Applications of simulated annealing algorithm in mathematical modelling for scheduling problems

Scheduling problems are common in many fields, with project planning, industrial, operations management, and service establishment. These problems involve the advanced distribution of resources across tasks to exploit exact objectives within predetermined bounds. This study examines an enhanced simulated annealing (ESA) algorithm for addressing dynamic scheduling challenges in job shops, particularly in the context of random job arrivals frequently encountered in manufacturing environments. This programming methodology seeks to minimize three primary targets: the machine sequence variation, the make-span divergence from the original schedule, and the discontinuity rate of newly delivered tasks during processing. In the rescheduling horizon, the ongoing processes are handled as dynamic resource allocation (DRA). Tactics involved in the ESA algorithm include a modified cooling schedule, adaptive temperature regulation, and a solution approval criterion that considers DRA. The experimental results indicate that the ESA algorithm effectively solves job shop scheduling problems. The proposed ESA has a high job completion rate of 95%, task acceptance rate of 92%, job arrival predictability of 85%, discontinuity rate of 8%, and return on investment of 25%. The results highlight the effectiveness of the ESA algorithm in achieving optimal scheduling solutions, underscoring its potential for practical applications in dynamic manufacturing settings.