • speed predictions • “organic” electronic road network • Better understanding of routing policies • Better VRP algorithms

  20. Issues in VRP research • Large, ill-structured problems • rich models • uncertainty • dynamics • multiple criteria • reactivity • disruption? • slack • policies • plan quality vs. response time performance • decomposition • human issues

  21. Stochastic and dynamic VRPs • what does “dynamic” mean? • problem changes dynamically • Psaraftis (1995): “... information on the problem is made known to the decision maker or is updated concurrently with the determination of the set of routes.” • Baita, Ukovich, Pesenti, Favaretto (1998): “... releated decisions have to be taken at different times within some time horizon, and earlier decisions influence later decisions.” • a.k.a. “real-time”, “on-line” • “organic” routing plans • challenges • information flow • physical goods • good idea? (talk of Carlos Daganzo)

  22. Literature - dynamic VRPs • 6 INFORMS sessions since 1995, some 20 papers • some 50 journal papers

  23. Some papers Psaraftis (1995): Dynamic vehicle routing: Status and prospects Bertsimas, DJ / SimchiLevi, D (1996): A new generation of vehicle routing research: Robust algorithms, addressing uncertainty Crainic, TG / Laporte, G (1997): Planning models for freight transportation Baita, 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 problem Savelsbergh, 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 constraints Gans, 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 problem Gendreau, 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 problems in the presence of user noncompliance Cheung, RK / Muralidharan, B (2000): Dynamic routing for priority shipments in LTL service networks Gendreau, 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 customers Haughton, 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 restocking Haughton, 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 demands Shieh, HM / May, MD (1998): On-line vehicle routing with time windows - Optimization-based heuristics approach for freight demands requested in real-time Kilby / Prosser / Shaw: Dynamic VRPs: A Study of Scenarios (forthcoming)

  24. Approaches - uncertainty, dynamics • ignore • deterministic model - and repair • crisp, optimized plans are brittle • is disruption costly? • add slack, how? • stochastic model • investigation of policies • still need dynamic decision-making • lessons to be learnt from factory scheduling

  25. Dynamic VRP DSS • dependent on high quality updated information • fleet status • order status • “organic” route planning • concept of current plan • when do we commit? • when do we include changes? • locking parts of plan • do we need to worry about disruption? • dependence on type of operation / business rules • delivery vs. pickup • applicable algorithms • (how much) do we save by taking a dynamic approach?

  26. Approaches • insertion heuristics + iterative improvement • constraint propagation • MP formulations? • Minimal disruption possibly an additional goal criterion component

  27. Routing at SAM • SPIDER • GreenTrip • HAMMER - vessel routing with inventory constraints • Bus scheduling • eCSPlain, EU FP V • Distributed problem solving • Proposals

  28. SPIDER • a VRP Solver C++ program library • UNIX • Windows • COM component • instantiates to a module for optimised transport management • plan-administrasjon • VRP optimisation • cheapest path calculations • adaptable to wide variety of applications • distribution through sw vendors

  29. GreenTrip • Esprit 20603, January 1996-March 1999, > 40 person-years • Consortium • Tollpost-Globe (N) • Pirelli (I) • Ilog (F) • University of Strathclyde (GB) • SINTEF (N) • RTD effort in methods, algorithms, and generic sw for optimised fleet management

  30. The goal of GreenTrip • Produce a cost-effective tool to optimise routing of vehicles that • is generic • takes into account multiple business constraints • permits efficient (re)configuration • integrates easily in existing IT infrastructure

  31. GreenTrip Technical Approach • OO Programming • Constraint Programming • Iterative Improvement Techniques • Applications Modelling • Automated Systems (Re)Configuration

  32. Tollpost- Globe Pirelli SINTEF UoS ILOG The GreenTrip Consortium

  33. CASE : TOLLPOST-GLOBE • Pick up orders : 600 • Regular and non-regular customers • Deliveries : 2.400 • Time windows - Customer service • Two days are not the same • some 100 vehicles • Different vehicles (size, volume, equipment) • Depot with automatic sorting / registration

  34. CASE : TOLLPOST-GLOBE • Electronic road and address data are available via the GIS Transportation Demonstrator • Mobile communication installed in 15 vehicles • GPS installed in 5 vehicles • some 100.000 customers in the Oslo region • goal: dynamic fleet management system

  35. The Pirelli (Cables) Case • Logistics network simulator • Assessment of logistical performance • Detailed analysis of alternative structural changes • scenarios 6 months operation, 10.000 orders

