Two stage resource allocation decisions in modern distribution

Mofidi, Seyed Shahab
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Pazour, Jennifer A.
Sharkey, Thomas C.
Wallace, William A., 1935-
Anshelevich, Elliot
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Industrial and management engineering
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This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
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This is the first work to extend the two-stage multi-item newsvendor model to consider negative marginal shortage costs for a set of SKUs. Closed-form expressions are derived for finding the candidate SKUs, as well as their optimal quantity to handle in a proactive strategy. The impact of activity profile based fulfillment cost functions that vary for different SKUs is analyzed. It is optimal to apply a reactive strategy to only high-demanded SKUs in particular cases, which depends on the degree of required responsiveness and the application’s environment. Two operationally challenging applications are modeled. CHAPTER 2 focuses on sea-based logistics, where fulfilling personalized stochastic requests with little warning requires selective offloading of SKUs in a high dense storage area. A reactive strategy is a direct transfer of cargo to the receiving ship. A proactive strategy pre-stages cargo on the deck, which involves additional labor efforts and double handling of cargo, but can improve responsiveness. Results are illustrated with historical data from a sea-based logistics military application. CHAPTER 3 focuses on a ship-from-store environment, in which brick-and-mortar stores are employed as distribution centers to fulfill on-line demands in addition to serving in-store customers. Resource allocation decisions require trading off whether to retrieve a SKU from the shopping floor in advance or after demand materializes. Operational factors of a ship-from-store environment are identified and mapped to the input parameters of the model to determine how these factors influence the candidate SKUs for a proactive strategy. Our analytical models can aid decision makers with optimal item assignment quantities to proactively store in the backroom.
December 2018
School of Engineering
Dept. of Industrial and Systems Engineering
Rensselaer Polytechnic Institute, Troy, NY
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