車輛路徑問題

內(nèi)容概要

　　in the field of combinatorial optimization problems, the
vehicle routing problem (vrp) is one of the most challenging.
defined more than 40 years ago, the problem involves designing the
optimal set of routes for fleets of vehicles for the purpose of
serving a given set of customers . interest in vrp is motivated by
its practical relevance as well as its considerable
difficulty.
　　 the vehicle routing problem covers both exact and heuristic
methods developed for the vrp and some of its main variants,
emphasizing the practical issues common to vrp. the book is
composed of three parts containing contributions from well-known
experts. the first part covers basic vrp, known more commonly as
capacitated vrp. the second part covers three main variants of vrp:
with time windows, backhauls, and pickup and delivery. the third
part covers issues arising in real-world vrp applications and
includes both case studies and references to software
packages.
　　 this book will be of interest to both researchers and
graduate-level students in the communities of operations research
and mathematical sciences. it focuses on a specific family of
problems while offering a complete overview of the effective use of
the most important techniques proposed for the solution of hard
combinatorial problems. practitioners will find this book
particularly useful.
　　 reader need a basic knowledge of the main methods for the
solution of combinatorial optimization problems.

作者簡介

作者：（美國）托夫（Paolo Toth） （美國）Daniele VigoPaolo Toth is a Professor of Combinatorial Optimization at the Faculty of Engineering of the University of Bologna. His current research interests concern the design of algorithms for combinatorial optimization and graph theory problems and their application in real-world transportation, crew management and routing and loading problems. In July 1998, he was awarded the Euro Gold Medal. He has published more than 90 papers internationally,has co-authored and edited several books, and serves as editor for several journals. He is president of the International Federation of the Operational Research Societies (IFORS} for the period 2001-2003.Daniele Vigo is an Associate Professor of Operations Research at the Faculty of Engineering of the University of Bologna. His main research activities are in the field of combinatorial optimization, with particular interest in the design of algorithms for routing, cutting, packing, and crew management problems. He has published more than 30 papers internationally and serves as Associate Editor for the journal Operations Research.

