A hybrid scheduling approach for automated flowshops with material handling and time constraints

Abstract

Flowshop scheduling problems have been extensively studied by several authors using different approaches. A typical flowshop process consists of successive manufacturing stages arranged in a single production line where different jobs have to be processed following a predefined production recipe. In this work, the scheduling of a complex flowshop process involving Automated Wet-Etch Station (AWS) from Semiconductor Manufacturing Systems, requires a proper synchronization of processing and transport operations, due to stringent storage policies and fixed transfer times between stages. Robust hybrid solution strategies based on mixed integer linear programming (MILP) formulations and heuristic-based approaches, such as aggregation and decomposition methods, are proposed and illustrated on industrial scale problems. The results show significant improvements in solution quality coupled with a reduced computational effort compared to other existing methodologies.