标题：车辆路径问题
图书信息
出版社: 清华大学出版社; 第1版 (2011年2月1日)
外文书名: The Vehicle Routing Problem
丛书名: 国际著名数学图书
平装: 367页
正文语种: 英语
开本: 16
ISBN: 9787302244943, 7302244944
条形码: 9787302244943
尺寸: 24.4 x 17.4 x 1.8 cm
重量: 540 g
作者简介
作者：（美国）托夫（Paolo Toth） （美国）Daniele Vigo
Paolo 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.
内容简介
《车辆路径问题(影印版)》内容简介：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.
目录
list of contributors 
preface 
1 an overview of vehicle routing problems 
p. toth, d. vigo 
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 
p. toth, d. vigo 
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 
d. naddef, g. rinami 
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 
j. bramel, d. simchi-levi 
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 
g. laporte, f. semet 
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 
m. gendreau, g. laporte, j y. potvin 
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 
j.-f. cordeau, g. desaulniers, j. desrosiers, m. m. solomon, e soumis 
7. i 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 
p. toth, d. vigo 
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 
g. desaulniers, j. desrosiers, a. erdmann, m. m. solomon, f. soumis 
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, 
beverage, food, dairy, and newspaper industries 
b. l. golden, a. a. assad, e. a. wasil 
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 
j. sniezek, l. bodin, l. levy, m. ball 
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 
ann m. campbell, lloyd w. clarke, martin w.p. savelsbergh 
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 
e. hadjiconstantinou, d. roberts 
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 
e. k. baker 
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