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Minimizing makespan in a Flow Shop Sequence Dependent Group Scheduling problem with blocking constraint

机译:带有阻塞约束的流水车间序列相关组调度问题中的最小化跨度

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Flow Shop Sequence Dependent Group Scheduling (FSDGS) problems gathered much attention from the body of literature in recent years. Nevertheless, the combination of blocking constraint and Group Technology (GT) principles has not been faced by academics so far. The aim of the present paper is to propose an original meta-heuristic approach for minimizing makespan in a FSDGS problem with blocking constraint. To this end, a novel Parallel Self-Adaptive Genetic Algorithm (PSAGA) which adaptively varies the genetic parameters along the evolutionary mechanism was devised. Validation of the proposed metaheuristics was performed by means of the global optima generated by a proper mixed integer linear programming model. An extended experimental campaign, also supported by a specific statistical analysis, demonstrates the effectiveness of the proposed approach compared to other meta-heuristics arising from the relevant literature.
机译:近年来,流水车间序列相关组调度(FSDGS)问题引起了文学界的广泛关注。尽管如此,到目前为止,学者们还没有将阻塞约束与组技术(GT)原理相结合。本文的目的是提出一种原始的启发式方法,以最小化具有阻塞约束的FSDGS问题中的制造期。为此,设计了一种新颖的并行自适应遗传算法(PSAGA),该算法可以沿着进化机制自适应地改变遗传参数。拟议的元启发法的验证是通过适当的混合整数线性规划模型生成的全局最优方法进行的。扩展的实验活动,也得到特定的统计分析的支持,证明了与相关文献中提出的其他元启发式方法相比,该方法的有效性。

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