An ameliorated discrete teaching-learning-based optimization algorithm for fuzzy flexible job shop scheduling with dynamic AGV coordination in mold manufacturing

Mold manufacturing is of vital importance in industrial production. The production planning and scheduling for molds have a significant impact on production efficiency and the competitiveness of enterprises. This study delves into fFJSPa, the fuzzy flexible job shop scheduling problem in mold manufacturing, where automated guided vehicle (AGV) constraints are considered. A rigorous mathematical model is formulated for fFJSPa, employing triangular fuzzy numbers to characterize uncertainties in both processing and transportation times, with the objective of minimizing the fuzzy makespan. To solve this complex problem, an ameliorated discrete teaching-learning-based optimization (ADTLBO) algorithm is proposed. ADTLBO enhances the balance between exploration and exploitation through stochastic integration of discrete crossover operators, historical population archives, self-feedback learning, and backtracking learning mechanisms. Comprehensive experiments on fFJSPa test case demonstrate that ADTLBO significantly outperforms other17 algorithms in optimal fuzzy completion time, worst fuzzy completion time, average fuzzy completion time, and mean certain completion time, particularly in large-scale scenarios. Although computational time is marginally higher in specific cases, ADTLBO delivers robust efficacy in resolving intricate fFJSPa instances, thereby providing a reliable and practical decision-support framework for intelligent production scheduling in mold manufacturing environments.