Benefits of in-vehicle consolidation in less than truckload freight transportation operations

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Benefits of in-vehicle consolidation in less than truckload freight transportation operations. Rodrigo Mesa- Arango Satish Ukkusuri 20 th International Symposium of Transportation and Traffic Theory Noorwijk , Netherlands July 2013. Outline. Introduction Problem Methodology - PowerPoint PPT Presentation

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Benefits of in-vehicle consolidation in less than truckload freight transportation operationsRodrigo Mesa-ArangoSatish Ukkusuri20th International Symposium of Transportation and Traffic TheoryNoorwijk, NetherlandsJuly 2013* / 28OutlineIntroductionProblemMethodologyNumerical ResultsConclusionQuestions/Comments* / 281. IntroductionTrucking: Important economic sector (1)US GDP: $ 14,499 billion dollarsFor hire transportation: $ 403 billion dollarsTrucking:$ 116 billion dollarsAir:$ 63 billion dollarsRail:$ 15 billion dollarsExternalities- Emissions- Safety- Congestion- Asset deteriorationMitigation: Increasing vehicle utilization(2)(3)(4)(5)(1) U.S. Department of Transportation (2012). National transportation statistics(2) Sathaye, et al, The Environmental Impacts of Logistics Systems and Options for Mitigation, 2006(3) Organisation for Economic Co-Operation and Development. Delivering the Goods-21st Century Challenges to Urban Goods Transport. 2003.(4) European Commission, Directorate-General for Energy and Transport. Urban Freight Transport and Logistics. European Communities. 2006.(5) Transport for London. London Freight Plan Sustainable Freight Distribution: A Plan for London. 2007.* / 281. IntroductionEconomic mechanism attractive for consolidation?Combinatorial AuctionsSuccessful implementations (6)(7)(8)(9)(10):(6) Elmaghraby, and Keskinocak. Combinatorial Auctions in Procurement. 2002.(7) De Vries, and Vohra. Combinatorial Auctions: A Survey. 2003.(8) Moore, et al. The Indispensable Role of Management Science in Centralizing Freight Operations at Reynolds Metals Company. 1991(9) Porter, et al. The First Use of a Combined-Value Auction for Transportation Services. 2002.(10) Sheffi, Y. Combinatorial Auctions in the Procurement of Transportation Services. 2004.- Home Depot Inc.- Staples Inc.- Wal-Mart Stores Inc.- Reynolds Metal Company- K-Mart Corporation- Ford Motor company- The Limited- Compaq Computer Corporation - Sears Logistics Services* / 28Combinatorial Auctions in Freight Transportation(11) Caplice and Sheffi. Combinatorial Auctions for Truckload Transportation. 2006.(12) Sandholm. Algorithm for Optimal Winner Determination in Combinatorial Auctions. 2002(13) Abrache, et al. Combinatorial auctions. Annals of Operations Research. 2007(14) Ma, et al. A Stochastic Programming Winner Determination Model for Truckload Procurement Under Shipment Uncertainty. 20101. Introduction* / 28Bidding advisory modelsTruckload (TL) operations(15)(16)(17)(18)(19) Direct movementsEconomies of scope(20)(21)(22)(23) Less-Than-Truckload (LTL) operations?Consolidated movementsEconomies of scope, scale, density(20)(21)(22)(23) 1. Introduction(15) Song, and Regan. Combinatorial Auctions for Transportation Service Procurement, The Carrier Perspective. 2003,(16) Song, and Regan. Approximation Algorithms for the Bid Construction Problem in Combinatorial Auctions for the Procurement of Freight Transportation Contracts. 2005,(17) Wang, and Xia. Combinatorial Bid Generation Problem for Transportation Service Procurement. 2005(18) Lee, et al. A Carriers Optimal Bid Generation Problem in Combinatorial Auctions for Transportation Procurement. 2007(19) Chang. Decision Support for Truckload Carriers in One-Shot Combinatorial Auctions. 2009(20) Caplice, and Sheffi. Combinatorial Auctions for Truckload Transportation. 2006(21) Caplice. An Optimization Based Bidding Process: A New Framework for Shipper-Carrier Relationship. 1996(22) Jara-Diaz. Transportation Cost Functions: A Multiproducts Approach. 1981(23) Jara-Diaz. Freight Transportation Multioutput Analysis. 1983* / 281. IntroductionRoutes, costs and prices* / 281. IntroductionEconomies of scope [TL]1234* / 281. IntroductionEconomies of consolidation (scale and density) [LTL]* / 28This researchShow Benefits for carriesIn-vehicle consolidationBidding constructionFreight Transportation combinatorial auctionsUseMulti-commodity one-to-one pick up and delivery vehicle routing problem (m-PDVRP) to find optimal LTL bundles.Compare against optimal bundles obtained for TL carriers1. Introduction* / 28Objective fun: Minimize total traversing costEach node visited onceAll vehicles are usedVehicle flow conservationSub-tour eliminationMIP Formulation for m-PDVRP (1/2)Binary variables2. Problem* / 28Objective fun: Minimize total traversing costMIP Formulation for m-PDVRP (2/2)Demand Satisfaction constraint (Deliveries)Demand Satisfaction constraint (Pickups)Payload flow conservationVehicles leave the depot empty and return emptyLoads only on traversed links without exceeding vehicle capacityNon-negative continuous variables2. Problem* / 283. MethodologyBranch-and-price(24)(25)Branch-and-boundDantzig-Wolfe and Column generationMaster ProblemSub - problem(24) Barnhart, et al. 1998. Branch-and-price: Column generation for solving huge integer programs.