A new look of dispatching for multi-objective interbay AMHS in semiconductor wafer manufacturing: A T–S fuzzy-based learning approach

Abstract

Semiconductor wafer fabrication systems (SWFS) are among the most intricate discrete processing environments globally. Since the costs associated with automated material handling systems (AMHS) within fabs account for 20%–50% of manufacturing expenses, it is crucial to enhance the efficiency of material handling in semiconductor production lines. However, optimizing AMHS is difficult due to the complexities inherent in large-scale, nonlinear, dynamic, and stochastic production settings, as well as differing objectives and goals. To overcome these challenges, this paper presents a novel fuzzy-based learning algorithm to enhance the multi-objective dispatching model, which incorporates both transportation and production aspects for interbay AMHS in wafer fabrication manufacturing, aligning it more closely with real-world conditions. Furthermore, we formulate a new constrained nonlinear dispatching problem. To tackle the inherent nonlinearity, a Takagi-Sugeno (T–S) fuzzy modeling approach is developed, which transforms nonlinear terms into a fuzzy linear dispatching model and optimizes the weight in multi-objective problems to obtain the optimal solution. The effectiveness and superiority of the proposed approach are demonstrated through extensive simulations and comparative analysis with existing methods. As a result, the proposed method significantly improves transport efficiency, increases wafer throughput, and reduces processing cycle times.