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Author

Éric D. Taillard

Bio: Éric D. Taillard is an academic researcher from Information Technology University. The author has contributed to research in topic(s): Vehicle routing problem & Tabu search. The author has an hindex of 32, co-authored 59 publication(s) receiving 12318 citation(s). Previous affiliations of Éric D. Taillard include École Polytechnique Fédérale de Lausanne & University of Applied Sciences Western Switzerland.
Papers
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Journal ArticleDOI
Éric D. Taillard1Institutions (1)
TL;DR: This paper proposes 260 randomly generated scheduling problems whose size is greater than that of the rare examples published, and the objective is the minimization of the makespan.
Abstract: In this paper, we propose 260 randomly generated scheduling problems whose size is greater than that of the rare examples published. Such sizes correspond to real dimensions of industrial problems. The types of problems that we propose are: the permutation flow shop, the job shop and the open shop scheduling problems. We restrict ourselves to basic problems: the processing times are fixed, there are neither set-up times nor due dates nor release dates, etc. Then, the objective is the minimization of the makespan.

1,945 citations


Journal ArticleDOI
TL;DR: A tabu search heuristic for the vehicle routing problem with soft time windows, where lateness at customer locations is allowed although a penalty is incurred and added to the objective value.
Abstract: This paper describes a tabu search heuristic for the vehicle routing problem with soft time windows. In this problem, lateness at customer locations is allowed although a penalty is incurred and added to the objective value. By adding large penalty values, the vehicle routing problem with hard time windows can be addressed as well. In the tabu search, a neighborhood of the current solution is created through an exchange procedure that swaps sequences of consecutive customers (or segments) between two routes. The tabu search also exploits an adaptive memory that contains the routes of the best previously visited solutions. New starting points for the tabu search are produced through a combination of routes taken from different solutions found in this memory. Many best-known solutions are reported on classical test problems.

923 citations


Journal ArticleDOI
Yves Rochat1, Éric D. Taillard2Institutions (2)
TL;DR: A probabilistic technique to diversify, intensify, and parallelize a local search adapted for solving vehicle routing problems and local searches is presented.
Abstract: This article presents a probabilistic technique to diversify, intensify, and parallelize a local search adapted for solving vehicle routing problems. This technique may be applied to a very wide variety of vehicle routing problems and local searches. It is shown that efficient first-level tabu searches for vehicle routing problems may be significantly improved with this technique. Moreover, the solutions produced by this technique may often be improved by a postoptimization technique presented in this article, too. The solutions of nearly forty problem instances of the literature have been improved.

908 citations


Journal ArticleDOI
TL;DR: This presentation demonstrates that a well-tuned implementation of tabu search makes it possible to obtain solutions of high quality for difficult problems, yielding outcomes in some settings that have not been matched by other known techniques.
Abstract: We describe the main features of tabu search, emphasizing a perspective for guiding a user to understand basic implementation principles for solving combinatorial or nonlinear problems. We also identify recent developments and extensions that have contributed to increasing the efficiency of the method. One of the useful aspects of tabu search is the ability to adapt a rudimentary prototype implementation to encompass additional model elements, such as new types of constraints and objective functions. Similarly, the method itself can be evolved to varying levels of sophistication. We provide several examples of discrete optimization problems to illustrate the strategic concerns of tabu search, and to show how they may be exploited in various contexts. Our presentation is motivated by the emergence of an extensive literature of computational results, which demonstrates that a well-tuned implementation makes it possible to obtain solutions of high quality for difficult problems, yielding outcomes in some settings that have not been matched by other known techniques.

907 citations


Book
01 Jan 1999
TL;DR: It is shown that MACS-VRPTW is competitive with the best known existing methods both in terms of solution quality and computation time and improves some of the best solutions known for a number of problem instances in the literature.
Abstract: MACS-VRPTW, an Ant Colony Optimization based approach useful to solve vehicle routing problems with time windows is presented. MACS-VRPTW is organized with a hierarchy of artificial ant colonies designed to successively optimize a multiple objective function: the first colony minimizes the number of vehicles while the second colony minimizes the traveled distances. Cooperation between colonies is performed by exchanging information through pheromone updating. We show that MACS-VRPTW is competitive with the best known existing methods both in terms of solution quality and computation time. Moreover, MACS-VRPTW improves some of the best solutions known for a number of problem instances in the literature.

788 citations


Cited by
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Book
Fred Glover, Manuel Laguna1Institutions (1)
31 Jul 1997
TL;DR: This book explores the meta-heuristics approach called tabu search, which is dramatically changing the authors' ability to solve a host of problems that stretch over the realms of resource planning, telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics, pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservation and scores of other problems.
Abstract: From the Publisher: This book explores the meta-heuristics approach called tabu search, which is dramatically changing our ability to solve a hostof problems that stretch over the realms of resource planning,telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics,pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservationand scores of other problems. The major ideas of tabu search arepresented with examples that show their relevance to multipleapplications. Numerous illustrations and diagrams are used to clarifyprinciples that deserve emphasis, and that have not always been wellunderstood or applied. The book's goal is to provide ''hands-on' knowledge and insight alike, rather than to focus exclusively eitheron computational recipes or on abstract themes. This book is designedto be useful and accessible to researchers and practitioners inmanagement science, industrial engineering, economics, and computerscience. It can appropriately be used as a textbook in a masterscourse or in a doctoral seminar. Because of its emphasis on presentingideas through illustrations and diagrams, and on identifyingassociated practical applications, it can also be used as asupplementary text in upper division undergraduate courses. Finally, there are many more applications of tabu search than canpossibly be covered in a single book, and new ones are emerging everyday. The book's goal is to provide a grounding in the essential ideasof tabu search that will allow readers to create successfulapplications of their own. Along with the essentialideas,understanding of advanced issues is provided, enabling researchers togo beyond today's developments and create the methods of tomorrow.

6,198 citations


Book
30 Jun 2002
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
Abstract: List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

5,811 citations


Journal ArticleDOI
Nenad Mladenović1, Pierre Hansen1Institutions (1)
TL;DR: This chapter presents the basic schemes of VNS and some of its extensions, and presents five families of applications in which VNS has proven to be very successful.
Abstract: Variable neighborhood search (VNS) is a metaheuristic for solving combinatorial and global optimization problems whose basic idea is a systematic change of neighborhood both within a descent phase to find a local optimum and in a perturbation phase to get out of the corresponding valley. In this chapter we present the basic schemes of VNS and some of its extensions. We then describe recent developments, i.e., formulation space search and variable formulation search. We then present some families of applications in which VNS has proven to be very successful: (1) exact solution of large scale location problems by primal-dual VNS; (2) generation of solutions to large mixed integer linear programs, by hybridization of VNS and local branching; (3) generation of solutions to very large mixed integer programs using VNS decomposition and exact solvers (4) generation of good feasible solutions to continuous nonlinear programs; (5) adaptation of VNS for solving automatic programming problems from the Artificial Intelligence field and (6) exploration of graph theory to find conjectures, refutations and proofs or ideas of proofs.

3,088 citations


01 Jan 2018

3,017 citations


Proceedings ArticleDOI
Matthew W. Moskewicz1, Conor F. Madigan2, Ying Zhao3, Lintao Zhang3  +1 moreInstitutions (3)
22 Jun 2001
TL;DR: The development of a new complete solver, Chaff, is described which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy.
Abstract: Boolean satisfiability is probably the most studied of the combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in electronic design automation (EDA), as well as in artificial intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.

2,782 citations


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Performance
Metrics

Author's H-index: 32

No. of papers from the Author in previous years
YearPapers
20221
20191
20151
20132
20122
20093