Abstract
In this study, a distributed flow shop scheduling problem with batch delivery constraints is investigated. The objective is to minimize the makespan and energy consumptions simultaneously. To this end, a hybrid algorithm combining the wale optimization algorithm (WOA) with local search heuristics is developed. In the proposed algorithm, each solution is represented by three vectors, namely a job scheduling sequence vector, batch assignment vector, and a factory assignment vector. Then, an efficient neighborhood structure is applied in the proposed algorithm to enhance search abilities. Furthermore, the simulated annealing algorithm and clustering method are embedded to improve the global search abilities of the algorithm. Finally, 30 instances are generated based on realistic application to test the performance of the algorithm. After detailed comparisons with three efficient algorithms, i.e., ABC-Y, ICA-K, and IWOANS, the superiority of the proposed algorithm is verified.
Highlights
With the rapid development of manufacturing, many enterprises began to consider multiple factories working at the same time which formed distributed scheduling (DS)
Based on the above discussed optimization problems and meta-heuristics, we develop a hybrid algorithm combining the wale optimization algorithm (WOA) with local search heuristics to solve the distributed flow shop scheduling problem with batch delivery constraints (DFSP-BD)
The main contributions are as follows: (1) a hybrid algorithm combining the wale optimization algorithm (WOA) with local search heuristics is developed; (2) each solution is represented by two vectors, namely a job scheduling sequence vector, and a two-dimensional vector to record the factory assignment, and product assignment, respectively; (3) an efficient neighborhood structure is applied in the proposed algorithm to enhance search abilities; and (4) a simulated annealing algorithm and clustering method are embedded, to improve the global search abilities of the algorithm
Summary
Version of Record: A version of this preprint was published at Soft Computing on August 21st, 2021.
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