Adaptive Production Rescheduling System for Managing Unforeseen Disruptions

This work presents a mixed-integer linear programming (MILP) model to solve the production rescheduling problem in a job shop manufacturing system impacted by unexpected events, aiming to minimize production costs and disruptions to the initial schedule. The approach begins by generating an optimal production plan through batch assignments to machines. When unforeseen events, such as machine breakdowns or raw material shortages, occur, a dynamic rescheduling process is triggered, employing an iterative and reactive algorithm to adapt the plan to the real-time conditions on the shop floor. The results demonstrate that this rescheduling method efficiently adjusts to the new conditions while minimizing deviations from the original schedule, achieving solutions within acceptable computational times.