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1Anvendt matematikk
Transportation Planning in
Supply ChainsTalk at
SMARTLOG WorkshopTrondheim, June 9 2005
Dr. scient. Geir HasleChief Research Scientist, SINTEF Applied Mathematics
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Outline
MotivationTransportation planning tasksFocus: Vehicle RoutingDecision Support TechnologyChallenges
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Main messages
Transportation is an important part of the supply chainComplex decision problems at multiple levelsImprovement potential through better co-ordinationNeed for decision-support technologyTechnology implemented at an increasing rateStill many challengesMore research and technology development needed
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Efficient transportation is importantNorway: 16.900 companies (EU 1/2 million)Annual turnover 44 billion NOK (EU 1.200 billion)Transportation some 10-15 % of GNP in western countries”Lastebilundersøkelsen”, Statistics Norway (2002)
12,7 billion ton kilometers31,2 million trips - 18,1 million with loadTotal capacity utilization 46,7 %Capacity utilization with load: 63 %
Huge volumes, important to society and businessesSmall relative improvements may give huge effects
economyenvironmentcustomer service
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ChallengesLack of efficiency - uncoordinated, unnecessary driving Customer service, punctuality, short lead timesIncreased dynamics - need for increased reactivityToday: Manual planning predominates
complextime consumingfirst feasible solutionroute revision may take years, MNOK
Remediesstructural changesco-ordination
Potential for improvement: efficiency, customer service, reactivity Need for “coordination technology” – decision support systems
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Tasks in transportation management
(Re)configuration of transportation networkFleet dimensioningRoute design and fleet management, vehiclerouting
Allocation of order to vehicleSequencing of stops in a tour
Distance / time / cost from A to B?
ACME AS, Main rd 3, 3 sh a 15 kg, after 15:00
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Transportation ManagementStrategic, tactical, operative, dynamic (real-time) decisionsOften complex, several causes
Information availabilityUncertainty, dynamicsManagement policies, practical limitationsComputational complexity Response times
Often need for toolsDistribution network designFleet dimensioningRoute design, real-time routingInteraction human planners – system
Tools are implemented at an increasing rateAwareness
Challenges
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Tools in transport management- prerequisites for positive effects
Solve the right problemInformation availability, qualityUser acceptanceUser interfaceUser trainingOrganizational changesSW IntegrationUnderlying solution methods
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Mathematical formulation of optimal route design (with time windows, VRPTW)
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3) vehicle capacity constraint
8) time window at customer
minimize transport costs
each customer served
k tours out of depot
flow conservation at customer
k tours into depot
sequence, travel time
vehicle k travels from i to j
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VRP InstanceG-n262-k25
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VRP Solution – Routing PlanG-n262-k25: 5685 vs. 6119, 5767 CPU s
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Some problems are harder than others- computationally
Best route from A to B in a road network (NAF, Visveg, Michelin, ...)Shortest Path Computing time depends on size of networkPolynomial growth, computationally tractable problemChallenges: Information quality, very large road networksSpeed information, time-varying speeds, ...
Find the best sequence of multiple stops in a tour Traveling salesman problem, TSPComputing time depends on number of stopsComputing time grows ”exponentially”, computationally intractable problemDepends on solving many SPP
Design routing plan for vehicles and transportation problemsVehicle Routing Plan (VRP)Generalization of TSPComputing time grows ”exponentially”, computationally intractable problemDepends on solving many SPP
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Some Major Variants of the VRPPickups, deliveries, pickup and deliverySpit deliveries allowed?Single depot / multiple depotsFleet homogeneous or notFleet to be determined or notCapacity dimensionsTime windows
none, single, multiplehard or soft
Periodic ordersOrders on points or on arcsInventory routingTransport modality, type of goods
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Extensions in the VRP LiteratureLocation Routing LRPFleet Size and Mix FSMVRPVRP With Time Windows VRPTWGeneral Pickup and Delivery GPDPDial-A-Ride DARPPeriodic VRP PVRPInventory Routing IRPDynamic VRP DVRPCapacitated Arc Routing Problem CARP
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Real-life Applications need Rich ModelsTypes of Operation, Services
multiple depotsmix of pickup and deliveryorder splittingarc routing
Constraintscapacitytime windowsprecedencesincompatibilitiesdriving time restrictions
Objectivemultiple componentssoft constraints
Uncertainty, dynamicsExtensions in the literature address aspects
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State-of-the-art: Exact methods
Basic VRP with Capacity ConstraintsSolves instances up to 50 orders
VRP with time windows (VRPTW)Finding a feasible solution is computationally intractableSolves instances up to 100 orders
For most applications (and generic tools) we have to use approximation methods that cannot give strong guaranteesThe VRP has not yet been solved... and will not be solved in the foreseeable futuredevelopment of better VRP algorithms has industrial impact
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Goal: Good solutions, in due time
LC1_6_8
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0 100 200 300 400 500 600 700 800 900
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ApproachesConventional Approach in OR
Extensions studied in isolationTaxonomy of VRPsSuccessful
Increased UnderstandingHigh-performance, algorithmic approachesRobustness?
