考虑准备时间和关键目标的柔性作业车间低碳调度研究

Research on Flexible Job Shop Low Carbon Scheduling with Setup Times and Key Objectives

针对考虑依赖于顺序准备时间的柔性作业车间低碳调度问题(Flexible job shop low carbon scheduling problem, FJSP),提出了一种新型帝国竞争算法(Imperialist competitive algorithm,ICA)以充分优化关键目标最大完成时间和总延迟时间的同时持续改进非关键目标总能耗。该算法采用新的同化策略使得帝国内每个解至少存在多个学习对象并区别对待帝国内的最好解和其他殖民地,新型帝国竞争中给出了归一化总成本新定义并引入了殖民国家的全局搜索。通过试验系统地分析了总能耗的恶化程度与关键目标的改善程度之间的关系,并验证了新型ICA在求解所研究低碳FJSP方面较强的优势。

Aiming at flexible job shop low carbon scheduling problem(FJSP) with sequence-dependent setup times, a new imperialist competitive algorithm(ICA) is proposed to optimize fully makespan and total tardiness as key objectives and continuously improve total energy consumption as non-key one. In ICA, a new assimilation is implemented by making each solution have more than one learning object and differing some best solutions from other colonies and a new imperialist competition is done by the new definition of normalized total cost and the inclusion of global search of imperialist. A number of experiments are conducted to analyze the relation between the deterioration degree of total energy consumption and improvement degree of key objectives. Experimental results also demonstrate the strong advantages on solving the considered FJSP.