Optimizing Multi-Objective Flexible Job-Shop Scheduling Using Hybrid Bat Algorithm and Simulated Annealing
Keywords:
MOFJSSP, Hybrid Bat Algorithm (BA), Manufacturing Scheduling Optimization, Multi-Objective Optimization Simulated Annealing (SA)Abstract
This research investigates the application of a Hybrid Bat Algorithm (BA) and Simulated Annealing (SA) approach to solve the Multi-Objective Flexible Job-Shop Scheduling Problem (MOFJSSP) within contemporary manufacturing settings. MOFJSSP embodies the complexities of scheduling in modern industries, encompassing multiple conflicting objectives such as minimizing makespan, reducing idle time, optimizing machine utilization, and minimizing production costs. Traditional approaches often struggle to address these complexities adequately. To confront these challenges, a hybrid algorithm integrating BA and SA is proposed, leveraging their respective strengths in exploration and exploitation of solution spaces. The methodology involves problem formulation, solution representation, parameter settings, initialization strategies, iterative evolution mechanisms, and comprehensive evaluation. Experimental results showcase the hybrid approach's superior convergence rates, solution quality, and robustness in comparison to individual algorithms and state-of-the-art methods. The implications suggest potential applications in optimizing manufacturing scheduling, logistics, and diverse industries. Moreover, the research paves the way for future exploration into hybridization with emerging techniques, integration with Industry 4.0 technologies, and adaptation to dynamic manufacturing environments. Embracing these findings promises enhanced operational efficiency, informed decision-making, and continuous innovation in manufacturing scheduling practices.
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