A Stochastic Optimization Algorithm for Joint Inventory and Fulfillment in an Omnichannel Supply Network

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Abstract

We study an inventory optimization problem for a retailer that faces stochastic online and in-store demand in a selling season of fixed length. The retailer has to decide the order-up-to inventory levels and an order fulfillment policy that optimizes the expected total costs. We propose a technique that combines the framework of Turing-Good sampling and stochastic optimization. Our algorithm obtains an average of 6.2% total cost reduction compared to a state-of-the-art algorithm. The cost decrease is obtained by reserving more inventory, thereby reducing the lost sales costs and reducing fulfillment costs. The algorithm we propose is especially beneficial for shorter time horizons and higher in-store demand.

Original languageBritish English
Title of host publicationProceedings - 4th European Rome Conference 2021
EditorsMario Fargnoli, Mara Lombardi, Massimo Tronci, Patrick Dallasega, Matteo Mario Savino, Francesco Costantino, Giulio Di Gravio, Riccardo Patriarca
Pages2387-2395
Number of pages9
StatePublished - 2021
Event4th European International Conference on Industrial Engineering and Operations Management, IEOM 2021 - Virtual, Online
Duration: 2 Aug 20215 Aug 2021

Publication series

NameProceedings of the International Conference on Industrial Engineering and Operations Management
ISSN (Electronic)2169-8767

Conference

Conference4th European International Conference on Industrial Engineering and Operations Management, IEOM 2021
CityVirtual, Online
Period2/08/215/08/21

Keywords

  • Clustering
  • Inventory
  • Omnichannel logistics
  • Two-stage optimization

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