A Mathematical Approach to Paint Production Process Optimization

Abstract

As the global paint market steadily grows, finding the most effective processing model to increase production capacity will be the best way to enhance competitiveness. Therefore, this study proposes two production environments commonly used in the paint industry: build-to-order (BTO) and the variation of a configuration-to-order (CTO), called group production, to schedule paint production. Mixed-Integer Linear Program (MILP) was solved using genetic algorithms (GA) to analyze two production environments with various products, different set-up times, and different average demand for each product. The models determine the number of batches, the size and product of each batch, and the batch sequence such that the makespan is minimized. Several numerical instances are presented to analyze the proposed models. The experimental results show that BTO production completes products faster than group production when products are simple (low variety). However, group production is more applicable to manufacturing diverse products (high variety) and mass production (high volume). Finally, the number of colors has the most significant impact on the two models, followed by the number of product types, and finally the average demand.