Iterative two-stage hybrid algorithm for the vehicle lifter location problem in semiconductor manufacturing

Abstract

Abstract Automated material handling systems (AMHSs) in semiconductor fabrication facilities (fabs) are inherently capital intensive because they are moving toward full automation. However, in addition to being capital intensive, full automation can come at the cost of compromised performance or instability when abnormal events occur. Vehicular congestion is one example of an abnormal event and is a recurring problem in fabs that reduces production efficiency. In this paper, motivated by a material handling system design problem when constructing a new semiconductor fabrication plant in practice, we present a model for optimizing the location of overhead hoist transport lifters, which have proven to be a suitable addition to AMHSs for resolving bottlenecks caused by heavy congestion. To do so, we study a capacitated facility location problem (CFLP) that incorporates real-life constraints and consider the interactions between lifters of differing types. We first propose a hybrid approach that combines a genetic algorithm with a depth-first search (DFS) based on memorization to approximate the optimum positions for the installation of the lifters. We then conduct a numerical experiment to compare the performance of our approach with optimal solutions in small- to medium-sized facilities and perform a sensitivity analysis for the important parameters involved. Finally, an experimental study based on real data from semiconductor fabs is conducted to demonstrate the applicability and usefulness of the proposed model.