Title: Hybrid flow shop scheduling with finite buffers

Authors: Zhonghua Han; Yue Sun; Xiaofu Ma; Zhe Lv

Addresses: Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; Department of Digital Factory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China ' Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China ' Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, USA ' Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China

Abstract: In this paper, the scheduling problem for hybrid flow shop is investigated with the consideration of the finite buffers. Different from the existing works which focus on the makespan minimisation under the constraints of given intermediate buffer sizes, this paper investigates how to decide the reasonable size of the intermediate buffer. Firstly, the problem is modelled with the design of the accurate and reasonable buffer space without affecting the flow shop production efficiency. Then, a hybrid heuristic method is proposed to reduce the algorithm complexity. Specifically, the buffer size is estimated based on the theory of probability distribution, and the searching for the global optimal solution is processed by using a novel and effective self-adaptive differential evolution algorithm. The proposed algorithm can adjust parameters intelligently for stopping unnecessary iterations as well as avoiding the stagnant situation of the local optimum. Lastly, a wide range of practical scenarios are considered for algorithm evaluation, and the numerical results show that the proposed approach is effective on: 1) reducing the buffer size; 2) guaranteeing the hybrid flow shop efficiency.

Keywords: hybrid flow shop; buffer size; probability distribution; self-adaptive differential evolution algorithm.

DOI: 10.1504/IJSPM.2018.091738

International Journal of Simulation and Process Modelling, 2018 Vol.13 No.2, pp.156 - 166

Available online: 02 May 2018 *

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