| dc.description.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. | en_US |
