Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Statistics: principles and methods
Statistics: principles and methods
Enumerative approaches to combinatorial optimization - part I
Annals of Operations Research
Stochastic discrete optimization
SIAM Journal on Control and Optimization
The vehicle scheduling problem with intermittent customer demands
Computers and Operations Research
Modern heuristic techniques for combinatorial problems
A method for discrete stochastic optimization
Management Science
Nelder-Mead simplex modifications for simulation optimization
Management Science
A tabu search heuristic for the heterogenous fleet vehicle routing problem
Computers and Operations Research
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Proceedings of the 33nd conference on Winter simulation
A survey on metaheuristics for stochastic combinatorial optimization
Natural Computing: an international journal
A multiobjective metaheuristic for a mean-risk multistage capacity investment problem
Journal of Heuristics
A multiobjective metaheuristic for a mean-risk static stochastic knapsack problem
Computational Optimization and Applications
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In the field of combinatorial optimization, it may be possible tomore accurately represent reality through stochastic models rather thandeterministic ones. When randomness is present in a problem, algorithmdesigners face new difficulties which complicate their task significantly.Finding a proper mathematical formulation and a fast evaluation of theobjective function are two major issues. In this paper we propose a new tabusearch algorithm based on sampling and statistical tests. The algorithm isshown to perform well in a stochastic environment where the quality offeasible solutions cannot be computed easily. This new search principle isillustrated in the field of cause and effect analysis where the true causeof an undesirable effect needs to be eliminated. A set of npotential causes is identified and each of them is assumed to be the truecause with a given probability. The time to investigate a cause is a randomvariable with a known probability distribution. Associated with each causeis the reward obtained if the cause is really the true cause. The decisionproblem is to sequence the n potential causes so as to maximizethe expected reward realized before a specified time horizon.