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LEADER: 12266cam 22006494a 4500
001 ocm47126854
003 OCoLC
005 20211116234636.0
008 010607s2002 paua b 001 0 eng
010 $a 2001042043
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035 $a(OCoLC)47126854
042 $apcc
050 00 $aQA402.6$b.V44 2002
082 00 $a388.3/1/0285$221
084 $a31.12$2bcl
084 $a55.20$2bcl
245 04 $aThe vehicle routing problem /$cedited by Paolo Toth, Daniele Vigo.
260 $aPhiladelphia :$bSociety for Industrial and Applied Mathematics,$c©2002.
300 $axviii, 367 pages :$billustrations ;$c27 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
490 1 $aSIAM monographs on discrete mathematics and applications
504 $aIncludes bibliographical references and index.
505 00 $g1$tAn Overview of Vehicle Routing Problems /$rP. Toth, D. Vigo$g1 --$g1.2$tProblem Definition and Basic Notation$g5 --$g1.2.1$tCapacitated and Distance-Constrained VRP$g5 --$g1.2.2$tVRP with Time Windows$g8 --$g1.2.3$tVRP with Backhauls$g9 --$g1.2.4$tVRP with Pickup and Delivery$g10 --$g1.3$tBasic Models for the VRP$g11 --$g1.3.1$tVehicle Flow Models$g11 --$g1.3.2$tExtensions of Vehicle Flow Models$g17 --$g1.3.3$tCommodity Flow Models$g19 --$g1.3.4$tSet-Partitioning Models$g21 --$g1.4$tTest Instances for the CVRP and Other VRPs$g22 --$gI$tCapacitated Vehicle Routing Problem$g27 --$g2$tBranch-and-Bound Algorithms for the Capacitated VRP /$rP. Toth, D. Vigo$g29 --$g2.2$tBasic Relaxations$g30 --$g2.2.1$tBounds Based on Assignment and Matching$g30 --$g2.2.2$tBounds Based on Arborescences and Trees$g32 --$g2.2.3$tComparison of the Basic Relaxations$g33 --$g2.3$tBetter Relaxations$g35 --$g2.3.1$tAdditive Bounds for ACVRP$g35 --$g2.3.2$tFurther Lower Bounds for ACVRP$g39 --$g2.3.3$tLagrangian Lower Bounds for SCVRP$g40 --$g2.3.4$tLower Bounds from a Set-Partitiong Formulation$g41 --$g2.3.5$tComparison of the Improved Lower Bounds$g42 --$g2.4$tStructure of the Branch-and-Bound Algorithms for CVRP$g44 --$g2.4.1$tBranching Schemes and Search Strategies$g44 --$g2.4.2$tReduction, Dominance Rules, and Other Features$g46 --$g2.4.3$tPerformance of the Branch-and-Bound Algorithms$g47 --$g2.5$tDistance-Constrained VRP$g48 --$g3$tBranch-and-Cut Algorithms for the Capacitated VRP /$rD. Naddef, G. Rinaldi$g53 --$g3.1$tIntroduction and Two-Index Flow Model$g53 --$g3.2$tBranch-and-Cut$g55 --$g3.3$tPolyhedral Studies$g58 --$g3.3.1$tCapacity Constraints$g59 --$g3.3.2$tGeneralized Capacity Constraints$g61 --$g3.3.3$tFramed Capacity Constraints$g62 --$g3.3.4$tValid Inequalities from STSP$g62 --$g3.3.5$tValid Inequalities Combining Bin Packing and STSP$g67 --$g3.3.6$tValid Inequalities from the Stable Set Problem$g69 --$g3.4$tSeparation Procedures$g71 --$g3.4.1$tExact Separation of Capacity Constraints$g71 --$g3.4.2$tHeuristics for Capacity and Related Constraints$g72 --$g3.4.3$tSTSP Constraints$g75 --$g3.5$tBranching Strategies$g75 --$g3.6$tComputational Results$g78 --$g4$tSet-Covering-Based Algorithms for the Capacitated VRP /$rJ. Bramel, D. Simchi-Levi$g85 --$g4.2$tSolving the Linear Programming Relaxation of P$g87 --$g4.3$tSet-Covering-Based Solution Methods$g89 --$g4.3.1$tBranch-and-Bound Algorithm for Problem CG$g89 --$g4.3.2$tPolyhedral Branch-and-Bound Algorithm$g91 --$g4.3.3$tPseudo-Polynomial Lower Bound on cmin$g92 --$g4.3.4$tSolving P[subscript D] via Dual-Ascent and Branch-and-Bound$g94 --$g4.4$tSolving the Set-Covering Integer Program$g96 --$g4.4.1$tA Cutting Plane Method$g97 --$g4.4.2$tBranch-and-Price$g99 --$g4.4.3$tAdditional Comments on Computational Approaches$g100 --$g4.5$tComputational Results$g100 --$g4.6$tEffectiveness of the Set-Covering Formulation$g102 --$g4.6.1$tWorst-Case Analysis$g103 --$g4.6.2$tAverage-Case Analysis$g103 --$g5$tClassical Heuristics for the Capacitated VRP /$rG. Laporte, F. Semet$g109 --$g5.2$tConstructive Methods$g110 --$g5.2.1$tClarke and Wright Savings Algorithm$g110 --$g5.2.2$tEnhancements of the Clarke and Wright Algorithm$g111 --$g5.2.3$tMatching-Based Savings Algorithms$g113 --$g5.2.4$tSequential Insertion Heuristics$g114 --$g5.3$tTwo-Phase Methods$g116 --$g5.3.1$tElementary Clustering Methods$g116 --$g5.3.2$tTruncated Branch-and-Bound$g118 --$g5.3.3$tPetal Algorithms$g120 --$g5.3.4$tRoute-First, Cluster-Second Methods$g120 --$g5.4$tImprovement Heuristics$g121 --$g5.4.1$tSingle-Route Improvements$g121 --$g5.4.2$tMultiroute Improvements$g122 --$g6$tMetaheuristics for the Capacitated VRP /$rM. Gendreau, G. Laporte, J.-Y. Potvin$g129 --$g6.2$tSimulated Annealing$g130 --$g6.2.1$tTwo Early Simulated Annealing Algorithms$g130 --$g6.2.2$tOsman's Simulated Annealing Algorithms$g131 --$g6.2.3$tVan Breedam's Experiments$g133 --$g6.3$tDeterministic Annealing$g133 --$g6.4$tTabu Search$g134 --$g6.4.1$tTwo Early Tabu Search Algorithms$g134 --$g6.4.2$tOsman's Tabu Search Algorithm$g134 --$g6.4.3$tTaburoute$g135 --$g6.4.4$tTaillard's Algorithm$g137 --$g6.4.5$tXu and Kelly's Algorithm$g137 --$g6.4.6$tRego and Roucairol's Algorithms$g137 --$g6.4.7$tBarbarosoglu and Ozgur's Algorithm$g138 --$g6.4.8$tAdaptive Memory Procedure of Rochat and Taillard$g138 --$g6.4.9$tGranular Tabu Search of Toth and Vigo$g138 --$g6.4.10$tComputational Comparison$g140 --$g6.5$tGenetic Algorithms$g140 --$g6.5.1$tSimple Genetic Algorithm$g140 --$g6.5.2$tApplication to Sequencing Problems$g141 --$g6.5.3$tApplication to the VRP$g142 --$g6.6$tAnt Algorithms$g144 --$g6.7$tNeural Networks$g146 --$gII$tImportant Variants of the Vehicle Routing Problem$g155 --$g7$tVRP with Time Windows /$rJ.