关键词: 低碳, 柔性作业车间, 多目标调度, 非支配排序遗传算法

Abstract:  To solve the low-carbon scheduling problems of multi-objective flexible job-shops taking equipment energy consumption， tool wear and cutting fluid consumption as carbon emission sources and energy consumption and labor cost as processing cost， a low-carbon scheduling model was formulated to minimize carbon emission， makespan and processing cost， and an improved elitist NSGA-Ⅱ was proposed to solve the problem. Firstly， the chromosome composition was dynamically adjusted by encoding based on Tent chaotic map and greedy decoding based on analytic hierarchy process to improve the quality of the initial population. Then， an adaptive genetic strategy was proposed based on genetic parameters， which adjusted the crossover and mutation rates according to the population evolution stage and the population non dominated state dynamically. Finally， based on external archives an improved elite retention strategy was designed to improve the population diversity in the later stages of the algorithm and retain high-quality individuals in the evolution processes. The effectiveness of the improved algorithm was verified by standard scheduling examples and a practical case. 

Key words: low-carbon, flexible job-shop, multi-objective scheduling, non-dominated sorting genetic algorithm（NSGA-Ⅱ）