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Artificial Immune System Algorithm and Simulated Annealing Algorithm for Scheduling Batches of Parts based on Job Availability Model in a Multi-Cell Flexible Manufacturing System

机译:一种基于多电池柔性制造系统作业可用性模型的零件调度批次的人工免疫系统算法及模拟退火算法

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The flow shop manufacturing cell scheduling problem with sequence dependent setup time is a topic of great concern for many industrial applications. This paper addresses batch scheduling problem in the Multi cell Flexible Manufacturing System (MCFMS) havingsequence dependent batch set up time with flow shop characteristics. The objective of this research problem is the minimization of makespan. A mathematical model for the research problem is developed in this paper. As the research problem is known to be NP-hard, Artificial Immune Systems (AIS) algorithm and Simulated annealing algorithm (SA) algorithm are developed to solve the problem. Eighty test problems are solved in order to test the proposed algorithms. The quality of solution in terms of makespan obtained by AIS algorithm is found superior to that of the SA algorithm. But in computational point of view, SA algorithm takes shorter time than AIS algorithm to find the near optimal batch sequence. Since the operation parameters of meta-heuristic algorithms normally affect the quality of the solution, Sensitive Analysis is carried out for the test problems by varying the percentage of receptor editing in AIS algorithm.
机译:序列依赖设置时间的流店制造单元调度问题是许多工业应用的极大问题的主题。本文涉及多电池柔性制造系统(MCFMS)中的批量调度问题,其具有序列相关批量设置时间与流量店特性。该研究问题的目的是Mapspan的最小化。本文开发了研究问题的数学模型。由于研究问题被称为NP - 硬,开发了人工免疫系统(AIS)算法和模拟退火算法(SA)算法以解决问题。解决了八十个测试问题,以便测试所提出的算法。通过AIS算法获得的MakEspan方面的解决方案质量优于SA算法。但在计算的角度来看,SA算法比AIS算法更短的时间来找到近最佳批量序列。由于元 - 启发式算法的操作参数通常影响解决方案的质量,因此通过改变AIS算法中的受体编辑的百分比来对测试问题进行敏感性分析。

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