Multi-objective process planning and scheduling using controlled elitist non-dominated sorting genetic algorithm
Multi-objective process planning and scheduling using controlled elitist non-dominated sorting genetic algorithm
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DOI：
10.1080/00207543.2014.957872
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发表时间：
2015-01
影响因子：
9.2
通讯作者：
P. Mohapatra;Ashutosh Nayak;S. K. Kumar;M. Tiwari
中科院分区：
文献类型：
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作者：
P. Mohapatra;Ashutosh Nayak;S. K. Kumar;M. Tiwari
工艺规划和调度的集成被认为是制造系统的关键组成部分。在本文中，多目标的方法来解决计划和调度问题。在这项工作中考虑的三个不同的目标是最小化的最大完工时间，加工成本和闲置时间的机器。为了解决这个问题，我们提出了一种改进的控制精英非支配排序遗传算法（NSGA），以考虑到计算的棘手问题。一个说明性的例子和五个测试用例已被用来证明所提出的模型的能力。结果证实，所提出的多目标优化模型给出了最优和鲁棒的解决方案。该算法与受控精英NSGA和NSGA-II的比较研究表明，该算法显着降低了最大完工时间、成本和空闲时间等调度目标，并且计算效率更高。
The integration of process planning and scheduling is considered as a critical component in manufacturing systems. In this paper, a multi-objective approach is used to solve the planning and scheduling problem. Three different objectives considered in this work are minimisation of makespan, machining cost and idle time of machines. To solve this integration problem, we propose an improved controlled elitist non-dominated sorting genetic algorithm (NSGA) to take into account the computational intractability of the problem. An illustrative example and five test cases have been taken to demonstrate the capability of the proposed model. The results confirm that the proposed multi-objective optimisation model gives optimal and robust solutions. A comparative study between proposed algorithm, controlled elitist NSGA and NSGA-II show that proposed algorithm significantly reduces scheduling objectives like makespan, cost and idle time, and is computationally more efficient.