求解多目标柔性作业车间调度问题的离散人工蜂群算法  

A Discrete Artificial Bee Colony Algorithm for Multi-objective Flexible Job Shop Scheduling Problem

作　　者：田野[1] 徐洪华[1] 

机构地区：[1]长春理工大学计算机科学技术学院,长春130022

出　　处：《长春理工大学学报（自然科学版）》2015年第4期116-121,共6页Journal of Changchun University of Science and Technology(Natural Science Edition)

基　　金：吉林省科技发展计划;吉林省公共计算平台资助(20130101179JC-11);吉林省自然科学基金(20130101054JC)

摘　　要：作业车间调度问题是一类典型的组合优化问题,要求多个作业在不同的机器上进行加工,目的是获得最好的作业加工序列,以满足特定的性能指标。柔性作业车间调度问题是对传统的作业车间调度问题的进一步扩展,由于求解的复杂性,使得传统方法很难在有效的时间内获得问题的最优解。人工蜂群算法是近年来提出的一种受生物行为启发的优化算法,该算法主要通过模拟蜜蜂的觅食来实现问题的求解。提出了一种离散的人工蜂群算法于求解柔性作业车间调度问题,算法通过交叉方式来搜索潜在的更好的蜜源,并采用自适应的变异策略来降低早熟收敛的可能性。最后通过对比实验证明算法对于求解多目标柔性作业车间调度问题是有效的。The job shop scheduling problem is one of the most classical combinatorial optimization problems, which con terns allocation of a set of jobs on a set of machines to meet certain criteria. Flexible job shop scheduling problem （FJSSP） is an extension of JSSP and very difficult to achieve an optimal solution with traditional optimization approach- es owing to the high computational complexity. Artificial bee colony （ABC） algorithm invented recently is a biological-inspired optimization algorithm, which simulates the foraging behaviors of honey bee swarm. A discrete artificial bee colony algorithm （DABC） is proposed to solve multi-objective flexible job shop scheduling problem. In DAB（;, the crossover strategy is introduced to search for the better solution （food source）. Besides, an adaptive mutation strategy is adopted to overcome the shortcoming of premature convergence. Finally, the proposed algorithm is tested on different scale problems and compared with the proposed efficient algorithms in the literature recently. The results show that DPSO is an effective and efficient.

关 键 词：组合优化 柔性作业车间调度问题 多目标优化 人工蜂群算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]