A mixed resilient‐efficient approach toward blood supply chain network design

Haeri, Abdorrahman; Hosseini‐Motlagh, Seyyed‐Mahdi; Samani, Mohammad Reza Ghatreh; Rezaei, Marziehsadat

doi:10.1111/itor.12714

Cited by 58 publications

(40 citation statements)

References 38 publications

(29 reference statements)

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“…erefore, this is still a new research area and direction in this aspect. Blood supply chain studies involving all nodes at four levels and above are even fewer [33][34][35][36][37][38]. is work targets at a blood supply chain including donors, the blood donation house, the blood centre, and the hospital.…”

Section: Blood Supply Chainmentioning

confidence: 99%

Dynamic Stochastic Optimization of Emergent Blood Collection and Distribution from Supply Chain Perspective

2021

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In response to emergencies, it is critical to investigate how to deliver emergency supplies efficiently and securely to disaster-affected areas and people. There is no doubt that blood is deemed one of the vital relief supplies, and ensuring smooth blood delivery may substantially alleviate subsequent impacts caused by the disaster. Taking red blood cell products as the research object, this work proposes a four-echelon blood supply chain model. Specifically, it includes blood donors, blood donation houses, blood centres, and hospitals. Furthermore, numerical analysis is provided to test the feasibility of blood collection and distribution schemes and conduct sensitivity analysis to test the impacts of the relevant parameters (e.g., apheresis donation proportion of red blood cells (RBCs), distance between blood donors and blood facilities, and times of blood donation) on the scheme. This research provides some scientific and reasonable support for decision makers and managerial implications for emergency departments and contributes to the study of emergent blood supply chain.

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Section: Blood Supply Chainmentioning

confidence: 99%

Dynamic Stochastic Optimization of Emergent Blood Collection and Distribution from Supply Chain Perspective

2021

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“…From reviewing above papers, the subsequent conclusions could be drawn. First, while a few studies have investigated socially responsible HSND (Eskandari-Khanghahi et al, 2018;Pishvaee et al, 2014;Zahiri et al, 2017) and efficient HSND (Haeri et al, 2020a), the literature is scant on the integrated socially responsible-efficient HSN in response to the humanitarian crisis.…”

Section: Humanitarian Hsnd Problemsmentioning

confidence: 99%

“…In the case of efficient HSND, existing literature has a gap. Haeri et al ( 2020a ) provided a mixed efficient-resilient model to design a network of blood supply chain. An augmented form of DEA model was incorporated into the proposed model to determine the best sites for opening blood distribution centers, blood production centers, and blood collection centers in Iran considering three outputs of environmental actions, population density and education and culture of each region and two inputs of pollution and traffic.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated socially responsible-efficient approach toward health service network design

et al. 2021

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Despite the fact that medical responses are crucial for saving precious lives during any humanitarian crisis (e.g., the COVID-19 pandemic), healthcare infrastructure in many communities are partially covered or are not covered yet. In order to strengthen the health system response to such crisis, especially in low-to middle-income communities, this paper extends a novel multi-objective model for designing a health service network under uncertainty which simultaneously considers efficiency, social responsibility, and network cost. For efficiency, a modified data envelopment analysis model is introduced and inserted into the proposed model to decrease the inefficiency of healthcare facilities belonging to the different tiers of the health system. For social responsibility, two measures of job creation and balanced development are incorporated into the extended model. This is not only considered to cope with the increased numbers of patients and disaster victims to healthcare facilities but also to deal with the challenge of the economy and the livelihoods of people during the crisis. Moreover, a novel mixed possibilistic-flexible robust programming (MPFRP) approach is developed to protect the considered network against uncertainty. To show the applicability of the extended model, a real-world case study is presented. The results reveal that contrary to fuzzy programming models, the MPFRP performs well in terms of social responsibility (72%), cost (8%), and efficiency (28%) and is able to make a trade-off between these three measures. In this study, the resilience level of the designed network is not addressed while disregarding any short-term stoppage owing to internal or external sources of disruption in designing may bring about a considerable loss.

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“…They utilized a robustness approach which ensured the feasibility of the problem and addressed the uncertainty of the problem with a representative set of disaster scenarios. Haeri et al [45] employed a robust optimization approach for a blood supply chain network. Due to the uncertain nature and irregular donation of blood, a particular kind of two-stage stochastic-robust programming introduced by Aghezzaf et al [46] was used.…”

Section: Robust Counterpart Modelmentioning

confidence: 99%

Multi-objective robust mathematical modeling for emergency relief in disaster under uncertainty

et al. 2020

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This paper presents a robust location-allocation planning problem for emergency relief in a disaster situation, which is formulated as a robust optimization model. It is a multi-objective, multi-commodity, multi-vehicle and multi-level logistics model considering injury variety through service prioritizing for more injuries and considering unmet demand of particular item type in various damaged areas, public donation of different relief goods, using capacitated medical centers and emergency centers regarding damage type and capacitated relief distribution centers and disaster management centers. This a non-linear mixed-integer programming model that simultaneously optimizes three objectives; i.e., maximizing service fairness to damaged areas, maximizing fair commodity disaster management, and minimizing the total logistics cost. To solve such a hard problem, a non-dominated sorting genetic algorithm (NSGA-II) is developed and the Taguchi method is applied to adjust its parameters. The ε-constraint method is used for the evaluation of the proposed algorithm performance. For more accurate validation, three comparison metrics, including diversification, spacing and mean ideal distance, are used. The results verify the algorithm's effectiveness in a reasonable computational time. Eventually, to examine the applicability of the presented model and the proposed algorithm, a case study is analyzed in the area located in the north of Iran, known with historical earthquake records and aggregated active faults.

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A mixed resilient‐efficient approach toward blood supply chain network design

Cited by 58 publications

References 38 publications

Dynamic Stochastic Optimization of Emergent Blood Collection and Distribution from Supply Chain Perspective

Dynamic Stochastic Optimization of Emergent Blood Collection and Distribution from Supply Chain Perspective

An integrated socially responsible-efficient approach toward health service network design

Multi-objective robust mathematical modeling for emergency relief in disaster under uncertainty

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