書籍目錄

list of contributors
preface
1 an overview of vehicle routing problems
　1.1 introduction
　1.2 problem definition and basic notation
　　1.2.1 capacitated and distance-constrained vrp
　　1.2.2 vrp with time windows
　　1.2.3 vrp with backhauls
　　1.2.4 vrp with pickup and delivery
　1.3 basic models for the vrp
　　1.3.1 vehicle flow models
　　1.3.2 extensions of vehicle flow models
　　1.3.3 commodity flow models
　　1.3.4 set-partitioning models
　1.4 test instances for the cvrp and other vrps
　bibliography
ⅰ capacitated vehicle routing problem
　2 branch-and-bound algorithms for the capacitated vrp
　　2.1 introduction
　　2.2 basic relaxations
　　　2.2.1 bounds based on assignment and matching
　　　2.2.2 bounds based on arborescences and trees
　　　2.2.3 comparison of the basic relaxations
　　2.3 better relaxations
　　　2.3.1 additive bounds for acvrp
　　　2.3.2 further lower bounds for acvrp
　　　2.3.3 lagrangian lower bounds for scvrp
　　　2.3.4 lower bounds from a set-partitioning formulation
　　　2.3.5 comparison of the improved lower bounds
　　2.4 structure of the branch-and-bound algorithms for cvrp
　　　2.4.1 branching schemes and search strategies
　　　2.4.2 reduction, dominance rules, and other features
　　　2.4.3 performance of the branch-and-bound algorithms
　　2.5 distance-constrained vrp
　　bibliography
　3 branch-and-cut algorithms for the capacitated vrp
　　3.1 introduction and two-index flow model
　　3.2 branch-and-cut
　　3.3 polyhedral studies
　　　3.3.1 capacity constraints
　　　3.3.2 generalized capacity constraints
　　　3.3.3 framed capacity constraints
　　　3.3.4 valid inequalities from stsp
?　　3.3.5 valid inequalities combining bin packing and stsp
　　　3.3.6 valid inequalities from the stable set problem
　　3.4 separation procedures
　　　3.4.1 exact separation of capacity constraints
　　　3.4.2 heuristics for capacity and related constraints
　　　3.4.3 stsp constraints
　　3.5 branching strategies
　　3.6 computational results
　　3.7 conclusions
　　bibliography
　4 set-covering-based algorithms for the capacitated vrp
　　4.1 introduction
　　4.2 solving the linear programming relaxation of p
　　4.3 set-covering-based solution methods
　　　4.3.1 branch-and-bound algorithm for problem cg
　　　4.3.2 polyhedral branch-and-bound algorithm
　　　4.3.3 pseudo-polynomial lower bound on cmin
　　　4.3.4 solving pa via dual-ascent and branch-and-bound
　　4.4 solving the set-covering integer program
　　　4.4.1 a cutting plane method
　　　4.4.2 branch-and-price
　　　4.4.3 additional comments on computational approaches
　　4.5 computational results
　　4.6 effectiveness of the set-covering formulation
　　　4.6.1 worst-case analysis
　　　4.6.2 average-case analysis
　　bibliography
　5 classical heuristics for the capacitated vrp
　　5.1 introduction
　　5.2 constructive methods
　　　5.2.1 clarke and wright savings algorithm
　　　5.2.2 enhancements of the clarke and wright algorithm
　　　5.2.3 matching-based savings algorithms
　　　5.2.4 sequential insertion heuristics
　　5.3 two-phase methods
　　　5.3.1 elementary clustering methods
　　　5.3.2 truncated branch-and-bound
　　　5.3.3 petal algorithms
　　　5.3.4 route-first, cluster-second methods
　　5.4 improvement heuristics
　　　5.4.1 single-route improvements
　　　5.4.2 multiroute improvements
　　5.5 conclusions
　　bibliography
　6 metaheuristics for the capacitated vrp
　　6.1 introduction
　　6.2 simulated annealing
　　　6.2.1 two early simulated annealing algorithms
　　　6.2.2 osman's simulated annealing algorithms
　　　6.2.3 van breedam's experiments
　　6.3 deterministic annealing
　　6.4 tabu search
　　　6.4.1 two early tabu search algorithms
　　　6.4.2 osman's tabu search algorithm
　　　6.4.3 taburoute
　　　6.4.4 taillard's algorithm
　　　6.4.5 xu and kelly's algorithm
　　　6.4.6 rego and roucairol's algorithms
　　　6.4.7 barbarosoglu and ozgur's algorithm
　　　6.4.8 adaptive memory procedure of rochat and taillard
　　　6.4.9 granular tabu search of toth and vigo
　　　6.4.10 computational comparison
　　6.5 genetic algorithms
　　　6.5.1 simple genetic algorithm
　　　6.5.2 application to sequencing problems
　　　6.5.3 application to the vrp
　　6.6 ant algorithms
　　6.7 neural networks
