The flexible job shop problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. This problem covers two main difficulties, namely, machine assignment problem ...
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The flexible job shop problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. This problem covers two main difficulties, namely, machine assignment problem and operation sequencing problem. To reflect as close as possible the reality of this problem, the sequence dependent setup time is taken into consideration. For solving such a complex problem, we propose a hybrid algorithm based on a genetic algorithm (GA) combined with iterated local search (ILS). It is well known that the performance of an algorithm is heavily dependent on the setting of control parameters. For that, our algorithm uses a self-adaptive strategy based on : (1) the current specificity of the search space, (2) the preceding results of already applied algorithms (GA and ILS) and (3) their associated parameter settings. We adopt this strategy in order to detect the next promising search direction and maintain the balance between exploration and exploitation. Computational results show that our algorithm provides better solutions than other well known algorithms. (C) 2017 The Authors. Published by Elsevier B.V.
The flexible job shop problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. This problem covers two main difficulties, namely, machine assignment problem ...
详细信息
The flexible job shop problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. This problem covers two main difficulties, namely, machine assignment problem and operation sequencing problem. To reflect as close as possible the reality of this problem, the sequence dependent setup time is taken into consideration. For solving such a complex problem, we propose a hybrid algorithm based on a genetic algorithm (GA) combined with iterated local search (ILS). It is well known that the performance of an algorithm is heavily dependent on the setting of control parameters. For that, our algorithm uses a self-adaptive strategy based on : (1) the current specificity of the search space, (2) the preceding results of already applied algorithms (GA and ILS) and (3) their associated parameter settings. We adopt this strategy in order to detect the next promising search direction and maintain the balance between exploration and exploitation. Computational results show that our algorithm provides better solutions than other well known algorithms.
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