LITERATURE STUDY: OPTIMIZATION OF MOLD MACHINE SCHEDULING IN PARALLEL BY CONSIDERING SETUP TIME

Abstract

Scheduling on parallel machine systems is a critical issue in modern manufacturing. This study combines two problems: (1) scheduling to maximize revenue on uniform parallel machines with major and minor setups and job splitting, and (2) minimizing makespan on two identical parallel machines that have mold constraints. The methods used include the formulation  of Mixed-Integer Linear Programming (MILP), Revenue Rate (RR) Heuristic (RR-P and RR-B variants), and Branch-and-Bound as an exact method for validation on small instances. The case study uses HP Inc. production data (revenue case) and Presisi Group (mold case). Experimental results showed that RR heuristics provided an average revenue increase of 12.6% and  a  17.8% reduction in makespan over manual schedules; while Branch-and-Bound provided an optimal solution for small instances with an additional 1–2% increase in revenue compared to heuristics. The results of the analysis show that the integration of work grouping strategies based on the type of setup and mold allocation is able to increase revenue significantly while reducing makespan compared to traditional scheduling methods.