　　6.8 conclusions
　　bibliography
ⅱ important variants of the vehicle routing problem
　7 vrp with time windows
　　7.1 introduction
　　7.2 problem formulation
　　　7.2.1 formulation
　　　7.2.2 network lower bound
　　　7.2.3 linear programming lower bound
　　　7.2.4 algorithms
　　7.3 upper bounds: heuristic approaches
　　　7.3.1 route construction
　　　7.3.2 route improvement
　?　7.3.3 composite heuristics
　　　7.3.4 metaheuristics
　　　7.3.5 parallel implementations
　　　7.3.6 optimization-based heuristics
　　　7.3.7 asymptotically optimal heuristics
　　7.4 lower bounds from decomposition approaches
　　　7.4.1 lagrangian relaxation
　　　7.4.2 capacity and time-constrained shortest-path problem.
　　　7.4.3 variable splitting
　　　7.4.4 column generation
　　　7.4.5 set-partitioning formulation
　　　7.4.6 lower bound
　　　7.4.7 commodity aggregation
　　　7.4.8 hybrid approach
　　7.5 integer solutions
　　　7.5.1 binary decisions on arc flow variables
　　　7.5.2 integer decisions on arc flow variables
　　　7.5.3 binary decisions on path flow variables
　　　7.5.4 subtour elimination and 2-path cuts
　　　7.5.5 k-path cuts and parallelism
　　　7.5.6 integer decisions on (fractional and integer) time
variables
　　7.6 special cases
　　　7.6.1 multiple tsp with time windows
　　　7.6.2 vrp with backhauls and time windows
　　7.7 extensions
　　　7.7.1 heterogeneous fleet, multiple-depot, and initial
conditions
　　　7.7.2 fleet size
　　　7.7.3 multiple time windows
　　　7.7.4 soft time windows
　　　7.7.5 time- and load-dependent costs
　　　7.7.6 driver considerations
　　7.8 computational results for vrptw.
　　7.9 conclusions
　　bibliography
　8 vrp with backhauls
　　8.1 introduction
　　　8.1.1 benchmark instances
　　8.2 integer linear programming models
　　　8.2.1 formulation of toth and vigo
　　　8.2.2 formulation of mingozzi, giorgi, and baldacci
　　8.3 relaxations
　　　8.3.1 relaxation obtained by dropping the cccs
　　　8.3.2 relaxation based on projection
　　　8.3.3 lagrangian relaxation
　　　8.3.4 overall additive lower bound
　　　8.3.5 relaxation based on the set-partitioning model
　　8.4 exact algorithms
　　　8.4. i algorithm of toth and vigo
　　　8.4.2 algorithm of mingozzi, giorgi, and baldacci
　　　8.4.3 computational results for the exact algorithms
　　8.5 heuristic algorithms
　　　8.5.1 algorithm of deif and bodin
　　　8.5.2 algorithms of goetschalckx and jacobs-blecha
　　　8.5.3 algorithm of toth and vigo
　　　8.5.4 computational results for the heuristics
　　bibliography
　9 vrp with pickup and delivery
　　9.1 introduction
　　9.2 mathematical formulation
　　　9.2.1 construction of the networks
　　　9.2.2 formulation
　　　9.2.3 service quality
　　　9.2.4 reduction of the network size
　　9.3 heuristics
　　　9.3.1 construction and improvement
　　　9.3.2 clustering algorithms
　　　9.3.3 metaheuristics
　　　9.3.4 neural network heuristics
　　　9.3.5 theoretical analysis of algorithms
　　9.4 optimization-based approaches
　　　9.4.1 single vehicle cases
　　　9.4.2 multiple vehicle cases
　　9.5 applications
　　9.6 computational results
　　9.7 conclusions
　　bibliography
ⅲ applications and case studies
　10 routing vehicles in the real world: applications in the solid
waste,
　　10.1 introduction
　　10.2 computerized vehicle routing in the solid waste
industry
　　　10.2.1 history
　　　10.2.2 background
　　　10.2.3 commercial collection
　　　10.2.4 residential collection
　　　10.2.5 case studies
　　　10.2.6 roll-on-roll-off
　　　10.2.7 further remarks
　　10.3 vehicle routing in the beverage, food, and dairy
industries
　　　10.3.1 introduction
　　　10.3.2 beverage industry
　　　10.3.3 food industry
　　　10.3.4 dairy industry
　　10.4 distribution and routing in the newspaper industry
　　　10.4.1 industry background
　　　10.4.2 newspaper distribution problem
　　　10.4.3 vehicle routing algorithms for ndp
　　　10.4.4 three case studies
　　　10.4.5 further remarks
　　10.5 conclusions
　　bibliography
　11 capacitated arc routing problem with vehicle-site dependencies:
the philadelphia experience
　　11.1 introduction
　　11.2 networks, assumptions, and goals of the carp-vsd
　　　11.2.1 travel network
　　　11.2.2 service network
　　　11.2.3 vehicle classes
　　　11.2.4 travel network and service network for a vehicle
