Efficiently Addressing the Flow Shop Scheduling Problem with Hybrid Obstructions using the Ant Colony Algorithm

Abstract

The flow shop scheduling problem (FSSP), characterized by the continuous flow of production work across multiple machines, presents formidable computational complexity, necessitating innovative and meta-heuristic approaches for effective solutions. In this research, we harness the potential of the ant colony algorithm (ACA), a promising meta-heuristic technique, to tackle the FSSP incorporating combined obstructions. Our primary objective is to minimize overall production time. This paper introduces various types of obstructions inherent to the FSSP, elucidating the intricacies of the contemporary workshop scheduling challenge. We then present a comprehensive outline of the steps comprising our proposed ACA. To assess the algorithm's effectiveness and practical applicability, we rigorously implement it on a selection of benchmark problems sourced from the existing literature in the field. The results obtained are meticulously compared with optimal solutions, highlighting the algorithm's robust performance. Our findings reveal a promising approach to solving complex FSSP, with notable improvements in efficiency and scheduling optimization. This research represents a significant contribution to the field of flow shop scheduling, offering a viable and innovative solution for addressing intricate production scheduling challenges. The successful application of the ACA in this context underscores its potential for resolving real-world manufacturing dilemmas efficiently.