Integrated inventory routing and surgical delay for blood products: A bi-objective trade-off approach

Liu Zhao; Nengmin Wang; Yinfeng Xu; Meng Zhang; Liu Zhao; Nengmin Wang; Yinfeng Xu; Meng Zhang

doi:10.3934/jimo.2026206

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 12: 6124-6149. doi: 10.3934/jimo.2026206

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Research article

Integrated inventory routing and surgical delay for blood products: A bi-objective trade-off approach

1.
School of Management, Xi'an Jiaotong University, Xi'an Shaanxi 710049, China
2.
ERC for Process Mining of Manufacturing Services in Shaanxi Province, Xi'an Shaanxi 710049, China
3.
The Key Lab of the Ministry of Education for Process Management & Efficiency Engineering, Xi'an Shaanxi 710049, China
4.
School of Economics and Management, Xi'an Technological University, Xi'an Shaanxi 710021, China

Received: 15 April 2026 Revised: 30 September 2026 Accepted: 22 October 2026 Published: 03 November 2026
90B06, 90C59

Despite the large and rapidly growing global blood transfusion diagnostics market, it still cannot meet the blood demands worldwide. Medical institutions often resort to postponing or canceling elective surgeries to cope with blood shortages, which not only affects patient treatment but also increases the risk of preventable deaths. In this study, we examined a bi-objective inventory routing problem (IRP) for blood products, taking surgical delays into account. Correspondingly, a bi-objective mixed-integer programming model was developed with two objectives of minimizing the total costs and the total weighted surgical delay time simultaneously. To solve the model, a hybrid algorithm combining the ε-constraint method, the decomposition method, and the variable neighborhood search (VNS) approaches was designed. Specifically, the bi-objective IRP was first transformed into a series of single-objective IRPs based on the framework of the ε-constraint method, and then each single-objective IRP was decomposed into two subproblems of blood product distribution and vehicle routing optimization, which could be solved by the CPLEX solver and the VNS separately. Comparisons with the non-dominated sorting genetic algorithm (NSGA-Ⅱ) and the augmented ε-constraint method (AUGMECON2) on small- and large-scale instances demonstrated the efficiency and effectiveness of the proposed model and algorithm. Finally, sensitivity analyses revealed that the proposed optimization model provides guidance on setting appropriate hospital inventory capacities under different scenarios, and shows that reducing transportation cost is an effective way to improve the efficiency of the blood supply chain (BSC).
- bi-objective optimization,
- IRP,
- ε-constraint method,
- decomposition method,
- VNS
Citation: Liu Zhao, Nengmin Wang, Yinfeng Xu, Meng Zhang. Integrated inventory routing and surgical delay for blood products: A bi-objective trade-off approach[J]. Journal of Industrial and Management Optimization, 2026, 22(12): 6124-6149. doi: 10.3934/jimo.2026206

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Abstract

Despite the large and rapidly growing global blood transfusion diagnostics market, it still cannot meet the blood demands worldwide. Medical institutions often resort to postponing or canceling elective surgeries to cope with blood shortages, which not only affects patient treatment but also increases the risk of preventable deaths. In this study, we examined a bi-objective inventory routing problem (IRP) for blood products, taking surgical delays into account. Correspondingly, a bi-objective mixed-integer programming model was developed with two objectives of minimizing the total costs and the total weighted surgical delay time simultaneously. To solve the model, a hybrid algorithm combining the ε-constraint method, the decomposition method, and the variable neighborhood search (VNS) approaches was designed. Specifically, the bi-objective IRP was first transformed into a series of single-objective IRPs based on the framework of the ε-constraint method, and then each single-objective IRP was decomposed into two subproblems of blood product distribution and vehicle routing optimization, which could be solved by the CPLEX solver and the VNS separately. Comparisons with the non-dominated sorting genetic algorithm (NSGA-Ⅱ) and the augmented ε-constraint method (AUGMECON2) on small- and large-scale instances demonstrated the efficiency and effectiveness of the proposed model and algorithm. Finally, sensitivity analyses revealed that the proposed optimization model provides guidance on setting appropriate hospital inventory capacities under different scenarios, and shows that reducing transportation cost is an effective way to improve the efficiency of the blood supply chain (BSC).

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