A bi-objective approach for the multi-skilled worker assignment of a hybrid assembly line-seru production system

¹ School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212100, P.R. China
² State Key Laboratory of Synthetic Automation for Process Industries, Department of Intelligent data and Systems Engineering, Northeastern University, Shenyang 110819, P.R. China

^* Corresponding author: wyh@just.edu.cn

Received: 25 April 2023
Accepted: 22 January 2024

Abstract

The flexibility and responsiveness of seru production have caught the attention of manufacturing and electronics industries. However, multi-skilled worker assignment poses a crucial and challenging decision-making problem for seru production systems. The existing literature on this problem for pure seru production systems primarily focuses on improving efficiency indexes, which often leads to an unbalanced workload among workers. To address this issue, this article investigates multi-skilled worker assignment for a hybrid assembly line-seru production system that comprises divisional serus and a short assembly line. To balance workload and optimize production efficiency, a bi-objective integer nonlinear programming model is developed. This model jointly optimizes worker-to-seru, worker-to-line, batch-to-seru, task-to-worker, and the processing sequence of each batch. A meta-heuristic method, combining Non-Dominated Sorting Genetic Algorithm II (NSGA-II) with Multi-Objective Simulated Annealing (MOSA), NSGA-II-MOSA, is designed to solve the model. The results of numerical experiments demonstrate that the proposed model and solving method can greatly reduce workload imbalance while maintaining production efficiency. Moreover, NSGA-II-MOSA provides better Pareto solutions than three well-known multi-objective optimization approaches.