Integrating Ai And Optimization Techniques For Flexible Job Shop Scheduling Problems In Semiconductor Manufacturing

Abstract

This thesis studies the optimization of the Flexible Job Shop Scheduling Problem (FJSSP) in semiconductor manufacturing through the application of Integer Programming (IP), Constraint Programming (CP), Hybrid Genetic Algorithm Simulated Annealing (GA-SA), and Deep Reinforcement Learning (DRL). The paper evaluates the efficacy of these techniques in improving scheduling efficiency through a comparative analysis among numerical, medium, and large case sets. While DRL shows promise in managing complex events despite sporadic poor results, CP is rather adept at providing quick answers. GA-SA offers a harmonic mix between the efficiency and quality of the solutions. The results highlight in this industry the significant social, environmental, and financial benefits of implementing advanced scheduling techniques. This work suggests that future research should prioritize hybrid models that combine DRL and CP to improve flexibility and efficiency. The practical consequences of implementing these models strategically within semiconductor manufacturing are to maximize output and resource use, hence supporting a sustainable industr