Technological advancements have fueled heightened competition in manufacturing, compelling companies to adopt strategies prioritizing swift, timely, and high-quality customer service. This necessitates seamless integration of supportive systems such as resources, equipment, facilities, and workforce, underscoring the criticality of scheduling in aligning activities and resources for on-time task completion. Scheduling, inseparable from sequencing, is pivotal in optimizing manufacturing and service industries' operations. However, challenges arise when tasks converge with limited facility capacities, necessitating effective resource allocation. By leveraging mathematical techniques and heuristic methods, scheduling optimizes resource utilization, minimizes production costs, and enhances service quality. Despite its significance, existing models often overlook critical aspects like identical job consideration and sequence-dependent setup times, limiting real-world applicability. This research addresses these gaps by proposing robust mathematical models for intricate scheduling requirements. The proposed approach seeks to optimize manufacturing operations by effectively handling complex scheduling needs, thereby minimizing production costs and enhancing operational efficiency. This research endeavours to advance scheduling optimization strategies through real-world implementation and evaluation and contribute to the manufacturing industry's sustainable growth.
