A Discrete Multi-Objective Squirrel Search Algorithm for Energy-Efficient Distributed Heterogeneous Permutation Flowshop with Variable Processing Speed

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 DHPFSPVPS.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.

Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

To meet the multi-cooperation production demand of enterprises,the distributed permutation flow shop scheduling problem(DPFSP)has become the frontier research in the field of manufacturing systems.In this paper,we investigate the DPFSP by minimizing a makespan criterion under the constraint of sequence-dependent setup times.To solve DPFSPs,significant developments of some metaheuristic algorithms are necessary.In this context,a simple and effective improved iterated greedy(NIG)algorithm is proposed to minimize makespan in DPFSPs.According to the features of DPFSPs,a two-stage local search based on single job swapping and job block swapping within the key factory is designed in the proposed algorithm.We compare the proposed algorithm with state-of-the-art algorithms,including the iterative greedy algorithm(2019),iterative greedy proposed by Ruiz and Pan(2019),discrete differential evolution algorithm(2018),discrete artificial bee colony(2018),and artificial chemical reaction optimization(2017).Simulation results show that NIG outperforms the compared algorithms.