TY  - EJOU
AU  - Zeng, Liang 
AU  - Ding, Ziyang 
AU  - Shi, Junyang 
AU  - Wang, Shanshan 

TI  - A Discrete Multi-Objective Squirrel Search Algorithm for Energy-Efficient Distributed Heterogeneous Permutation Flowshop with Variable Processing Speed
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 81
IS  - 1
SN  - 1546-2226

AB  - In the manufacturing industry, reasonable scheduling can greatly improve production efficiency, while excessive resource consumption highlights the growing significance of energy conservation in production. This paper studies the problem of energy-efficient distributed heterogeneous permutation flowshop problem with variable processing speed (DHPFSP-VPS), considering both the minimum makespan and total energy consumption (TEC) as objectives. A discrete multi-objective squirrel search algorithm (DMSSA) is proposed to solve the DHPFSP-VPS. DMSSA makes four improvements based on the squirrel search algorithm. Firstly, in terms of the population initialization strategy, four hybrid initialization methods targeting different objectives are proposed to enhance the quality of initial solutions. Secondly, enhancements are made to the population hierarchy system and position updating methods of the squirrel search algorithm, making it more suitable for discrete scheduling problems. Additionally, regarding the search strategy, six local searches are designed based on problem characteristics to enhance search capability. Moreover, a dynamic predator strategy based on Q-learning is devised to effectively balance DMSSA’s capability for global exploration and local exploitation. Finally, two speed control energy-efficient strategies are designed to reduce TEC. Extensive comparative experiments are conducted in this paper to validate the effectiveness of the proposed strategies. The results of comparing DMSSA with other algorithms demonstrate its superior performance and its potential for efficient solving of the DHPFSP-VPS problem.
KW  - Distributed heterogeneous permutation flowshop problem; squirrel search algorithm; muli-objective optimization; energy-efficient; variable processing speed

DO  - 10.32604/cmc.2024.055574