A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search
A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search
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DOI：
10.1016/j.ijpe.2010.08.004
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发表时间：
2011
影响因子：
12
通讯作者：
G. Moslehi;M. Mahnam
中科院分区：
文献类型：
--
作者：
G. Moslehi;M. Mahnam
作业车间调度问题是最困难的组合优化问题之一。灵活作业车间问题是作业车间问题的扩展，它允许由给定集合中的任何机器沿着不同路线处理操作。本文提出了一种基于粒子群和局部搜索算法混合的新方法来解决多目标灵活作业车间调度问题。粒子群优化是一种高效的新型进化计算技术，受到鸟类飞行和交流行为的启发。将多目标粒子群算法应用于基于优先级的柔性作业车间调度问题。此外，还将根据类似算法（目标的加权求和和帕累托方法）报告的结果来评估所提出的方法的效率。结果表明，所提出的算法令人满意地捕获了多目标灵活作业车间问题，并且与类似方法具有良好的竞争性。
The job-shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job-shop problem is an extension of the job-shop problem that allows an operation to be processed by any machine from a given set along different routes. This paper present a new approach based on a hybridization of the particle swarm and local search algorithm to solve the multi-objective flexible job-shop scheduling problem. The particle swarm optimization is a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The multi-objective particle swarm algorithm is applied to the flexible job-shop scheduling problem based on priority. Also the presented approach will be evaluated for their efficiency against the results reported for similar algorithms (weighted summation of objectives and Pareto approaches). The results indicate that the proposed algorithm satisfactorily captures the multi-objective flexible job-shop problem and competes well with similar approaches.