Complementary ApproachGeneric, rich modelUniform algorithmic approachRobustnessIndustrial impact
Cross-fertilization
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VRP etc. Research @ SINTEF
TRUTH GreenTrip
SPIDER
eCSPlain
SPIDER Pilot
Concordia
HAMMER “Jørgen”
NAM Scoop NAM SPOC
GGT/SPIDER
End Users
Tool Industries
Methodologies
QuantitativeLogistics
Optimization and CSP
I-Rute
TOP
SPIDER Web
Driving Restrictions
Fleet Dimensioning
DOiT
TOP
EDGE
1992 1994 1996 1998 2000 2002 2004 2006
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The SINTEF Generic VRP Solver - SPIDERDesigned to be widely applicableBased on generic, rich model
order typesvarious constraintscost componentscapacity dimensionsdriver regulations
Predictive route planningPlan repair, reactive planningRobust anytime algorithmsUniform algorithmic approachScalability
CommercializedFramework for VRP research
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Optimization approachHeuristic, does not guarantee optimal solutionsGood solutions in reasonable timeSame machinery for all problems
Generate initial solution with fast heuristicsimprove by local modificationsrestart
How good is this?Assessment through testing
Industrial problemsStandard benchmarks from scientific literature”World Champoinship in Vehicle Routing”
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Computational experiments PDPTWhttp://www.top.sintef.no/
354 standard test instances: 100 – 1.000 ordersJune 2003: SINTEF has produced best known solution on 273Today:
Author 100 200 400 600 800 1000 TotalLi & Lim 41 15 6 3 0 2 67SINTEF 13 12 5 4 9 37 80
BVH 2 8 8 3 4 13 38TS 0 1 2 0 0 6 9SR 0 24 39 50 47 - 160
Total 56 60 60 60 60 58 354
G-n262-k25: 5685 vs. 6119, 5767 CPU s
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M-n200-k16: First known feasible solution
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60 70
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Ongoing work at SINTEF
Stochastic and dynamic routing (DOiT project 2004-2007)including uncertainty in the modeldynamic revision
new ordersdelaysnew information on quantitiestraffic conditions
Huge scale routing problems (EDGE project 2005-2008)integration transportation control tech / routing techefficient acquisition of basic informationplan managementVRP resolution
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Innovation project partly financed by Research Council of NorwayTotale kostnader ~16 MNOK2004 – 2007
DOiT
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EDGE – Project OrganizationRCN
Division of Innovation/PULS
SPIDER
Solutions
(a.k.a. GreenTrip)
SINTEF
Distribution Innovation
EDGE Consortium
Ecomond Oy
AftenpostenJätekukko Oy
Agora Innoroad LabSteering
Committee
User Reference
Group
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Main messagesTransportation is an important part of the supply chainComplex decision problems at multiple levelsImprovement potential through better co-ordinationNeed for decision-support technologyTechnology implemented at an increasing rate – with successStill many challenges
stochastic and dynamic modelshuge-scale instancesintegrated models
Calls for collaboration in the RTD supply chain academiaapplied researchtool vendorsindustry
Road may be short from basic research to industrial gains
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Transportation Planning in
Supply ChainsTalk at
SMARTLOG WorkshopTrondheim, June 9 2005
Dr. scient. Geir HasleChief Research Scientist, SINTEF Applied Mathematics
