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Optimization of multi-objective integrated process planning and scheduling problem using a priority based optimization algorithm

机译:使用基于优先级的优化算法优化多目标集成过程计划和调度问题

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摘要

For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.
机译:为了提高现代制造系统的整体性能,过程规划和计划功能的有效集成已成为研究人员考虑的重要领域。由于同时处理流程计划和计划的复杂性,大多数研究工作仅限于解决单个目标函数的集成流程计划和计划(IPPS)问题。由于在处理过程计划和计划时存在许多相互矛盾的目标,因此仅考虑单个优化目标就无法完全解决现实问题。因此,考虑多目标IPPS(MOIPPS)问题是不可避免的。不幸的是,只有很少的研究论文可以解决MOIPPS问题。提出了一种求解MOIPPS问题的优化算法。所提出的算法使用一组调度规则和优先级分配来优化IPPS问题,以解决诸如制造期,总机器负载,总拖延等各种目标。使用固定大小的外部档案库和拥挤距离机制来存储和维护非主导解决方案。为了将结果与其他算法进行比较,已使用基于C矩阵的方法。解决了来自四篇最新论文的实例,以证明所提出算法的有效性。实验结果表明,该方法是解决MOIPPS问题的有效方法。

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