Multi-objective evolutionary algorithm based on multiple neighborhoods local search for multi-objective distributed hybrid flow shop scheduling problem
Multi-objective evolutionary algorithm based on multiple neighborhoods local search for multi-objective distributed hybrid flow shop scheduling problem
复制标题
基于多邻域局部搜索的多目标进化算法求解多目标分布式混合流水车间调度问题
DOI：
10.1016/j.eswa.2021.115453
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发表时间：
2021-11
影响因子：
8.5
通讯作者：
Pi Dechang
中科院分区：
文献类型：
--
作者：
Shao Weishi;Shao Zhongshi;Pi Dechang
为了在当今快速变化的商业世界中保持竞争力，企业在许多决策领域已经将集中式结构转变为分散式结构。如何在这些分散的生产中心之间有效地调度生产资源是一个亟待解决的问题。研究了以最小化完工时间、总加权提前/拖期和总工作负荷为目标的多目标分布式混合流水车间调度问题。在MDHFSP中，一组工件被分配给多个工厂，每个工厂包含一个混合流水车间调度问题，每个阶段有多台并行机。提出了一种基于多邻域局部搜索的多目标进化算法（MOEA-LS）来求解MDHFSP。在初始化阶段，加权机制被用来决定哪个位置是最好的一个为每个工作时，构造一个新的序列。基于这三个目标设计了多个多邻域局部搜索算子来产生后代。一些较差的邻近解被实现集中的解以模拟退火概率替换。为了避免陷入局部最优，当可达集不变时，采用自适应权值更新机制。与其他经典多目标优化算法的综合比较表明，该算法是非常有效的MDHFSP。
In order to be competitive in today’s rapidly changing business world, enterprises have transformed a centralized to a decentralized structure in many areas of decision. It brings a critical problem that is how to schedule the production resources efficiently among these decentralized production centers. This paper studies a multi-objective distributed hybrid flow shop scheduling problem (MDHFSP) with the objectives of minimizing makespan, total weighted earliness and tardiness, and total workload. In the MDHFSP, a set of jobs have to be assigned to several factories, and each factory contains a hybrid flow shop scheduling problem with several parallel machines in each stage. A multi-objective evolutionary algorithm based on multiple neighborhoods local search (MOEA-LS) is proposed to solve the MDHFSP. In the initialization phase, a weighting mechanism is used to decide which position is the best one for each job when constructing a new sequence. Several multiple neighborhoods local search operators based on the three objectives are designed to generate offsprings. Some worse neighboring solutions are replaced by the solutions in the achieve set with a simulated annealing probability. In order to avoid trapping into local optimum, an adaptive weight updating mechanism is utilized when the achieve set has no change. The comprehensive comparison with other classic multi-objective optimization algorithms shows the proposed algorithm is very efficient for the MDHFSP.
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DOI：
10.1109/tase.2018.2886303
发表时间：
2019-01
影响因子：
5.6
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1016/j.eswa.2017.01.006
发表时间：
2017-05
期刊：
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影响因子：
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DOI：
10.1016/j.swevo.2016.06.002
发表时间：
2017-02
期刊：
Swarm Evol. Comput.
影响因子：
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作者：
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通讯作者：
Jin Deng;Ling Wang
影响因子：
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作者：
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