Parallel Machine Scheduling with Constraints in a Labelling Industry Case Study
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This paper would present a case study on solving a parallel machine scheduling problem in the printing department of a global leader in labeling solutions. Motivated by low-season conditions with below-capacity order volumes, the study prioritizes accelerating job completions to reduce resource idle time in downstream processes. It would focus on minimizing the total weighted completion time while addressing real-world constraints including machine-job compatibility, shift boundaries, and resource limitations in color matching processes. The problem was formulated as a three parallel machines with different speeds model and used Weighted Shortest Processing Time dispatching rule to find out solutions. The scheduling objective is to minimize total weighted completion time. Our approach progressively incorporates operational constraints and achieves around 11.11% improvement in objective value compared to the current manual scheduling method. Some discussions on implementation of research results, limitations, and future optimization opportunities and real-time production data integration would be mentioned.
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