Tailored Base-Surge Policies in Dual-Sourcing Inventory Systems with Demand Learning

Dual-Sourcing Inventory Management Under Incomplete Information The class of dual-sourcing problems is one of the most fundamental challenges in inventory and supply chain management and is a common practice in global supply chain management. Under a dual-sourcing strategy, a firm procures items from a regular source with lower cost and longer lead time and an expedited source with higher cost and shorter lead time. We focus on Tailored Base-Surge (TBS) policies, where a constant order is placed with the regular source each period, and the expedited source follows an order-up-to rule. Although existing literature assumes that stochastic demand processes are known inputs to optimization models, decision-makers often lack this information in practice and must learn it in real time to maximize expected revenue. In “Tailored Base-Surge Policies in Dual-Sourcing Inventory Systems with Demand Learning”, B. Chen and C. Shi develop online learning algorithms for finding the optimal TBS policy, demonstrating strong theoretical and numerical performance.

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Publication Details

Journal
Operations Research
Published
2024-12-20
DOI
https://doi.org/10.1287/opre.2022.0624
Citations
9
Primary Topic
Advanced Bandit Algorithms Research
Type
article
Field-Weighted Citation Impact
4.41
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article

Tailored Base-Surge Policies in Dual-Sourcing Inventory Systems with Demand Learning

Cong Shi, Boxiao Chen
9 citations
Operations Research
Advanced Bandit Algorithms Research
4.41
article

Tailored Base-Surge Policies in Dual-Sourcing Inventory Systems with Demand Learning

Cong Shi, Boxiao Chen
article en
9 citations

Abstract

Dual-Sourcing Inventory Management Under Incomplete Information The class of dual-sourcing problems is one of the most fundamental challenges in inventory and supply chain management and is a common practice in global supply chain management. Under a dual-sourcing strategy, a firm procures items from a regular source with lower cost and longer lead time and an expedited source with higher cost and shorter lead time. We focus on Tailored Base-Surge (TBS) policies, where a constant order is placed with the regular source each period, and the expedited source follows an order-up-to rule. Although existing literature assumes that stochastic demand processes are known inputs to optimization models, decision-makers often lack this information in practice and must learn it in real time to maximize expected revenue. In “Tailored Base-Surge Policies in Dual-Sourcing Inventory Systems with Demand Learning”, B. Chen and C. Shi develop online learning algorithms for finding the optimal TBS policy, demonstrating strong theoretical and numerical performance.

Operations ResearchVol. 73(4)
University of Miami (US), University of Illinois Urbana-Champaign (US), University of Illinois Chicago (US), Decision Sciences (United States) (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 5%
Advanced Bandit Algorithms Research
4.41
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Tailored Base-Surge Policies in Dual-Sourcing Inventory Systems with Demand Learning — Cong Shi, Boxiao Chen · Operations Research (2024) | TGRS Research Map | TGRS