Modeling the Tradeoffs of Chaotic Storage in Warehouse Management

Abstract

Warehouse operations are critical factors that affect the responsiveness of the supply chain. To meet customer demand on time, it is essential to optimize the operations of the warehouse to enhance the overall supply chain performance. Storage policy is critical in warehouse operations, directly impacting order fulfillment efficiency, lead times, and space utilization. Two storage policies are examined in this study: dedicated storage policy and random storage policy. A dedicated storage policy involves allocating specific locations for each product, while a random storage policy involves placing products in the closest empty cell. Hence, products are equally likely to be placed anywhere within the warehouse.

This study presents a simulation model that mimics a generalized warehouse with two operations operating simultaneously: the storage and retrieval process. The simulation model examines the lead times for storage and retrieval processes, as well as the amount of time needed to store and retrieve products. The main objective of this study is to determine the effect of storage policies on the utilization of the warehouse, lead times, and overall operations of the warehouse. For warehouses with the goal of optimizing the efficiency of storing and receiving items, a dedicated storage policy is recommended, as it is more efficient and has lower storage and pickup times, which can enhance the overall warehouse operations. Yet, these recommendations vary depending on the specific objectives of each warehouse, its unique operations, and essential KPIs. A pilot study was also conducted to perform the storage and retrieval operations, and it can also be concluded that the decision of which storage type to imply highly depends on the costs of each warehouse and its critical requirements. This simulation model is a generalized form of warehouses with the main operations; this model can be modified to suit the specific requirements of each warehouse.

Date of Award	15 Dec 2023
Original language	American English
Supervisor	Saed Amer (Supervisor)