Multi-objective artificial bee colony algorithm for order oriented simultaneous sequencing and balancing of multi-mixed model assembly line
Multi-objective artificial bee colony algorithm for order oriented simultaneous sequencing and balancing of multi-mixed model assembly line
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DOI：
10.1007/s10845-017-1316-4
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发表时间：
2017-03
影响因子：
8.3
通讯作者：
Saif Ullah;Z. Guan;Li Zhang;Fei Zhang;Baoxi Wang;M. Jahanzaib
中科院分区：
文献类型：
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作者：
Saif Ullah;Z. Guan;Li Zhang;Fei Zhang;Baoxi Wang;M. Jahanzaib
在多型号混合装配线中，客户订单的型号需求和交货期不同，这使得不同型号的装配线排序和生产线平衡成为关键问题。因此，目前的研究，第一次，研究了面向订单的多混合模型装配线的同时排序和平衡问题，以最小化的材料使用的变化，最小化多条生产线之间的最大完工时间和最小化的惩罚成本的延迟交付模型同时从不同的订单。此外，一个新的混合最小部分排序方法的发展和多目标人工蜂群（MABC）算法，提出了所考虑的问题的解决方案。在标准装配线数据上进行实验，测试MABC算法与文献中著名的多目标优化算法（强度Pareto进化算法SPEA 2）的性能。此外，所提出的MABC算法也测试的数据从一个著名的制造公司在中国对著名的算法在文献中（即SPEA 2）。最终结果表明，建议的MABC优于SPEA 2算法的标准数据和公司数据的问题。
In multi-mixed model assembly lines, customer orders with different demand of models and due dates make it critical to decide the sequencing of different models and balancing of lines. Therefore, current research, first time, investigated an order oriented simultaneous sequencing and balancing problem of multi-mixed model assembly lines with an aim to minimize the variation in material usage, minimize the maximum makespan among the multi-lines and minimize the penalty cost of the late delivery models from different orders simultaneously. Moreover, a new mix-minimum part sequencing method is developed and a multi-objective artificial bee colony (MABC) algorithm is proposed to get the solution for the considered problem. Experiments are performed on standard assembly line data taken from operations library (OR) to test the performance of the proposed MABC algorithm against a famous multi-objective algorithm (Strength Pareto Evolutionary Algorithm i.e. SPEA 2) in literature. Moreover, the proposed MABC algorithm is also tested on the data taken from a well reputed manufacturing company in China against the famous algorithm in literature (i.e. SPEA 2). End results indicate that the proposed MABC outperforms SPEA 2 algorithm for both standard data and company data problems.