Multi-objective artificial bee colony algorithm for order oriented simultaneous sequencing and balancing of multi-mixed model assembly line
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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.
KeywordsMulti-mixed model assembly line Simultaneous sequencing and balancing Multi-objective optimization Artificial bee colony algorithm Pareto solutions
This work has been supported by MOST (Ministry of Science & Technology of China) under the Grants Nos. 2012AA040909, 2012BAH08F04, & 2013AA040206, and by the National Natural Science Foundation of China (Grants Nos. 51035001, 50825503, & 71271156).
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