Hybrid Enhanced Evolutionary Algorithm with Differential Evolution for Distributed No-Wait Flow-Shop Scheduling Problem

Abstract

Abstract: The distributed no-wait flow-shop scheduling problem (DNWFSP) holds significant importance in real-world production environments. However, existing algorithms for solving the DNWFSP have limitations such as unsatisfactory solutions. Therefore, this paper proposes a hybrid enhanced multi-objective evolutionary algorithm (HEMOEA) with differential evolution (HEMOEA-DE) to solve DNWFSP with the criteria of minimizing makespan and total processing time. The algorithm incorporates a mathematical model for the DNWFSP. Effective encoding and decoding methods are employed, and DE is utilized as an efficient genetic operator for chromosome recombination. The HEMOEA-DE algorithm combines the Pareto dominating, dominated relationship-based fitness function and vector-evaluated genetic algorithm algorithms within HEMOEA to approach the Pareto Front quickly. To evaluate the performance of the proposed method, experiments were conducted on the E. Taillard benchmark problem. The outcomes were compared with those of other well-known algorithms. Regarding convergence and performance on the DNWFSP, the HEMOEA-DE algorithm outperformed other HEMOEA algorithms.