A new cuckoo search algorithm for 2-machine robotic cell scheduling problem with sequence-dependent setup times

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• 作者：Arindam Majumder ^{arindam2012@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Dipak Laha ; ^{dipaklaha_jume@yahoo.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Scheduling with sequence-dependent setup times ; 2-Machine centered robot cell ; One-unit cycles ; Total cycle time ; Cuckoo search algorithm ; Minimal part set sequence ; Sequence of robot moves
• 刊名：Swarm and Evolutionary Computation
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：28
• 期：Complete
• 页码：131-143
• 全文大小：1242 K

文摘

The paper addresses the problem of 2-machine robotic cell scheduling of one-unit cycle with sequence-dependent setup times and different loading/unloading times of the parts. As an alternative metaheuristic algorithm, the cuckoo search algorithm has recently attracted growing interests of researchers. It has the capability to search globally as well as locally to converge to the global optimality by exploring the search space more efficiently due to its global random walk governed by Levy flights, rather than standard isotropic random walk. In this study, a discrete cuckoo search algorithm is proposed to determine the sequence of robot moves along with the sequence of parts so that the cycle time is minimized. In the proposed algorithm, the fractional scaling factor based procedure is presented to determine the step length of Levy flights distribution in discrete from and then, using this step length, two neighborhood search techniques, interchange and cyclical shift methods are applied to the current solution to obtain improved solution. A response surface methodology based on desirability function is used to enhance the convergence speed of the proposed algorithm. Also, a design of experiment is employed to tune the operating parameters of the algorithm. Finally, empirical results with a large number of randomly generated problem instances involving large part sizes varying from 200 to 500 under different operating conditions are compared with two well-known algorithms in the literature and demonstrate the effectiveness of the proposed algorithm.