An interactive heuristic method for multi-objective combinatorial optimization

We have previously developed an adaptation of the simulated annealing for multi-objective combinatorial optimization (MOCO) problems to construct an approximation of the efficient set of such problem. In order to deal with large-scale problems, we transform this approach to propose an interactive pr...

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Veröffentlicht in:Computers & operations research Jg. 27; H. 7; S. 621 - 634
Hauptverfasser: Teghem, J., Tuyttens, D., Ulungu, E.L.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.06.2000
Pergamon Press Inc
Schlagworte:
Assignment problem
Combinatorial optimization
Knapsack problem
Multi-objective programming
Operations research
Optimization
Simulated annealing
Studies
Multi-objective programming
Assignment problem
Combinatorial optimization
Simulated annealing
Knapsack problem
ISSN:0305-0548, 1873-765X, 0305-0548
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Zusammenfassung:We have previously developed an adaptation of the simulated annealing for multi-objective combinatorial optimization (MOCO) problems to construct an approximation of the efficient set of such problem. In order to deal with large-scale problems, we transform this approach to propose an interactive procedure. The method is tested on the multi-objective knapsack problem and the multi-objective assignment problem. Meta-heuristics methods are intensively used with success to solve optimization problems and especially combinatorial problems (Pirlot. EJOR 1996;92:493–511). In the case of a single objective problem, such methods compute an approximation to the unique optimal solution. Recently, some meta-heuristics have been adapted to treat multi-objective problems. These methods construct an approximation of the set of all efficient solutions. For large-scale multi-objective combinatorial problems, the number of efficient solutions may become very large. In order to help a decision maker to make a choice between these solutions, an interactive procedure is developed in this paper.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/S0305-0548(99)00109-4