Masters Theses

Keywords and Phrases

Inventory Management; k-means Clustering; Operations Research; Supply Chain Optimization; Warehouse Slotting

Abstract

In the era of Industry 4.0, the warehouse management system (WMS) employed by many firms prescribes hybrid storage, i.e., products with high turnover, called fast movers, are kept in random storage for a short time duration before being shifted to a dedicated storage area, while products with low turnover, called slow movers, remain in random storage. From dedicated storage, the products are dispatched to the customer. The challenge for managers is selecting the slot in dedicated storage to assign to each product while demand data change because of fluctuating market conditions; this problem is referred to as slotting in the literature. In this thesis, a practical algorithm rooted in k-means clustering that enables managers to improve warehouse efficiency is employed. The existing literature discusses exact approaches based on mixed integer programming to solve the slotting problem; however, an approach rooted in data analytics, in particular, clustering, is used here instead, as it is computationally more convenient and can scale up to the dimensionality of real-world problems. Also, to the best of knowledge while prior studies (e.g., Rosenwein, 1994) have explored clustering techniques in warehouse slotting, limited work has integrated clustering with weighted priority metrics in hybrid storage environments. The specific clustering algorithm developed here accounts for turnover as well as the physical weight of the product to ensure reduction of material-handling costs while simultaneously providing ease of dispatching from the warehouse's exit point. Numerical results show that this approach outperforms other approaches.

Advisor(s)

Gosavi, Abhijit

Committee Member(s)

Lea, Bih-Ru
Allada, Venkat

Department(s)

Engineering Management and Systems Engineering

Degree Name

M.S. in Engineering Management

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2026

Pagination

ix, 32 pages

Note about bibliography

Includes_bibliographical_references_(pages 28-30)

Rights

© 2026 Teng Yang , All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12611

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