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|Jobshop scheduling problems are NP ?hard problems. The durations in the reality of manufacturing are often imprecise and the imprecision in data is very critical for the scheduling procedures. Therefore, the fuzzy approach, in the framework of the Dempster-Shafer theory, commands attention. The fuzzy numbers are considered as sets of possible probabilistic distributions. After a review of some issues concerning fuzzy numbers, we discuss the determination of a unique optimal solution of the problem and then we cast a metaheuristic (Simulated Annealing) to this particular framework for optimization.It should be stressed that the obtained schedule remains feasible for all realizations of the operations durations.

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The success of modern heuristics (Simulated Annealing (S.A.), Tabu Search, Genetic Algorithms, …) in solving classical combinatorial optimization problems has drawn the attention of the research community in multicriteria methods. In fact, for large‐scale problems, the simultaneous difficulties of 𝒩𝒫‐hard complexity and of multiobjective framework make most Multiobjective Combinatorial Optimization (MOCO) problems intractable for exact methods. This paper develops the so‐called MOSA (Multiobjective Simulated Annealing) method to approximate the set of efficient solutions of a MOCO problem. Different options for the implementation are illustrated and extensive experiments prove the efficiency of the approach. Its results are compared to exact methods on bi‐objective knapsack problems. Copyright © 1999 John Wiley & Sons, Ltd.

The success of modern heuristics (Simulated Annealing (S.A.), Tabu Search, Genetic Algorithms, . . . ) in solving classical combinatorial optimization problems has drawn the attention of the research community in multicriteria methods.In fact, for large-scale problems, the simultaneous difficulties of NP-hard complexity and of multiobjective framework make most Multiobjective Combinatorial Optimization (MOCO) problems intractable for exact methods.This paper develops the so-called MOSA (Multiobjective Simulated Annealing) method to approximate the set of efficient solutions of a MOCO problem. Different options for the implementation are illustrated and extensive experiments prove the efficiency of the approach. Its results are compared to exact methods on bi-objective knapsack problems.