关键词: 带精英策略的非支配排序遗传算法, 车间调度, 贪婪算法, 多目标优化, 行走时间, 最大完工时间

Abstract: To decrease makespan and walking time of one worker job shop considering learning effect,one worker one-operation-job and multi-machine job shop scheduling model with sum-of-processing-time based learning effect was proposed.Multi-objective Greedy algorithm based on Learning effect (MOGL) was presented.Combined fast elitist Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) with local search based on greedy algorithm,Iterated Multi-objective Genetic Algorithm (IMOGA) was proposed,and the initial solutions based on MOGL was designed.Numerical experiments were design to evaluate the performance of IMOGA,and compared IMOGA with traditional algorithms by Hypervolume indicator.The experimental results showed that IMOGA could solve the problem effectively,revising its initial solutions and local search based on greedy algorithm can improve performance of NSGA-Ⅱ effectively.

Key words: fast elitist non-dominated sorting genetic algorithm, job shop scheduling, greedy algorithm, multi-objective optimization, walking time, makespan