  36. Application- Modelling Application Model Legacy Systems GIS Road data GreenTrip - GGT Systems Architecture Application Server VRP Solver

  37. The VRP Solver - Objects • Plans • Locations • Visits • Vehicles • Routes • Dimensions • Constraints

  38. VRP Solver - Algorithms • Construction • Savings • Sweep • Nearest ... • Improvement, move operators • 2-opt, Or-opt • Relocate • Exchange • Cross

  39. VRP Solver - Search Control • Basic heuristic • Greedy Search (First Improvement) • Steepest Descent (Best Improvement) • Meta-heuristics • Tabu Search • Guided Local Search • Guided Tabu Search

  40. GreenTrip - Results • VRP Solver -> ILOG Dispatcher • GGT -> GreenTrip AS “Dynamic planner” • “best-until-now” results on OR benchmarks • Industrial Test Cases • Publications • some 20 scientific papers • reports - “VRP Solving and IIT Survey”

  41. GreenTrip Dissemination • Kilby, Prosser, Shaw: “Guided Local Search for the VRP”, Proc. MIC 97 • De Backer, Furnon: “Metaheuristics in Constraint Programming: Experiments with Tabu Search on the VRP”, Proc. MIC 97 • De Backer, Furnon, Kilby, Prosser, Shaw: “Local Search in Constraint Programming: Applications to vehicle routing problems”, CP 97 Scheduling Workshop • Hasle: “GreenTrip - the Development of a Generic Toolkit for Vehicle Routing”, NOAS 97 • De Backer, Furnon: “Solving vehicle routing problems with Side Constraints Using Constraint Programming”, INFORMS 97 • De Backer, Furnon: “Modelling pickup and delivery problems in constraint programming”, INFORMS 98 • Bouzoubaa, Hasle, Kloster, Prosser: “The GGT: a Generic Toolkit for VRP Applications and its Modelling Capabilities”, Proc. PACLP 99

  42. GreenTrip Papers • De Backer, Furnon, Kilby, Prosser, Shaw: “Solving vehicle routing problems with constraint programming and metaheuristics”, Journal of Heuristics, Special Issue on CP • Kilby, Prosser, Shaw: “A comparison of traditional and constraint-based heuristic methods on vehicle routing problems with side constraints”, Constraints, April 98 • De Backer, Furnon: “Local Search in Constraint Programming”, in META-HEURISTICS: Advances and Trends in Local Search Paradigms for Optimization (Voss, Martello, Osman, Roucairol, 1999) • Kilby, Prosser, Shaw: “Guided Local Search for the Vehicle Routing problem with Time Windows”, in META-HEURISTICS: Advances and Trends in Local Search Paradigms for Optimization (Voss, Martello, Osman, Roucairol, 1999) • Kilby, Prosser, Shaw: “Dynamic VRPs: A Study of Scenarios” (forthcoming)

  43. Vessel Routing - Ammonia • Norsk Hydro Agri • Producer - Consumer Harbours (25) • Fleet (10) • Strong Inventory Constraints • External Trading • Feasible solution • Earlier approach: MIP • Approach taken: Heuristic Sequencing + LP

  44. HAMMER Problem Producing harbours Quantity Time-window Consuming harbours Fleet of vessels External orders (laycans) Harbours with stock inventory Find the routing plan with the lowest cost so that inventory limits are not exceeded and all external orders included.

  45. H4: Combinatorial solution Vessel View: Harbour View: 3 2 H1: 6 H2: 1 4 H3: 7 H5: H6: H7: 5 Site Route for Vessel 1 Vessel 1 Route for Vessel 2 Vessel 2 Vessel View: Which harbours, and in which sequence, each vessel will visit. Harbour View: Which vessels, and in which sequence, will call at each harbour.

  46. HAMMER - Linear solution Vessel view: Harbour view: max Load min Time Call Stock Time

  47. HAMMER - System overview Problem data Initial solver Iterative improver Combinatorial solution Feasibility check Greedy Propagator Feasible solution Update LP solver

  48. HAMMER - Working with the system • Initialisation of the problem • Harbours, ships, laycans and planning parameters • Schedule generation • Initial solver - from scratch or existing • Iterative improvement • Analysis and user interaction • plan statistics - slack, unserviced • manually change plan • Lock 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

  49. Research Agenda SAM: VRP • construction heuristics • construct and improve • restart • greedy + limited backtracking • IIT by local search and meta-heuristics • exact methods subproblems / limited problems • hybrid methods • dynamic VRP • empirical investigation

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