(25) Desaulniers, et al. 1998. A unified framework for deterministic time constrained vehicle routing and crew scheduling problems.* / 283.1 Branch-and-boundBranch-and-BoundSolve linear relaxation of IPTerminate (fathom) a nodeInfeasibility \ Bound \ SolutionBranchStop when all nodes are terminatedIPLinear Relaxation* / 283.2. Dantzig Wolfe dec. + col. gen.MIP has special structure appropriate for decompositionMaster Problem (MP)Linear ProgramControls column generation processRequests columns from the Sub problemInteger variables are represented as convex combination of the columns generated by the Sub problemSub ProblemInteger programGenerates columnsSet of integer variables with common structure* / 283.2. Dantzig Wolfe dec. + col. gen.Each node visited onceAll vehicles are usedVehicle flow conservationSub-tour eliminationBinary variablesVRP deployment (t)Convex combination* / 283.2. Dantzig Wolfe dec. + col. gen.|V| = 1|V| = 2|V| = 3Examples of deployments of trucks* / 28Objective fun: Minimize total traversing costDemand Satisfaction constraint (Deliveries)Demand Satisfaction constraint (Pickups)Payload flow conservationVehicles leave the depot empty and return emptyLoads only on traversed links without exceeding vehicle capacityNon-negative continuous variables3.2. Dantzig Wolfe dec. + col. gen.Vehicles leave the depot empty and return emptyMaster problem (MP): Generates lt as needed(MP)Non-negativityConvexity Constraint* / 283.2. Dantzig Wolfe dec. + col. gen.Objective fun: Minimize reduced costEach node visited onceAll vehicles are usedVehicle flow conservationSub-tour eliminationBinary variablesEach Solution generates a column t, {x0j0,,xi0v}, that is associated with a variable lt in the MP(Sub-P)* / 283.2. Branch-and-priceSolve MPAdd new column to poolSolve Sub-PSet Sub-P costsUpdate arcs and costsNoRoot B&B Node (Active)Column GenerationYesSelect B&B node and set as inactiveTerminate node by infeasibilityTerminate node by solutionTerminate node by boundBranchActive nodes?NoStopSet node as inactiveUpdate incumbent solutionColumn GenerationColumn GenerationB&B node (active)B&B node (active)* / 283.3. Acceleration strategiesOriginally depth-first searchFinding initial incumbent solution (upper bound): Time consumingStrategy 2: Continuous increment to lower boundStrategy 1 replace Step 3 as followsFind branch-and-bound node with current lowest solution and fathom it, repeatStrategy 1: Fast initial upper bound* / 284. Numerical ResultsImplementationJavaBranch-and-BoundInteractions in Column GenerationSet Sub-PUpdate MPNetwork ManagementInformation/Updates: Nodes, Links, ToursILOG CPLEXMP LP SolutionSub-P IP Solution* / 284. Numerical Resultscij012345678099.03.07.05.01.01.07.05.03.013.099.03.07.05.01.05.01.07.027.03.099.03.07.05.01.01.05.035.07.03.099.03.07.01.05.01.041.05.07.03.099.03.05.07.01.051.01.05.07.03.099.07.03.05.067.05.01.01.05.07.099.03.03.075.01.01.05.07.03.03.099.07.083.07.05.01.01.05.03.07.099.0* / 284. Numerical Results (LTL)* / 284. Numerical ResultsTL + Scenario 3ComparisonOpt.forBundleNo.lanesLTL operationTL operationLTLminmarginDeploymentTotalcostCost perlaneDeploymentTotalcostCost perlaneLTL{(1,3),(5,6),(7,8)}30-5-1-7-6-3-8-011.003.670-5-6-1-3-7-8-035.0011.6724.01LTL{(1,3),(5,6)}20-5-1-6-3-013.006.500-5-6-1-3-025.0012.5012TL{(1,3),(2,4)}20-1-2-3-4-013.006.500-1-3-2-4-021.0010.508TL{(5,6),(7,8)}20-5-7-6-8-013.006.500-5-6-7-8-021.0010.508TL{(5,6),(2,4)}20-5-2-6-4-013.006.500-5-6-2-4-017.008.504TL / LTL{(1,3),(2,4),(5,6),(7,8)}40-5-1-7-6-2-3-8-4-013.003.250-1-3-2-4-5-6-7-8-043.0010.7530TL / LTL{(1,3)}10-1-3-015.0015.000-1-3-015.0015.000TL / LTL{(2,4)}10-2-4-015.0015.000-2-4-015.0015.000TL / LTL{(5,6)}10-5-6-015.0015.000-5-6-015.0015.000TL / LTL{(7,8)}10-7-8-015.0015.000-7-8-015.0015.000* / 285. ConclusionResearch shows benefits of considering in-vehicle consolidation (LTL) in the construction of bidsNumerical results show that consolidated bids (LTL) dominate non-consolidated ones (TL)LTL carriers can submit bids with prices that are less than or equal to the costs of TL carriersSavings increase as the capacity of trucks increasesLow transportation costs potentially reduce shipper procurement costIn-vehicle consolidation (as defined in this research) integrates the flexibility of TL (economies of scope) to the economies of scales/density of LTL* / 285. ConclusionFuture researchUnderstanding the tradeoff between low price and delivery times (as well as other attributes of the carrier) for shippersEconometric techniquesSegmented pricing policiesAcceleration of the solution methodologyParallel computingHybrid-metaheuristicsConsideration of stochastic demandDevelopment of a robust biding advisory model that incorporates these features.Analysis of positive/negative externalities associated to large trucks at a macroscopic levelThank you!* / 286. Questions - Comments**

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