Journal ArticleDOI
Integrating and accelerating tabu search, simulated annealing, and genetic algorithms
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While simulating the original Markov chain with the original cooling schedule implicitly, this work speed up both stand-alone simulated annealing and the combination by a factor going to infinity as the number of transitions generated goes to infinity.Abstract:
We integrate tabu search, simulated annealing, genetic algorithms, and random restarting. In addition, while simulating the original Markov chain (defined on a state space tailored either to stand-alone simulated annealing or to the hybrid scheme) with the original cooling schedule implicitly, we speed up both stand-alone simulated annealing and the combination by a factor going to infinity as the number of transitions generated goes to infinity. Beyond this, speedup nearly linear in the number of independent parallel processors often can be expected.read more
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Journal ArticleDOI
On Uncertainty, Ambiguity, and Complexity in Project Management
TL;DR: A model of a project as a payoff function that depends on the state of the world and the choice of a sequence of actions is developed, which establishes a rigorous language that allows the project manager to judge the adequacy of the available project information at the outset, choose an appropriate combination of strategies, and set a supporting project infrastructure.
Journal ArticleDOI
Vehicle Routing Problem with Time Windows, Part II: Metaheuristics
Olli Bräysy,Michel Gendreau +1 more
TL;DR: This paper surveys the research on the metaheuristics for the Vehicle Routing Problem with Time Windows and describes basic features of each method, and experimental results for Solomon's benchmark test problems are presented and analyzed.
Journal ArticleDOI
Metaheuristics: A bibliography
Ibrahim H. Osman,Gilbert Laporte +1 more
TL;DR: This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics that have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas.
Book ChapterDOI
The Theory and Practice of Simulated Annealing
TL;DR: This chapter presents practical guidelines for the implementation of simulated annealing in terms of cooling schedules, neighborhood functions, and appropriate applications, as well as recent advances in the analysis of finite time performance.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Future paths for integer programming and links to artificial intelligence
TL;DR: Four key areas of Integer programming are examined from a framework that links the perspectives of artificial intelligence and operations research, and each has characteristics that appear usefully relevant to developments on the horizon.
Journal ArticleDOI
Non-Uniform Random Variate Generation.
B. J. T. Morgan,Luc Devroye +1 more
TL;DR: This chapter reviews the main methods for generating random variables, vectors and processes in non-uniform random variate generation, and provides information on the expected time complexity of various algorithms before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.