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Abstract

Kelly-Moore Paints is a paint manufacturing company founded in San Carlos, California in 1946 by William Kelly and William Moore. It has stores located in California, Texas, Oklahoma, and Nevada. They currently own 11 42’ trailers, contract 4 distinct drivers, and service 44 stores Monday-Thursday from its Texas Distribution and Manufacturing Center in Hurst, TX. Given that transportation costs are typically the highest in the supply chain costs, this study will employ data science techniques to ensure the transportation routing, store ordering mechanism, and trailer utilization are at the best efficiency possible given the current ordering patters of the stores. Using an approach based on the Ant Colony Algorithm (ACO) [7] and Traveling Salesman Problem [8], the team will optimize a model of convergence that seeks to allow Kelly-Moore to fine-tune their operation to recover transportation costs that could be leaking from inefficient use of trailer loads and routing optimization. The analysis of convergence provided by Zhu and Wang [14] will serve as guidance to solving a similar problem for Kelly-Moore. In addition to the optimization model, parameters will be used to explore “out of the box” thinking to present various alternatives to Kelly-Moore to help them find the solution that works best for them based on their real data from years past.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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