A blood component production–inventory problem with preparation method combination and ABO compatibility

Abstract

We consider the component dependence resulting from each preparation method consistently outputting a particular component combination in the integrated blood component production–inventory problem in a blood center. We first formulate a Markov decision process that comprehensively considers the alternative preparation methods, ABO compatibility, varying age-based inventories, stochastic supply of whole blood, and stochastic demand for components. Then, an approximate dynamic programming algorithm with the interval–adaptive and myopic-learning acceleration approaches is proposed to solve the problem. It performs well in the improvements of both the precision and learning speed. The numerical study displays the sophisticated optimal inventory levels and issuance policies of the blood components. We show that an integrated, operational production-inventory modeling is more capable of dealing with the interaction among the dependent outputs and their differentiated compatibilities and perishabilities. Moreover, the different substitution strategies (i.e., push and pull) are verified to provide similar overall supply levels while causing slight differences in other aspects. Further suggestions for specific components are also provided.

Keywords:

• Production–inventory problem
• blood component preparation
• approximate dynamic programming
• preparation methods
• ABO compatibility

Acknowledgments

The authors are indebted to the Department Editor, the Associate Editor and the two anonymous reviewers for their helpful and thoughtful suggestions on this paper.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Notes on contributors

Fei Yang is a PhD candidate at Southwest Jiaotong University, China. His specific interests lie in health care, sustainable agriculture optimization, inventory management, transportation research, approximate dynamic programming, and decomposition algorithms. His research appeared in reputed journals such as INFORMS Journal on Computing and Transportation Research Part E.

Ying Dai is a Professor at Southwest Jiaotong University, China. Her research focuses on blood supply chain, closed-loop supply chain, and emergency logistics. She has published research papers in INFORMS Journal on Computing, Decision Sciences, Omega, International Journal of Production Economics, Transportation Research Part E, and Computers & Industrial Engineering, etc.

Zu-Jun Ma is a Professor at Southwest Jiaotong University, China. His expertise resides in system analysis and optimization to improve the efficiency and effectiveness of complex systems, such as blood supply chain, closed-loop supply chain, emergency logistics, rural logistics, and natural gas networks. He has been the project manager for many research projects, authored four research books, and published in peer-refereed journals such as Production and Operations Management, INFORMS Journal on Computing, Decision Sciences, IEEE Transactions on Intelligent Transportation Systems, Omega, International Journal of Production Economics, Transportation Research Part E, Computers & Industrial Engineering, Energy Policy, and Energy Economics.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under grant 71672154; the National Social Science Fund of China under grant 19XGL021; and the Humanities and Social Sciences Foundation of the Ministry of Education of China under grant 16YJA630038.