Atölye Çizelgeleme Problemi: Modelleme ve Havacılık Sektöründe Uygulama Çalışması

Abstract

In job shop production systems, the decision regarding the assignment sequence of a limited number of parts and their respective operations to machines with constrained capacities is a frequently encountered and highly complex scheduling problem in production planning. In the literature, such problems are classified as Job Shop Scheduling Problems (JSSPs). The presence of parts with varying processes and routing paths, along with limited machine and time resources, complicates the management of delivery schedules. However, effective management of production processes is possible through the use of appropriate priority rules. This study addresses a job shop scheduling problem in an aircraft manufacturing workshop characterized by constraints in machine capacity and part routing, where priority rules are taken into account. The existence of parts with different routing paths and processing times, combined with limited machine capacity, further increases the complexity of the scheduling problem. Within this context, several mathematical models have been developed with the aim of improving production performance, and the production process has been optimized through these models. Two different objective functions were considered in the models: minimizing the maximum tardiness/earliness and minimizing the total production (flow) time. The results obtained from the developed mathematical models were compared with simulation results of the current production system, and the effects of capacity variations and delivery times on scheduling performance were evaluated. Furthermore, the models were associated with specific priority rules, and it was observed that schedules serving different objective functions could be generated. This study provides an original contribution to the literature on operational scheduling in the aviation sector by developing solutions aimed at different objective functions associated with priority rules, thereby offering concrete outcomes for improving scheduling performance and enhancing resource utilization in real-world production environments.