-F. Cordeau, G. Desaulniers, J. Desrosiers, M.M. Solomon, F. Soumis$g157 --$g7.2$tProblem Formulation$g158 --$g7.2.1$tFormulation$g158 --$g7.2.2$tNetwork Lower Bound$g159 --$g7.2.3$tLinear Programming Lower Bound$g159 --$g7.2.4$tAlgorithms$g160 --$g7.3$tUpper Bounds: Heuristic Approaches$g160 --$g7.3.1$tRoute Construction$g160 --$g7.3.2$tRoute Improvement$g161 --$g7.3.3$tComposite Heuristics$g161 --$g7.3.4$tMetaheuristics$g162 --$g7.3.5$tParallel Implementations$g165 --$g7.3.6$tOptimization-Based Heuristics$g165 --$g7.3.7$tAsymptotically Optimal Heuristics$g165 --$g7.4$tLower Bounds from Decomposition Approaches$g166 --$g7.4.1$tLagrangian Relaxation$g166 --$g7.4.2$tCapacity and Time-Constrained Shortest-Path Problem$g167 --$g7.4.3$tVariable Splitting$g168 --$g7.4.4$tColumn Generation$g169 --$g7.4.5$tSet-Partitioning Formulation$g169 --$g7.4.6$tLower Bound$g170 --$g7.4.7$tCommodity Aggregation$g171 --$g7.4.8$tHybrid Approach$g172 --$g7.5$tInteger Solutions$g173 --$g7.5.1$tBinary Decisions on Arc Flow Variables$g173 --$g7.5.2$tInteger Decisions on Arc Flow Variables$g173 --$g7.5.3$tBinary Decisions on Path Flow Variables$g174 --$g7.5.4$tSubtour Elimination and 2-Path Cuts$g175 --$g7.5.5$tk-Path Cuts and Parallelism$g176 --$g7.5.6$tInteger Decisions on (Fractional and Integer) Time Variables$g176 --$g7.6$tSpecial Cases$g177 --$g7.6.1$tMultiple TSP with Time Windows$g177 --$g7.6.2$tVRP with Backhauls and Time Windows$g177 --$g7.7$tExtensions$g178 --$g7.7.1$tHeterogeneous Fleet, Multiple-Depot, and Initial Conditions$g178 --$g7.7.2$tFleet Size$g179 --$g7.7.3$tMultiple Time Windows$g179 --$g7.7.4$tSoft Time Windows$g179 --$g7.7.5$tTime- and Load-Dependent Costs$g180 --$g7.7.6$tDriver Considerations$g180 --$g7.8$tComputational Results for VRPTW$g181 --$g8$tVRP with Backhauls /$rP. Toth, D. Vigo$g195 --$g8.1.1$tBenchmark Instances$g197 --$g8.2$tInteger Linear Programming Models$g198 --$g8.2.1$tFormulation of Toth and Vigo$g198 --$g8.2.2$tFormulation of Mingozzi, Giorgi, and Baldacci$g200 --$g8.3$tRelaxations$g201 --$g8.3.1$tRelaxation Obtained by Dropping the CCCs$g202 --$g8.3.2$tRelaxation Based on Projection$g202 --$g8.3.3$tLagrangian Relaxation$g203 --$g8.3.4$tOverall Additive Lower Bound$g204 --$g8.3.5$tRelaxation Based on the Set-Partitioning Model$g204 --$g8.4$tExact Algorithms$g208 --$g8.4.1$tAlgorithm of Toth and Vigo$g208 --$g8.4.2$tAlgorithm of Mingozzi, Giorgi, and Baldacci$g209 --$g8.4.3$tComputational Results for the Exact Algorithms$g210 --$g8.5$tHeuristic Algorithms$g214 --$g8.5.1$tAlgorithm of Deif and Bodin$g214 --$g8.5.2$tAlgorithms of Goetschalckx and Jacobs-Blecha$g215 --$g8.5.3$tAlgorithm of Toth and Vigo$g216 --$g8.5.4$tComputational Results for the Heuristics$g217 --$g9$tVRP with Pickup and Delivery /$rG. Desaulniers, J. Desrosiers, A. Erdmann, M.M. Solomon, F. Soumis$g225 --$g9.2$tMathematical Formulation$g226 --$g9.2.1$tConstruction of the Networks$g226 --$g9.2.2$tFormulation$g227 --$g9.2.3$tService Quality$g228 --$g9.2.4$tReduction of the Network Size$g228 --$g9.3$tHeuristics$g229 --$g9.3.1$tConstruction and Improvement$g229 --$g9.3.2$tClustering Algorithms$g230 --$g9.3.3$tMetaheuristics$g230 --$g9.3.4$tNeural Network Heuristics$g231 --$g9.3.5$tTheoretical Analysis of Algorithms$g231 --$g9.4$tOptimization-Based