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.

Tabu Search

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.

/pdf/evolutionary-algorithms-for-solving-multi-objective-problems-2txxk4mqci.pdf

Evolutionary algorithms for solving multi-objective problems

https://sci2s.ugr.es/sites/default/files/files/Teaching/GraduatesCourses/Metaheuristicas/Bibliography/VNS.pdf

Variable neighborhood search

Variable Neighborhood Search.

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.

/pdf/chaff-engineering-an-efficient-sat-solver-2ima1ekzuu.pdf

Chaff: engineering an efficient SAT solver

The multisource Weber problem is to locate simultaneouslym facilities in the Euclidean plane to minimize the total transportation cost for satisfying the demand ofn fixed users, each supplied from its closest facility. Many heuristics have been proposed for this problem, as well as a few exact algorithms. Heuristics are needed to solve quickly large problems and to provide good initial solutions for exact algorithms. We compare various heuristics, i.e., alternative location-allocation (Cooper 1964), projection (Bongartz et al. 1994), Tabu search (Brimberg and Mladenovic 1996a),p-Median plus Weber (Hansen et al. 1996), Genetic search and several versions of Variable Neighbourhood search. Based on empirical tests that are reported, it is found that most traditional and some recent heuristics give poor results when the number of facilities to locate is large and that Variable Neighbourhood search gives consistently best results, on average, in moderate computing time.

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Improvement and Comparison of Heuristics for Solving the Uncapacitated Multisource Weber Problem

The vehicle routeing problem with multiple use of vehicles is a variant of the standard vehicle routeing problem in which the same vehicle may be assigned to several routes during a given planning period. A tabu search heuristic is developed for this problem. It is shown to produce high quality solutions on a series of test problems.

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Vehicle Routeing with Multiple Use of Vehicles

This paper presents a tabu search based method for finding good solutions to a real-life vehicle routing problem. The problem considered deals with some new features beyond those normally associated with the classical problems of the literature: in addition to capacity constraints for vehicles and time windows for deliveries, it takes the heterogeneous character of the fleet into account, in the sense that utilization costs are vehicle-dependent and that some accessibility restrictions have to be fulfilled. It also deals with the use of trailers. In spite of the intricacy of the problem, the proposed tabu search approach is easy to implement and can be easily adapted to many other applications. An emphasis is placed on means that have to be used to speed up the search. In a few minutes of computation on a personal workstation, our approach obtains solutions that are significantly better than those previously developed and implemented in practice.

/pdf/solving-real-life-vehicle-routing-problems-efficiently-using-17hj95ith1.pdf

Solving real-life vehicle routing problems efficiently using tabu search

The vehicle routing problem with time windows models many realistic applications in the context of distribution systems. In this paper, a parallel tabu search heuristic for solving this problem is developed and implemented on a network of workstations. Empirically, it is shown that parallelization of the original sequential algorithm does not reduce solution quality, for the same amount of computations, while providing substantial speed-ups in practice. Such speed-ups could be exploited to quickly produce high quality solutions when the time available for computing a solution is reduced, or to increase service quality by allowing the acceptance of new requests much later, as in transportation on demand systems.

/pdf/a-parallel-tabu-search-heuristic-for-the-vehicle-routing-28d1pzlgg4.pdf

A parallel tabu search heuristic for the vehicle routing problem with time windows

Les metaheuristiques et leurs applications. Les ingenieurs, les economistes, les decideurs se heurtent quotidiennement, quel que soit leur secteur d'activite, a des problemes d'optimisation. Il peut s'agir de minimiser un cout de production, d'optimiser le parcours d'un vehicule ou le rendement d'un portefeuille boursier, de rationaliser l'utilisation de ressources, d'ameliorer les performances d'un circuit electronique, de fournir une aide a la decision a des managers, etc... Cet ouvrage presente une famille de techniques d'optimisation, appelees "metaheuristiques", adaptees a la resolution de problemes pour lesquels il est difficile de trouver un optimum global ou de bons optimums locaux par des methodes plus classiques. Un ouvrage de reference illustre d'etudes de cas. La premiere partie de l'ouvrage presente les principales metaheuristiques : recuit simule, recherche avec tabous, algorithmes evolutionnaires et algorithmes genetiques, colonies de fourmis. La deuxieme partie decrit differentes variantes et extensions de ces methodes, ainsi que de nouvelles voies de recherche. Y sont egalement proposes des conseils methodologiques : techniques de modelisation, comparaisons de methodes et choix de la methode la mieux adaptee a un probleme donne. La troisieme partie presente trois etudes de cas reels : optimisation de reseaux de mobiles UMTS (France Telecom R&D), gestion de trafic aerien (ENAC), optimisation de tournees de vehicules (ILOG)A qui s'adresse ce livre : Aux eleves ingenieurs et etudiants en mathematiques appliquees, algorithmique, recherche operationnelle, gestion de production, economie et finance, aide a la decision,etc... Aux ingenieurs, enseignants-chercheurs, informaticiens, industriels, economistes et decideurs ayant a resoudre des problemes complexes d'optimisation et d'aide a la decision.

Éric D. Taillard

Papers

Improvement and Comparison of Heuristics for Solving the Uncapacitated Multisource Weber Problem

Vehicle Routeing with Multiple Use of Vehicles

Solving real-life vehicle routing problems efficiently using tabu search

A parallel tabu search heuristic for the vehicle routing problem with time windows

Métaheuristiques pour l'optimisation difficile