An enhanced procedure for managing blood supply chain under disruptions and uncertainties

Samani, Mohammad Reza Ghatreh; Hosseini‐Motlagh, Seyyed‐Mahdi

doi:10.1007/s10479-018-2873-4

Cited by 73 publications

(27 citation statements)

References 49 publications

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“…Rahmani [25] proposes a robust model for the BSN design by using p-criterion technique to protect the solution against the risk of disruptions. Similar works can refer to Shishebori and Babadi [26], Zahiri et al [27], Fereiduni and Shahanaghi [28], Ramezanian and Behboodi [29], Samani and Hosseini-Motlagh [30], and Kamyabniya et al [31]. These works indicate that RO can provide robust solutions to unexpected disasters.…”

Section: Literature Reviewsupporting

confidence: 66%

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Incorporating a Bayesian Network into Two-Stage Stochastic Programming for Blood Bank Location-Inventory Problem in Case of Disasters

Chen

Wang

2019

Discrete Dynamics in Nature and Society

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This paper is concerned with the optimal decisions of blood banks in a blood logistics network (BLN) with the consideration of natural disasters. One of the biggest challenges is how to deal with unexpected disasters. Our idea is to consider the disasters as the natural consequences of interaction among multiple interdependent uncertain factors, such as the locations and the levels of disasters, the number of casualties, and the availabilities of rescue facilities, which work together to influence the rescue effects of the BLN. Thus, taking earthquakes as the example, a Bayesian Network is proposed to describe such uncertainties and interdependences and, then, we incorporate it into a dedicated two-stage multi-period stochastic programming model for the BLN. The planning stage in the model focuses on blood bank location and inventory decisions. The subsequent operational stage is composed of multiple periods, some of which may suffer disasters and initiate corresponding rescue operations. Numerical tests show that the proposed approach can be efficiently applied in blood management under the complicated disaster scenarios.

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Section: Literature Reviewsupporting

confidence: 66%

“…By Central Limit Theorem, the total demand for blood product a is approximately normally distributed especially when Ω nm is big enough. Let X a be the demand for blood product a of each injured person per hour, and the mean and variance of total blood demand are explained as formulas (30) and (31):…”

Section: Bayesian Network (Bn) and Bn-based Scenario Setmentioning

confidence: 99%

Incorporating a Bayesian Network into Two-Stage Stochastic Programming for Blood Bank Location-Inventory Problem in Case of Disasters

Chen

Wang

2019

Discrete Dynamics in Nature and Society

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“…A two-phase methodology was suggested by Samani and Hosseini-Motlagh [36] for managing a blood supply chain under uncertainties and possible disruptions. They applied the fuzzy analytic hierarchy process and grey rational analysis in the first phase to find supplementary blood facilities.…”

Section: Literature Reviewmentioning

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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“…Despite the several benefits of the robust programming, it directly focuses on the worst-case analysis, and all computations are carried out based on the corresponding scenario (Bertsimas et al, 2004; Schmitt et al, 2017 ; Ivanov et al, 2019 ). On the other hand, fuzzy optimization can prevent taking some important cases into account regarding the ripple effect ( Samani and Hosseini-Motlagh, 2019 ; Özçelik et al, 2020 ). Therefore, aside from robust or fuzzy optimization, using a more comprehensive technique is essential to provide integrated operational and strategic decisions for RSC by considering most commonly encountered disruption threats at the same time through a set of scenarios.…”

Section: Introductionmentioning

confidence: 99%

Ensuring sustainability in the reverse supply chain in case of the ripple effect: A two-stage stochastic optimization model

Yılmaz

Özçelik

Yeni

2021

Journal of Cleaner Production

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The devastating impact of the ripple effect increases the importance of the reverse supply chain (RSC) design to ensure sustainability in the long-term. That being the case, in this study, a two-stage stochastic mixed-integer optimization model is proposed to design an RSC network under uncertainty sourcing from the ripple effect (i.e. external side of RSC) by considering the environmental and economic dimensions of sustainability. The environmental and economic disruptions of the ripple effect are represented by the increase in the carbon emission levels and the distance of roads, and the decrease in the capacity of facilities, respectively. Accordingly, a set of scenarios is considered based on the disruption levels (low- and high-impact) in case of the ripple effect. Furthermore, an -reliability constraint is integrated into the model to further analyze the occurrence of scenarios. The model allows us to make integrated operational and strategic decisions by placing an emphasis on the carbon emission levels (i.e. environmental dimension) and the total cost (i.e. economic dimension). To obtain some remarkable insights, the proposed model is validated through computational experiments based on data extracted from a real case. The computational results show that the ripple effect increases the emission level and total cost up to 40%. For this reason, it suggested that the regulations regarding WEEE (Waste Electrical and Electronic Equipment) should be prepared by considering sustainability in the entire RSC network. Besides, it is realized that the centralized distribution strategy leads to a more resilient RSC network design.

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An enhanced procedure for managing blood supply chain under disruptions and uncertainties

Cited by 73 publications

References 49 publications

Incorporating a Bayesian Network into Two-Stage Stochastic Programming for Blood Bank Location-Inventory Problem in Case of Disasters

Incorporating a Bayesian Network into Two-Stage Stochastic Programming for Blood Bank Location-Inventory Problem in Case of Disasters

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

Ensuring sustainability in the reverse supply chain in case of the ripple effect: A two-stage stochastic optimization model

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