Applying Mathematical Modeling Optimization Algorithms to Solve Shop Floor Scheduling Problems

Abstract

Shop floor scheduling is a key optimization problem in contemporary manufacturing, seeking to enhance production tasks and resources while increasing productivity and lowering costs. This paper solves the shop floor scheduling problem using an extensive mathematical model and an enhanced simulated annealing (SA) algorithm. The mathematical model captures intricate aspects such as machine allocation, job sequencing, batch transportation, and assembly procedures. To effectively solve the issue, the enhanced SA algorithm employs significant enhancement tactics like knowledgedriven initialization, a problem-specific neighborhood structure, and a restart mechanism to improve solution quality. The methodology is validated using an extensive experimental setup that investigates different situations with varying machine counts and job intricacies. Key findings show a 25% average decrease in makespan, a 20% rise in scheduling effectiveness, and a 15% reduction in computation time, demonstrating the algorithm's efficiency. These results highlight the theoretical and practical importance of this method in tackling real-world shop floor scheduling issues.