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A knowledge-based evolutionary algorithm with dual resource selection method for job-shop scheduling problem considering manual loading and unloading
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DOI：10.1080/00207543.2026.2626855.png)
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Abstract
En 中文
The job-shop scheduling problem (JSP) presents considerable challenges within the domain of production scheduling, with significant practical ramifications. In real-world workshops, the availability of workers is a critical resource constraint. However, joint optimisation of worker flexibility alongside the JSP is often overlooked. Furthermore, in environments characterised by low levels of equipment automation, the workforce plays a vital role in managing the loading and unloading of production tasks. Thus, it is essential to incorporate the workforce's loading and unloading times into the JSP. This paper investigates the JSP with manual loading and unloading times (JSP-WLU). The knowledge-based genetic algorithm with fitness distance selection (KGA-FDSmw) consists of three components: (1) a set of modified crossover and mutation operators considering balance exploration and exploitation; (2) a dual resource selection method with the information from the solution space (i.e. the distances between individuals) and the objective space (i.e. the fitness of the individuals); and (3) a knowledge-based local search method with block-based neighbourhood structures. The effectiveness of KGA-FDSmw is validated through three JSP test suites and compared against five state-of-the-art metaheuristic algorithms. The experimental results demonstrate that KGA-FDSmw exceeds the performance of its competitors in terms of computational efficiency and solution quality.
Keywords:
Production scheduling
job-shop scheduling
workforce scheduling
metaheuristic
neighbourhood structure
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