Inventory-Responsive Donor-Management Policy: A Tandem Queueing Network Model

Author:

Zhu Taozeng1ORCID,Yeo Nicholas Teck Boon2ORCID,Gao Sarah Yini3ORCID,Loke Gar Goei4ORCID

Affiliation:

1. Institute of Supply Chain Analytics, Dongbei University of Finance and Economics, Dalian 116025, China;

2. International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou 215000, China;

3. Lee Kong Chian School of Business, Singapore Management University, Singapore 178899;

4. Rotterdam School of Management, Erasmus University Rotterdam, 3062PA Rotterdam, Netherlands

Abstract

Problem definition: In the blood-donor-management problem, the blood bank incentivizes donors to donate, given blood inventory levels. We propose a model to optimize such incentivization schemes under the context of random demand, blood perishability, observation period between donations, and variability in donor arrivals and dropouts. Methodology/results: We propose an optimization model that simultaneously accounts for the dynamics in the blood inventory and the donor’s donation process, as a coupled queueing network. We adopt the Pipeline Queue paradigm, which leads us to a tractable convex reformulation. The coupled setting requires new methodologies to be developed upon the existing Pipeline Queue framework. Numerical results demonstrate the advantages of the optimal policy by comparing it with the commonly adopted and studied threshold policy. Our optimal policy can effectively reduce both shortages and wastage. Managerial implications: Our model is the first to operationalize a dynamic donor-incentivization scheme, by determining the optimal number of donors of different donation responsiveness to receive each type of incentive. It can serve as a decision-support tool that incorporates practical features of blood supply-chain management not addressed thus far, to the best of our knowledge. Simulations on existing policies indicate the dangers of myopic approaches and justify the need for smoother and forward-looking donor-incentivization schedules that can hedge against future demand variation. Our model also has potential wider applications in supply chains with perishable inventory.Funding: This study was funded by the Singapore Management University through a research [Grant 20-C207-SMU-015] from the Ministry of Education Academic Research Fund Tier 1.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1228 .

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Strategy and Management

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