遗传算法在考虑能耗的单机批调度中的应用

能耗总成本已成为生产调度中一个重要考虑因素,需要在最大完成时间和能耗总成本之间进行权衡,论文将遗传算法（GA）应用到考虑能耗的单机批调度中,并建立同时优化最大化完成时间和最小化能耗总成本的差异工件单机批调度模型.通过遗传算法在考虑能耗（CEC）和不考虑能耗（IEC）下求出非支配解集,利用工件分批的优化和对遗传选择算子的改进,以保证搜索的效率.实验结果表明,与IEC相比,在CEC下使用遗传算法求出的解效果更好,且随着问题规模的增大和工件加工功率的增加,所得解的优势更加明显.

Application of Genetic Algorithm to Single Machine Batch Scheduling Problem with Energy Cost Consideration

Energy cost has become a critical factor in production scheduling where trade-off between makespan and total energy consumption should be considered. In this study the genetic algorithm is applied to the single machine batch scheduling problem with energy cost consideration and a model which simultaneously optimizes the makespan and total energy cost was proposed. By using the genetic algorithm, a set of non-dominated solutions are obtained in the situation of Considering Energy Consumption (CEC) and Ignoring Energy Consumption (ICE) respectively and the algorithm''s efficiency was guaranteed by optimizing the batch and improving the selection of the genetic operators. The experimental results show that the solution is obtained under the CEC has better effectiveness than that under the IEC. Moreover, the performance of CEC is getting better obviously when the problem size and job power increase.