《車輛路徑問題》是2011年2月1日清華大學出版社出版的圖書，作者是Paolo Toth、Daniele Vigo。
基本介紹
- 書名：車輛路徑問題
- 作者：Paolo Toth、Daniele Vigo
- ISBN：9787302244943, 7302244944
- 頁數：367頁
- 出版社：清華大學出版社
- 出版時間：2011年2月1日
- 開本：16
- 叢書名：國際著名數學圖書
作者簡介,內容簡介,目錄,
作者簡介
作者：（義大利）托夫（Paolo Toth） （義大利）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.
內容簡介
《車輛路徑問題(影印版)》內容簡介：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.
目錄
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
車輛路徑問題的發展
1959年Dantzig和Ramse首次對閉合式VRP進行了研究，描述的是將汽油送往各個加油站的實際問題，並首次提出了相應的數學規劃模型以及求解算法。
1964年，Clark和Wright[4]一種對Dantzig-Ramse方法改進的有效的啟發式算法Clark-Wright節約算法。
正是由於以上兩篇開創性論文的發表，使得VRP成為運籌學以及組合最佳化領域的前沿和研究熱點課題。
1969年，Christofides和Eilon套用2-opt[5]和3-opt[6]處理車輛路徑問題。
1970年，提出了兩階段方法求解車輛路徑問題，包括先分組後定路線（clusterfirst-route second）和先定路線後分組（routefirst-cluster second）兩種啟發式策略。
1981年，Fisher和Jaikumar提出以數學規劃為主的最最佳化方法來處理包含大約50個顧客點的問題,同樣其運算效率是一個亟待解決的問題。同年，Gullen，Jarvis和Ratliff建立了人機互動的啟發式方法。
1981年，Bodin and Golden將眾多的VRP求解方法進行了歸納。分為以下七種：數學解析法（Exact Procedure）；人機互動法（Interactive Optimization）；先分群再排路線（Cluster First–Route Second）；先排路線再分群（Route First–Cluster Second）；節省法或插入法（Saving or Insertion）；改善或交換法（Improvement or Exchanges）；數學規劃近似法（Mathematical programming）。
1990年以來，人工智慧方法在解決組合最佳化問題上顯示出強大功能，在各個領域得到充分套用，很多學者也將人工智慧引入車輛路線問題的求解中，並構造了大量的基於人工智慧的啟發式算法。 禁忌搜尋法（TS）基本上是屬於一種人工智慧型（AI）的局部搜尋方法，Willard首先將此算法用來求解VRP 。袁慶達[7]等設計了考慮時間窗和不同車輛類型的禁忌算法，這種算法主要採用GA方法產生初始解，然後禁忌算法對初始解最佳化。模擬退火方法具有收斂速度快，全局搜尋的特點，Osman[8]對VRP的模擬退火算法進行了研究。遺傳算法具有求解組合最佳化問題的良好特性，Holland首先採用遺傳算法（GA）編碼解決VRPTW 問題。現在多數學者採用混合策略，分別採用兩種人工智慧方法進行路線分組和路線最佳化。Ombuki[9]提出了用GA進行路線分組，然後用TS方法進行路線最佳化的混合算法。Bent和Van Hentenryck[10]則首先用模擬退火算法將車輛路線的數量最小化，然後用大鄰域搜尋法（largneighborhood search）將運輸費用降到最低。
綜合過去有關VRP的求解方法，可以將其分為精確算法（exact algorithm）與啟發式算法（heuristics），其中精確算法有分支界限法、分支切割法、集合涵蓋法等；啟發式算法有節約法、模擬退火法、確定性退火法、禁忌搜尋法、基因算法、神經網路、螞蟻殖民算法等。
