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Jacques A. Ferland

Bio: Jacques A. Ferland is an academic researcher from Université de Montréal. The author has contributed to research in topics: Tabu search & Generalized assignment problem. The author has an hindex of 35, co-authored 82 publications receiving 3871 citations.


Papers
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Journal ArticleDOI
TL;DR: It is illustrated that genetic operators can fulfill long-term strategic functions for a tabu search implementation that is chiefly founded on short-term memory strategies.
Abstract: Some genetic algorithms are considered for the graph coloring problem. As is the case for other combinatorial optimization problems, pure genetic algorithms are outperformed by neighborhood search heuristic procedures such as tabu search. Nevertheless, we examine the performance of several hybrid schemes that can obtain solutions of excellent quality. For some graphs, we illustrate that genetic operators can fulfill long-term strategic functions for a tabu search implementation that is chiefly founded on short-term memory strategies.

330 citations

Journal ArticleDOI
TL;DR: The purpose of this paper is to outline basic approaches and basic types of algorithms available to deal with this problem and to review their convergence analysis.
Abstract: A generalized fractional programming problem is specified as a nonlinear program where a nonlinear function defined as the maximum over several ratios of functions is to be minimized on a feasible domain of źn. The purpose of this paper is to outline basic approaches and basic types of algorithms available to deal with this problem and to review their convergence analysis. The conclusion includes results and comments on the numerical efficiency of these algorithms.

267 citations

01 Jan 1993
TL;DR: In this paper, a new hybrid procedure that combines genetic operators to existing heuristics is proposed to solve the Quadratic Assignment Problem (QAP), where genetic operators are found to improve the performance of both local search and tabu search.
Abstract: A new hybrid procedure that combines genetic operators to existing heuristics is proposed to solve the Quadratic Assignment Problem (QAP). Genetic operators are found to improve the performance of both local search and tabu search. Some guidelines are also given to design good hybrid schemes. These hybrid algorithms are then used to improve on the best known solutions of many test problems in the literature.

241 citations

Journal ArticleDOI
TL;DR: A multi-objective approach is shown to be a very flexible tool for modeling this problem and could be used in several specific contexts.
Abstract: Solving the nurse scheduling problem properly has a great impact on nurses' working conditions which are strongly related to the level of quality of health care and recruiting of qualified personnel. In this paper, a multi-objective approach is shown to be a very flexible tool for modeling this problem. Hence, it could be used in several specific contexts. Three different solution techniques are also summarized. The approach is illustrated with an example, and numerical results are reported. Copyright © 1996 Elsevier Science Ltd

206 citations

Journal ArticleDOI
TL;DR: In this article, an algorithm is proposed that finds the constrained minimum of the maximum of finitely many ratios, which involves a sequence of linear subproblems if the ratios are linear (convex-concave).
Abstract: An algorithm is suggested that finds the constrained minimum of the maximum of finitely many ratios. The method involves a sequence of linear (convex) subproblems if the ratios are linear (convex-concave). Convergence results as well as rate of convergence results are derived. Special consideration is given to the case of (a) compact feasible regions and (b) linear ratios.

202 citations


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Book
01 Jan 2002

17,039 citations

Book
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.

6,373 citations

Journal ArticleDOI
TL;DR: An overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and the ant colony optimization (ACO) metaheuristic is presented.
Abstract: This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

2,862 citations

Journal ArticleDOI
TL;DR: Computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that MM AS is currently among the best performing algorithms for these problems.

2,739 citations