Blood supply chain configuration and optimization under the COVID-19 using benders decomposition based heuristic algorithm

AbstractDuring COVID-19, blood demand exceeded pre-pandemic levels due to reduced donations, causing shortages. Given the severe shortage, it's crucial to optimise blood use, prevent shortages, minimise wastage, and reduce unnecessary transfusions in all hospitalised patients. Designing a reliable blood supply chain network (BSCN) is an effective solution, especially for COVID-19 patients. This strategic decision significantly impacts emergency management performance. An efficient and reliable blood supply chain requires the consideration of multiple factors, including scarceness and perishability of blood, simultaneously. However, existing studies have not addressed all relevant factors in an integrated blood supply chain, and this paper aims to bridge this gap. Furthermore, an efficient Benders Decomposition based heuristic approach is proposed to solve the model. The solution approach has been compared with a set of commonly used meta-heuristic algorithms, including the red deer algorithm (RDA), tree growth algorithm (TGA), and genetic algorithm (GA). The outcomes illustrate that the proposed heuristic approach can solve small and large-size problems in significantly less CPU time than the other proposed solution approaches. For large-size problems, it can reduce the average CPU time by about 80% compared to TGA, about 80% compared to GA, and about 83% compared to RDA. A real case study has been implemented to validate the proposed mathematical model and solution method. The sensitivity analysis has been conducted to validate the significance of the model's parameters; consequently, several managerial insights have been derived.KEYWORDS: Supply chain managementCOVID-19Heuristic/meta-heuristic algorithmsBenders decomposition algorithm Data Availability StatementThe authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsOmid AbdolazimiOmid Abdolazimi received his MSc degree in Industrial Engineering from the School of Engineering at Kharazmi University in 2018. His current research interests include logistics and supply chain management and robust optimisation. He has published papers in international journals, including the Journal of Cleaner Production, Neural Computing and Applications, and the like. Now, he is a Ph.D. student at Mississippi State University in the USA. In his Ph.D. study, his research focus is on operations research principles and implementation-related research. He will participate in vessel-drone multi-modal transportation network development and optimisation and truck-drone-related disaster management.Mir Saman PishvaeeMir Saman Pishvaee received his Ph.D. in Industrial Engineering from the University of Tehran and is an Associate Professor at the Iran University of Science and Technology (IUST). He has published over 120 papers in various journals such as Energy, Renewable Energy, Omega, Transportation Research: Part E (TRE), and several book chapters under Springer-Verlag. His research areas are supply chain management, robust optimisation, and system dynamics. Notably, he was among the top 1 percent of researchers (engineering area) from 2017 to 2019 based on the ISI-ESI report.Mohammad ShafieeMohammad Shafiee received his MSc degree in Industrial Engineering at Yazd University in 2021. His research interests include Supply Chain Management, Operations Research, Scheduling, and Data-Driven Optimization. He has published papers in international journals, including the Journal of the Operational Research Society, the International Journal of Production Economics, and Transportation Research: Part E (TRE). Moreover, he is an ad hoc reviewer for several journals, such as the European Journal of Operational Research, the International Journal of Production Economics, and the International Journal of Production Research.Davood ShisheboriDavood Shishebori is presently a professor and the head of the Industrial Engineering department at Yazd University simultaneously. He got his Ph.D. from the Iran University of Science and Technology (IUST). His research interests are Supply Chain Management, Operations Research, and Facility location. So far, he has published decent papers in journals like Transportation Research: Part E (TRE), Journal of Cleaner Production, Neural Computing and Applications, and the like.Junfeng MaJunfeng Ma earned his dual title Ph.D. in Industrial Engineering and Operations Research from Pennsylvania State University in 2016. He is currently associate professor in Department of Industrial and Systems Engineering at Mississippi State University. His research primarily locates on applied operations research and data analytics with applications in complex system design, including sustainable logistic system design, human-technology teaming, and manufacturing system design. His research has been supported by multiple agencies in U.S., such as NSF, DOE, EPA, DOT, DOL, USDA and industries. He has published over 100 peer reviewed journals and conference proceedings, and received multiple best papers awards, such as IISE D&M track best paper, IISE FDP track best paper and two ASME-DFMLC scholar awards. He is the vice chair of Technical Committee of Design for Manufacturing and the Life Cycle (DFMLC) Conference in IDETC/CIE 2024. He is an active member of Institute of Industrial and Systems Engineers (IISE), The American Society of Mechanical Engineers (ASME), INFORMS, and the American Society for Engineering Education (ASEE).Sarah EntezariSarah Entezari received her MSc degree in Industrial Engineering from Industrial Engineering department at Yazd University in 2019. Her research interests include logistics and supply chain management, disaster management, transportation, and robust optimisation. She has submitted papers in international journals, including the Journal of Computers and Industrial Engineering and the Journal of Clean Technologies and Environmental Policy.