Approaches$g232 --$g9.4.1$tSingle Vehicle Cases$g232 --$g9.4.2$tMultiple Vehicle Cases$g234 --$g9.5$tApplications$g236 --$g9.6$tComputational Results$g236 --$gIII$tApplications and Case Studies$g243 --$g10$tRouting Vehicles in the Real World: Applications in the Solid Waste, Beverage, Food, Dairy, and Newspaper Industries /$rB.L. Golden, A.A. Assad, E.A. Wasil$g245 --$g10.2$tComputerized Vehicle Routing in the Solid Waste Industry$g247 --$g10.2.1$tHistory$g247 --$g10.2.3$tCommercial Collection$g249 --$g10.2.4$tResidential Collection$g250 --$g10.2.6$tRoll-on-Roll-off$g252 --$g10.2.7$tFurther Remarks$g254 --$g10.3$tVehicle Routing in the Beverage, Food, and Dairy Industries$g254 --$g10.3.2$tBeverage Industry$g255 --$g10.3.3$tFood Industry$g259 --$g10.3.4$tDairy Industry$g260 --$g10.4$tDistribution and Routing in the Newspaper Industry$g266 --$g10.4.1$tIndustry Background$g266 --$g10.4.2$tNewspaper Distribution Problem$g268 --$g10.4.3$tVehicle Routing Algorithms for NDP$g271 --$g10.4.4$tThree Case Studies$g276 --$g11$tCapacitated Arc Routing Problem with Vehicle-Site Dependencies: The Philadelphia Experience /$rJ. Sniezek, L. Bodin, L. Levy, M. Ball$g287 --$g11.2$tNetworks, Assumptions, and Goals of the CARP-VSD$g288 --$g11.2.1$tTravel Network$g288 --$g11.2.2$tService Network$g289 --$g11.2.3$tVehicle Classes$g290 --$g11.2.4$tTravel Network and Service Network for a Vehicle Class$g291 --$g11.2.5$tVehicle Preference List$g291 --$g11.2.6$tOther Assumptions$g292 --$g11.2.7$tGoals and Constraints of the CARP-VSD$g292 --$g11.3$tVehicle Decomposition Algorithm (VDA)$g293 --$g11.3.1$tStep A. Create and Verify Vehicle Class Networks$g293 --$g11.3.2$tStep B. Estimate Total Work and Determine Initial Fleet Mix$g294 --$g11.3.3$tStep C. Partition the Service Network$g301 --$g11.3.4$tStep D. Determine Travel Path and Balance the Partitions$g304.
650 0 $aTransportation problems (Programming)
650 6 $aProblèmes de transport (Programmation)
650 7 $aTransportation problems (Programming)$2fast$0(OCoLC)fst01155324
650 7 $aKombinatorische Optimierung$2gnd
650 7 $aTransportproblem$2gnd
650 7 $aFahrzeugeinsatz$2gnd
650 7 $aTourenplanung$2gnd
650 17 $aRoutekeuze.$2gtt
650 17 $aOptimaliseren.$2gtt
650 17 $aWegverkeer.$2gtt
650 7 $aVehicle routing problem.$2nli
650 7 $aTransportation problems (Programming)$2nli
700 1 $aToth, Paolo.
700 1 $aVigo, Daniele.
830 0 $aSIAM monographs on discrete mathematics and applications.
856 41 $3Table of contents$uhttp://catdir.loc.gov/catdir/toc/fy032/2001042043.html
938 $aBaker & Taylor$bBKTY$c102.00$d102.00$i0898714982$n0003761435$sactive
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938 $aYBP Library Services$bYANK$n2445794
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029 1 $aHEBIS$b107866242
029 1 $aHR0$b0898714982
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029 1 $aYDXCP$b1806046
994 $aZ0$bP4A
948 $hHELD BY P4A - 136 OTHER HOLDINGS