BRCE: bi-roles co-evolution for energy-efficient distributed heterogeneous permutation flow shop scheduling with flexible machine speed

Abstract

Distributed manufacturing is the mainstream model to accelerate production. However, the heterogeneous production environment makes engineer hard to find the optimal scheduling. This work investigates the energy-efficient distributed heterogeneous permutation flow scheduling problem with flexible machine speed (DHPFSP-FMS) with minimizing makespan and energy consumption simultaneously. In DHPFSP-FMS, the local search misleads the population falling into local optima which reduces the convergence and diversity. To solve this problem, a bi-roles co-evolutionary algorithm is proposed which contains the following improvements: First, the global search and local search is divided into two swarms producer and consumer to balance computation. Second, three heuristic rules are designed to get a high-quality initialization population. Next, five problem-based local search strategies are designed to accelerate converging. Then, an efficient energy-saving strategy is presented to save energy. Finally, to verify the performance of the proposed algorithm, 22 instances are generated based on the Taillard benchmark, and a number of numerical experiments are adopted. The experiment results state that our algorithm is superior to the state-of-arts and more efficient for DHPFSP-FMS.

Intelligent learning-based cooperative and competitive multi-objective optimization for energy-aware distributed heterogeneous welding shop scheduling

“…However, there is a scarcity of research addressing welding shop scheduling problems (WSP) from an engineering application perspective. Furthermore, as global trade continues to expand, distributed manufacturing has become the prevailing mode in modern manufacturing [6][7][8]. Consequently, different factories exhibit diverse production environments, encompassing variations in machine numbers, types, workers' skills, and more.…”