Enhancing Efficiency and Delivery Performance Through Optimization of Machine Scheduling in Pre-emptive Parallel Manufacturing Systems

The current study investigates the integration of unrelated parallel machine scheduling and inventory (WIP) buffer allocation in the production process using Mixed Integer Linear Programming (MILP). Industrial variables like pre-emption and job batching are considered to make it more realistic. The experiment showed that buffer stock is essential for managing unpredictability and meeting customer demand. The findings highlight the significance of the current schedule process with batching of jobs and buffer stock as a crucial mechanism for handling and reducing the negative impact of disruptions in the manufacturing process. Additionally, it also finds pre-emptive job locations and tardy jobs, both of which are crucial for on-time delivery. The CPLEX 12.8 solver is used to analyze the MILP model, which gives the integer results in a suitable time.