class
　　　11.2.5 vehicle preference list
　　　11.2.6 other assumptions
　　　11.2.7 goals and constraints of the carp-vsd
　　11.3 vehicle decomposition algorithm (vda)
　　　11.3.1 step a. create and verify vehicle class networks
　　　11.3.2 step b. estimate total work and determine initial fleet
mix
　　　11.3.3 step c. partition the service network
　　　11.3.4 step d. determine travel path and balance the
partitions
　　　11.3.5 step e. revise estimate of total work and adjust fleet
mix
　　11.4 implementation of the vda in philadelphia
　　11.5 enhancements and extensions
　　bibliography
　12 inventory routing in practice
　　12.1 introduction
　　12.2 problem definition
　　12.3 literature review
　　12.4 solution approach
　　　12.4.1 phase i: integer programming model
　　　12.4.2 phase i: solving the integer programming model
　　　12.4.3 phase ii: scheduling
　　12.5 computational experience
　　　12.5.1 instances
　　　12.5.2 solution quality
　　　12.5.3 alternate heuristic
　　　12.5.4 computational experiments
　　12.6 conclusion
　　bibliography
　13 routing under uncertainty: an application in the scheduling of
field service engineers
　　13.1 introduction
　　13.2 vrpsst with variable costs of recourse
　　13.3 literature review
　　　13.3.1 vrpst
　　　13.3.2 vrpsd
　　13.4 stochastic integer vrpsst formulation
　　　13.4.1 first-stage problem
　　　13.4.2 second-stage problem
　　13.5 paired tree search algorithm (ptsa)
　　　13.5.1 linked trees
　　　13.5.2 lower bounds
　　　13.5.3 computational implementation
　　13.6 applied maintenance scheduling problem
　　　13.6.1 maintenance scheduling system in practice
　　　13.6.2 stochastic problem setting
　　13.7 modeling the applied problem as a vrpsst
　　13.8 model input
　　　13.8.1 job locations and the road network
　　　13.8.2 service times
　　13.9 model output: computational considerations
　　　13.9.1 framework for the analysis of results
　　　13.9.2 reoptimization
　　13.10 example scenario
　　13.11 overall computational results
　　13.12 conclusion
　　bibliography
　14 evolution of microcomputer-based vehicle routing software: case
studies in the united states
　　14.1 introduction
　　14.2 definition of the vrp
　　　14.2.1 customer specification
　　　14.2.2 product specification
　　　14.2.3 vehicle specification
　　14.3 algorithms
　　14.4 future trends in vehicle routing software
　　14.5 summary and conclusions
　　bibliography
index

章節(jié)摘錄

版權頁：插圖：In this section we give a formal definition, as graph theoretic models, of the basic problems of the vehicle routing class. These problems, which have received the greatest attention in the scientific literature, are examined in detail in the first two parts of the book. We first describe the Capacitated VRP, which is the simplest and most studied member of the family, then we introduce the Distance-Constrained VRP, the VRP with Time Windows, the VRP with Backhauls, and the VRP with Pickup and Delivery.For each of these problems, several minor variants have been proposed and examined in the literature, and often different problems are given the same name. Although in many cases the solution methods, particularly the heuristic ones, may be adapted to incorporate additional features, this indeterminacy in problem definition generally causes much confusion. Therefore, for each problem we first describe the basic version, i.e., the one that in this book is denoted by the corresponding acronym, and then we discuss the variants. In addition, we make an explicit distinction between the symmetric and asymmetric versions of a problem only if models and solution approaches proposed in the literature make use of this distinction.Also in this section, we introduce all the relevant notation and terminology used throughout the book. Additional notation and definitions required to describe particular variants and practical VRP problems are given in the appropriate chapters. Figure 1.1 summarizes the main problems described in this section and illustrates their connections. In the figure, an arrow moving from problem A to problem B means that B is an extension of A.

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