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Aghababaei, Bahareh

University of British Columbia

One of the unique characteristics of online retail is the ability to ship items to customers from different fulfillment centers. This causes interdependence between fulfillment centers and creates new challenges in inventory management. Hence, this thesis investigates inventory management decisions in online retail, focusing on the initial stocking of fulfillment centers, their inventory levels, and order fulfillment processes. The problem is formulated as a dynamic integer program, with the objective of minimizing the sum of receiving, holding, and shipping costs while satisfying demand and capacity constraints. The large scale of the problem arises from the size of the fulfillment centers, the number of items, and the number of demand points. We propose a large-scale solution algorithm that aggregates the three sets of items, warehouses, and customers. The algorithm solves the aggregated problem with an exact solution that is tractable in size and then disaggregates the solution through three hierarchical disaggregation stages using a Greedy algorithm to address the NP-hardness of the problem. We develop a theoretical bound on the optimality gap caused by aggregation method. The numerical examples demonstrate that the large-scale algorithm is a computationally efficient approach which produce low-cost solutions for medium-sized clusters compared to the optimal model. Additionally, we also study the impact of key factors, including cost parameters and variations in item sizes on the optimality gap.

Text

2024-08-02

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/88864

Business Administration - Transportation and Logistics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/