Rescheduling And Minimizing Makespan In Dynamic Job Shop Scheduling Problem

Abstract

Dynamic job shop scheduling problem (DJSSP) plays a role in modern manufacturing due to the emergence of dynamic-event in real time: new job arrivals, processing time changes , machine breakdowns, and job cancellations. Objective of study was to minimize makespan in a production environment. Artificial Bee Colony (ABC) algorithm integrated with Tabu Search (TS) and rescheduling strategy was proposed to efficiently deal with these dynamic events in production environments. The research method involved developing mathematical model for small-size instance and using the HABC algorithm for large-size instance. The HABC algorithm integrated the cluster group roulette method and the Crossover Operator to improve searching and mining capabilities. Experimental design indicated that the proposed HABC is better than traditional methods in both solution quality and computational efficiency, demonstrating its accuracy, flexibility, and ability to quickly adapt to changing events. The conclusions highlighted the algorithm's ability to significantly improve scheduling performance in dynamic manufacturing environments, social, and economic impacts, along with recommendations for future research to develop better optimization techniques.