摘要:
本文将批调度问题扩展到针对多目标(ΣCj, MOC)的批调度问题，这一调度问题分为两个阶段：分批和批调度。分批过程使用的是传统的BFLPT分批规则，得到分批结果；而批调度过程中，针对多个目标函数，本文提出了改进型进化算法Improved-NSGA-II来完成多目标的极化问题，同时列举了算法NSGA-II和SPEA2作为对比。通过仿真实验，分别从帕累托解集的数量、质量和算法运行时间三个方面对三种算法进行比较，从而证明算法Improved-NAGS-II的有效性。
Abstract:
In this paper, the batch scheduling problem is extended to the multi-objective (ΣCj, MOC) batch scheduling problem. The scheduling problem is divided into two stages: batching and batch sche-duling. In the batching process using the traditional BFLPT batch rule to obtain the batching results; while in the batch scheduling process, for the multi-objective function, this paper not only presents improved evolutionary algorithm Improved-NSGA-II to solve the multi-objectives minimization problem, but also lists the algorithms of NSGA-II and SPEA2 as the contrast. Through the simulation experiment, to compare the three algorithms in three aspects, respectively from the number, the quality and the running time of Pareto solution set, this paper proves the effectiveness of the Improved-NAGS-II algorithm.