A Flexible Job Shop Scheduling Problem Considering On-Site Machining Fixtures: A Case Study From Customized Manufacturing Enterprise

Abstract

The joint optimization of production scheduling and resource constraints is critical to modern manufacturing systems. The number of auxiliary resources (fixtures) is usually insufficient in customized manufacturing. Thus, on-site machining fixtures (Type II fixtures) should be prepared in the workshop to reduce the shortage. In this way, Type II fixtures are production tasks and resource constraints, while Type I fixtures are only resource constraints. The existing studies mainly concentrate on Type I fixtures, whereas the research on Type II fixtures is limited. Therefore, this paper focuses on a flexible job shop with on-site machining fixtures (FJSP-F). Firstly, a mathematical model is developed to minimize total weight tardiness (TWT). Secondly, a job-fixture-machine (JFM) encoding and novel decoding methods are presented to obtain a feasible schedule solution. Thirdly, an improved genetic algorithm (IGA4F) with problem-specific variable neighborhood search (PVNS) is proposed to balance the exploration and exploitation. Finally, the proposed algorithm is tested on 20 instances with comparison algorithms. The results demonstrate that IGA4F is a competitive algorithm in large-scale instances. From the case study results, the performance gains of the TWT and makespan obtained by IGA4F are 49.27% and 28.94% compared to the original schedule solution. Note to Practitioners—The integrated problem of fixture allocation and production scheduling is widespread in highly customized manufacturing enterprises, such as aerospace and shipbuilding. A well-balanced allocation between fixtures and machines can facilitate productivity and resource utilization. In general, Type I fixtures can be used directly if they are idle, and these fixtures are treated as resource constraints. However, due to the limited number of Type II fixtures, they are only available when finished in the workshop. Hence, Type II fixtures are considered production tasks and resource constraints, and the number of these fixtures is dynamic during the production cycle. Therefore, it is necessary for enterprise managers to investigate the effect of Type II fixtures on production scheduling. This paper proposes novel encoding and decoding methods to represent the solution and objective spaces. The evolutionary-based algorithm is proposed to solve the daily order of a real-world enterprise. The obtained results from the proposed algorithm can guide the managers to promote the workshop’s productivity.