Integrated Warehouse Layout Planning with Fuzzy C-Means Clustering


KÜÇÜKDENİZ T., Sonmez O. E.

4th International Conference on Intelligent and Fuzzy Systems (INFUS), Bornova, Turkey, 19 - 21 July 2022, vol.504, pp.184-191 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 504
  • Doi Number: 10.1007/978-3-031-09173-5_24
  • City: Bornova
  • Country: Turkey
  • Page Numbers: pp.184-191
  • Keywords: Warehouse layout planning, Order picking, Fuzzy C-Means clustering
  • Istanbul University Affiliated: No

Abstract

Warehouses are regarded as critical junction points of supply chains by determining their cost and service level to designate the potential degree of the business success. Warehouse layout planning is related to a wide-ranging problem area regarding complex warehouse operations. Including the determination of items' positioning in warehouses, order picking planning performance may affect the overall warehouse achievements. This study contributes to develop Fuzzy C-Means (FCM) based integrated order picking strategy for items' warehouse layout planning. The aim and originality of the paper is to apply fuzzy clustering as a first phase to categorize the stock keeping units (SKUs) to design the warehouse layout by integrating the order frequency of SKUs and their weights. For this purpose, a specifically defined factor (Q factor) is calculated for each SKU. It represents both the order frequency of SKUs and the spread of the orders throughout the year. Q factor and the weights of SKUs are togetherly used for the analysis in order to generate distinct clusters of SKUs. Experimental results show that FCM clustering methodology outperforms K-Means clustering and also the case that solely considers the weight factors of SKUs.