Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem: Review and Analysis

Abstract

Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe. By combining domain knowledge of the specific optimization problem, the search efficiency and quality of meta-heuristic algorithms can be significantly improved, making it crucial to identify and summarize domain knowledge within the problem. In this paper, we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem (JSP). Firstly, this paper delves into the importance of domain knowledge in optimization algorithm design. After that, the development of different methods for the JSP are reviewed, and the domain knowledge in it for meta-heuristic algorithms is summarized and classified. Applications of this domain knowledge are analyzed, showing it is indispensable in ensuring the optimization performance of meta-heuristic algorithms. Finally, this paper analyzes the relationship among domain knowledge, optimization problems, and optimization algorithms, and points out the shortcomings of the existing research and puts forward research prospects. This paper comprehensively summarizes the domain knowledge in the JSP, and discusses the relationship between the optimization problems, optimization algorithms and domain knowledge, which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future.