Improved Adaptive Non-Dominated Sorting Genetic Algorithm With Elite Strategy for Solving Multi-Objective Flexible Job-Shop Scheduling Problem
Xu Liang, Jiabao Chen, Xiaolin Gu, Ming Huang
Abstract
Regarding the complicated flexible job-shop scheduling problem, it is not only required to get optimal solution of the problem but also required to ensure low-carbon and environmental protection. Based on the NSGA-II algorithm, this article proposes an improved adaptive non-dominated sorting genetic algorithm with elite strategy (IA-NSGA-ES). Firstly, the constructive heuristic algorithm is introduced in the initial population phase, and the weight aggregation method is used to restrain the multi-objective mathematical model which takes total completion time, carbon emission and maximum machine tools load as objectives; secondly, elite strategy is improved, simulated annealing method is used to replace parent generation by child generation to enhance the replaced population quality. The improved algorithm obtains the Pareto optimal solution set faster. Using standard computation example and practical workshop problem for simulation, the results of simulation prove that the algorithm is effective and feasible.