A modified biogeography-based optimization algorithm with improved mutation operator for job shop scheduling problem with time lags

M. Harrabi, O. Driss, K. Ghédira
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引用次数: 3

Abstract

This paper addresses the job shop scheduling problem including time lag constraints. This is an extension of the job shop scheduling problem with many applications in real production environments, where extra (minimum and maximum) delays can be introduced between successive operations of the same job. It belongs to a category of problems known as NP-hard problem due to large solution space. Biogeography-based optimization is an evolutionary algorithm which is inspired by the migration of species between habitats, recently proposed by Simon in 2008 to optimize hard combinatorial optimization problems. We propose a hybrid biogeography-based optimization (HBBO) algorithm for solving the job shop scheduling problem with additional time lag constraints with minimization of total completion time. In the proposed HBBO, the effective greedy constructive heuristic is adapted to generate the initial population of habitat. Moreover, a local search metaheuristic is investigated in the mutation step in order to ameliorate the solution quality and enhance the diversity of the population. To assess the performance of HBBO, a series of experiments on well-known benchmark instances for job shop scheduling problem with time lag constraints is performed.
针对时滞作业车间调度问题,提出了一种改进的基于生物地理的变异算子优化算法
研究了包含时滞约束的作业车间调度问题。这是实际生产环境中许多应用程序的作业车间调度问题的扩展,在实际生产环境中,同一作业的连续操作之间可能会引入额外的(最小和最大)延迟。由于解空间大,它属于np困难问题的一类。基于生物地理学的优化算法是Simon在2008年提出的一种基于物种在栖息地之间迁移的进化算法,用于优化组合优化问题。提出了一种基于生物地理的混合优化(HBBO)算法,用于求解具有附加时滞约束且总完工时间最小的作业车间调度问题。在提出的HBBO中,采用了有效的贪婪构造启发式算法来生成栖息地的初始种群。此外,为了改善解的质量和提高种群的多样性,在突变步骤中研究了局部搜索元启发式算法。为了评估HBBO的性能,对具有时滞约束的作业车间调度问题进行了一系列著名的基准测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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