Solving Flexible Shop Scheduling Problems Based on Improved NSGA-II

Abstract

This paper proposes an improved non-dominated sorting genetic algorithm-II (NSGA-II) by incorporating local search techniques to address the issues of slow convergence speed and susceptibility to local optima in traditional NSGA. Multiple local search techniques are combined with NSGA-II to enhance the convergence speed and solution quality. In each generation, the algorithm selects key operations on the critical path for local search and applies the principles of minimizing costs and minimizing energy consumption to guide the local search on individuals. The optimization goals consist of reducing the longest completion time, minimizing expenses, and lowering energy usage. Experimental results on multiple instances of flexible job shop scheduling problems demonstrate that this algorithm exhibits good performance and effectiveness.