Abstract
AbstractThe distributed permutation flow shop scheduling problem (DPFSP) is one of the hottest issues in the context of economic globalization. In this paper, a Q-learning enhanced fruit fly optimization algorithm (QFOA) is proposed to solve the DPFSP with the goal of minimizing the makespan. First, a hybrid strategy is used to cooperatively initialize the position of the fruit fly in the solution space and the boundary properties are used to improve the operation efficiency of QFOA. Second, the neighborhood structure based on problem knowledge is designed in the smell stage to generate neighborhood solutions, and the Q-learning method is conducive to the selection of high-quality neighborhood structures. Moreover, a local search algorithm based on key factories is designed to improve the solution accuracy by processing sequences of subjobs from key factories. Finally, the proposed QFOA is compared with the state-of-the-art algorithms for solving 720 well-known large-scale benchmark instances. The experimental results demonstrate the most outstanding performance of QFOA.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hunan Province
Young Talent of Lifting Engineering for Science and Technology in Hunan Province
the Outstanding Youth Project of Education Department of Hunan Province
the Key Project of Education Department of Hunan Province of china
Publisher
Springer Science and Business Media LLC
Reference58 articles.
1. Ali A, Gajpal Y, Elmekkawy TY (2020) Distributed permutation flowshop scheduling problem with total completion time objective. Opsearch 58:425–447
2. Alireza G, Ali A, Mostafa H (2023) Efficient multi-objective meta-heuristic algorithms for energy-aware non-permutation flow shop scheduling problem. Expert Syst Appl 213:119077
3. Babu KS, Vemuru S (2019) Spectrum signals handoff in lte cognitive radio networks using reinforcement learning. Traitement du Signal 36:119–125
4. Chen RH, Yang B, Li S (2020) A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem. Comput Ind Eng 149:106778
5. Dong H, Wang ZB (2022) Distributed assembly replacement flow shop scheduling based on iiga. Manuf Technol Mach Tools 11:169–176