Publication

Abstract : Real-time scheduling issues in Assembly Job Shops (AJS) have received little attention from researchers compared to regular job shops or flow shops. Simple Priority Rules (SPRs) are effective in generating job priorities in a shop floor environment by considering single criteria. Many real-world problems consider multiple criteria, where SPRs are not effective. This work aims to analyse the performance of TOPSIS-based Multi-Criteria Decision Making (MCDM) rules intending to minimize Makespan, Mean Flowtime, Mean Tardiness, and Maximum Tardiness of AJS. This study considers benchmark problem instances, which include flat, tall, and complex product structures, as well as large-scale real-world hybrid assembly job shop problems with realistic constraints for performance analysis. Proposed MCDMs are integrated with Discrete Event Simulation (DES) models for performance evaluation. The proposed TOPSIS-based MCDM method does better than the most popular priority dispatching rules (PDRs) in the literature on both large-scale benchmark instances and real-world assembly job shop problems. For the case study problem, the Makespan is improved by 24%, the Mean Flowtime is improved by 39%, and Tardiness is improved by 65% when compared against the best performing SPR. MCDM techniques integrated with discrete event simulation models offer a robust framework for handling the complexities of scheduling in static and dynamic situations. The proposed approach facilitates more effective and economical scheduling decisions by combining various criteria and adjusting to changing situations, which ultimately improves operational performance.