Optimizing For Warehouse Order Picking Operations: A Case Study At Kao Viet Nam

Abstract

Picking in warehouses is critical to the success and profitability of supply chain management. The major purpose of this research is to improve picking operations at the manufacturing and warehousing company Kao Vietnam. The project aims to uncover practical approaches to improve accuracy, speed, and cost effectiveness by reviewing current picking procedures and methodology. Traditional picking methods such as single picking and batch picking are considered, as well as modern techniques such as zone picking. In this study, I introduce an Integrated local search with A* search algorithm, and a genetic algorithm using Python. Data from Kao Vietnam's warehouse operations is analyzed to discover areas for improvement and cost reduction. The data show that improved picking methods can significantly enhance productivity while decreasing labor costs. The study aims to provide substantial insights into warehouse operational efficiency by undertaking a thorough examination of algorithms and heuristics.