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

IF 7 2区工程技术 Q1 ENGINEERING, INDUSTRIAL

International Journal of Production Research Pub Date : 2023-09-29 DOI:10.1080/00207543.2023.2263088

Omid Abdolazimi, Mir Saman Pishvaee, Mohammad Shafiee, Davood Shishebori, Junfeng Ma, Sarah Entezari

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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. 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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. 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引用次数: 0

Abstract

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.

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基于弯曲分解的启发式算法的COVID-19下血液供应链配置与优化

摘要COVID-19期间，由于献血减少，血液需求超过了大流行前的水平，造成短缺。鉴于严重短缺，优化血液使用，防止短缺，最大限度地减少浪费，并减少所有住院患者的不必要输血至关重要。设计一个可靠的血液供应链网络(BSCN)是有效的解决方案，特别是对COVID-19患者。这一战略决策显著影响应急管理绩效。高效可靠的血液供应链需要同时考虑多种因素，包括血液的稀缺和易腐性。然而，现有的研究并没有解决整合血液供应链中的所有相关因素，本文旨在弥补这一差距。在此基础上，提出了一种有效的基于Benders分解的启发式求解方法。将该方法与一组常用的元启发式算法进行了比较，包括马鹿算法(RDA)、树生长算法(TGA)和遗传算法(GA)。结果表明，与其他提出的解决方案相比，所提出的启发式方法可以在更少的CPU时间内解决小型和大型问题。对于大型问题，它可以将平均CPU时间比TGA减少约80%，比GA减少约80%，比RDA减少约83%。通过实例验证了所提出的数学模型和求解方法。进行敏感性分析，验证模型参数的显著性;因此，得出了一些管理见解。关键词:供应链管理covid -19启发式/元启发式算法benders分解算法数据可用性声明作者确认在文章及其补充材料中可以获得支持本研究结果的数据。披露声明作者未报告潜在的利益冲突。omid Abdolazimi于2018年获得哈拉兹米大学工程学院工业工程硕士学位。他目前的研究兴趣包括物流和供应链管理以及稳健优化。他曾在国际期刊上发表论文，包括《清洁生产杂志》、《神经计算与应用》等。现在，他是美国密西西比州立大学的博士生。在博士学习期间，他的研究重点是运筹学原理和实施相关的研究。他将参与船舶-无人机多式联运网络的开发和优化以及卡车-无人机相关的灾害管理。Mir Saman Pishvaee在德黑兰大学获得工业工程博士学位，现为伊朗科技大学(IUST)副教授。他在各种期刊上发表了120多篇论文，如能源，可再生能源，欧米茄，运输研究:E部分(TRE)，以及施普林格出版社的几本书章节。他的研究领域是供应链管理、稳健优化和系统动力学。值得注意的是，根据ISI-ESI报告，他在2017年至2019年的研究人员(工程领域)中排名前1%。Mohammad Shafiee于2021年在亚兹德大学获得工业工程硕士学位。他的研究兴趣包括供应链管理、运筹学、调度和数据驱动优化。他曾在国际期刊上发表论文，包括《运筹学学会杂志》、《国际生产经济学杂志》和《运输研究:E部分》。此外，他还是《欧洲运筹学杂志》、《国际生产经济学杂志》和《国际生产研究杂志》等多家期刊的特约审稿人。Davood Shishebori目前是亚兹德大学工业工程系的教授兼系主任。他在伊朗科技大学(IUST)获得博士学位。他的研究兴趣是供应链管理、运筹学和设施选址。到目前为止，他已经在《交通研究:E部分》、《清洁生产杂志》、《神经计算与应用》等杂志上发表了一些不错的论文。马俊峰，2016年获得宾夕法尼亚州立大学工业工程与运筹学博士学位。他目前是密西西比州立大学工业与系统工程系的副教授。他的研究主要集中在应用运筹学和数据分析在复杂系统设计中的应用，包括可持续物流系统设计、人类技术团队和制造系统设计。他的研究得到了美国多个机构的支持，如NSF, DOE, EPA, DOT, DOL, USDA和行业。他发表了100多篇同行评议期刊和会议论文集，并获得了多个最佳论文奖，如IISE D&M轨道最佳论文，IISE FDP轨道最佳论文和两次ASME-DFMLC学者奖。他是IDETC/CIE 2024年制造和生命周期设计(DFMLC)会议技术委员会副主席。他是工业与系统工程师学会(IISE)、美国机械工程师学会(ASME)、INFORMS和美国工程教育学会(ASEE)的活跃成员。Sarah Entezari于2019年获得亚兹德大学工业工程系工业工程硕士学位。她的研究兴趣包括物流和供应链管理、灾害管理、运输和稳健优化。她曾在国际期刊上发表论文，包括《计算机与工业工程杂志》和《清洁技术与环境政策杂志》。

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来源期刊

International Journal of Production Research 管理科学-工程：工业

CiteScore

19.20

自引率

14.10%

发文量

318

审稿时长

6.3 months

期刊介绍： The International Journal of Production Research (IJPR), published since 1961, is a well-established, highly successful and leading journal reporting manufacturing, production and operations management research. IJPR is published 24 times a year and includes papers on innovation management, design of products, manufacturing processes, production and logistics systems. Production economics, the essential behaviour of production resources and systems as well as the complex decision problems that arise in design, management and control of production and logistics systems are considered. IJPR is a journal for researchers and professors in mechanical engineering, industrial and systems engineering, operations research and management science, and business. It is also an informative reference for industrial managers looking to improve the efficiency and effectiveness of their production systems.