Improved Adaptive Non-Dominated Sorting Genetic Algorithm With Elite Strategy for Solving Multi-Objective Flexible Job-Shop Scheduling Problem

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.

Automated summary

This article presents an improved adaptive non-dominated sorting genetic algorithm with elite strategy to tackle the complex flexible job-shop scheduling problem. By introducing a constructive heuristic algorithm and improving the elite strategy, the algorithm achieves faster generation of Pareto optimal solution set for the multi-objective scheduling model.