Title: An improved genetic algorithm for seru scheduling problem with worker assignment considering lot-splitting

Authors: Ling Shen; Zhe Zhang; Yong Yin

Addresses: School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China ' School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China ' Graduate School of Business, Doshisha University, Karasuma-Imadegawa, Kamigyo-ku, Kyoto, Japan

Abstract: Seru, a production organisation that consists of some equipment and one or more workers that assemble one or more products. It is known as a useful tool in production practice to cope with the volatile environment with diversified demand, short product life cycles and uncertain product types due to its high flexibility and effectiveness. In this paper, we will discuss a seru scheduling problem with worker assignment considering lot-splitting, processing sequence and set-up time. We formulate a mathematical model to minimise the makespan and employ an improved genetic algorithm with strong robustness and global optimisation to solve this complex decision-making problem which is composed of two NP-hard subproblems. In the improved genetic algorithm, three different forms of real number coding are used to express the solution of this complex decision problem accurately at the same time. Then, the crossover and mutation with adaptive probability are adopted to improve the convergence speed and accuracy. Finally, a numerical example is taken and computed to validate the effectiveness of proposed model and algorithm.

Keywords: seru scheduling; worker assignment; lot-splitting; adaptive genetic operator.

DOI: 10.1504/IJADS.2021.118597

International Journal of Applied Decision Sciences, 2021 Vol.14 No.6, pp.710 - 730

Received: 22 Jun 2020
Accepted: 08 Aug 2020
Published online: 29 Oct 2021 *

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