Postdisaster Volatility of Blood Donations in an Unsteady Blood Supply Chain*

Hosseinifard, Zahra; Abbasi, Babak; Fadaki, Masih; Clay, Nigel M.

doi:10.1111/deci.12381

Cited by 17 publications

(14 citation statements)

References 56 publications

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“…Inventory management for the flow of blood and/or blood products in the supply chain is another important subject study in the literature. Ensafian et al ( 2017 ), Ensafian and Yaghoubi ( 2017 ), Osorio et al ( 2017 ), Hosseinifard and Abbasi ( 2018 ), Clay et al ( 2018 ), Hosseinifard et al ( 2020 ) can be shown as recent examples. In addition, Ghandforoush and Sen ( 2010 ), Şahinyazan et al ( 2015 ), Blake et al ( 2015 ), Gunpinar and Centeno ( 2016 ), Elalouf et al ( 2018 ) focus on the logistics of blood products in the supply chain.…”

Section: Literature Surveymentioning

confidence: 96%

Re-design of a blood supply chain organization with mobile units

2021

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This research analyses the re-organization of a blood supply chain organization. Blood supply chain network design is a hard problem. Uncertainties of the blood supply and demand, perishability of blood over time and compatibility of blood types are some factors that make the problem difficult. This paper presents a novel multi-objective mixed-integer location-allocation model for a blood supply chain design problem. Unlike many studies on blood supply chain design in the literature, supply chain network consisting of mobile and permanent units is planned together effectively with our mixed-integer programming model. Multi-objective structure of the model minimizes distances between the blood supply chain elements and the length of the mobile unit routes. The objectives are prioritized by experts using the Analytical Hierarchical Process. Finally, the model is implemented on a real life case study using real data from the Eastern Anatolia region of Turkey for various supply demand scenarios. The solutions offered by the model are compared with the current situation in the region. It is shown that the proposed model gives at least %25 more effective solutions. Moreover, sensitivity analysis on the budget constraint is conducted, and robustness of the model is empirically illustrated.

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Section: Literature Surveymentioning

confidence: 96%

Re-design of a blood supply chain organization with mobile units

2021

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“…Recently, quantitative models have been used to study aspects of the blood supply chain in disaster relief situations which make use of multi-objective programming (Samani et al, 2018), robust optimisation (Jabbarzadeh et al, 2014) and Markov chains (Hosseinifard et al, 2019). Other supply chain approaches include mathematical optimisation models for finding appropriate delivery routes for blood supplies (Ezugwu et al, 2019;Kazemi et al, 2017).…”

Section: Quantitative Modelsmentioning

confidence: 99%

“…In addition, the model in (Perry and Posner, 1990) can only deal with one or two switch-over levels, which limits the generalisation to more complex scenarios with a large number of input or output rates to choose from, each with its own characteristics in terms of costs incurred. In contrast, blood inventory optimisation models typically consider two dichotomous situations: normal and emergency (e.g., Kopach et al, 2008;Hosseinifard et al, 2019). Closed-form solutions are usually obtained with a view to maintaining the blood supply at an operationally satisfactory level.…”

Section: Quantitative Modelsmentioning

confidence: 99%

“…In this paper, we model blood inventory as a Markov decision process -MDP. MDPs are designed for sequential decision making under uncertainty (Puterman, 2014), hence their frequent use in medical applications (e.g., Andersen et al, 2019;Arruda et al, 2019;Steimle and Denton, 2017;Bennett and Hauser, 2013;Alagoz et al, 2010) and in blood inventory problems (e.g., Attari et al, 2019b;Hosseinifard et al, 2019). The use of an MDP ensures more flexibility as it allows an optimal decision for each available state, while also ensuring optimality in the long run.…”

Section: Mathematical Formulationmentioning

confidence: 99%

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Optimisation and control of the supply of blood bags in hemotherapic centres via Markov decision process with discounted arrival rate

Soares

Arruda

Bahiense

et al. 2020

Artificial Intelligence in Medicine

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Running a cost-effective human blood transfusion supply chain challenges decision makers in blood services world-wide. In this paper, we develop a Markov decision process with the objective of minimising the overall costs of internal and external collections, storing, producing and disposing of blood bags, whilst explicitly considering the probability that a donated blog bag will perish before demanded. The model finds an optimal policy to collect additional bags based on the number of bags in stock rather than using information about the age of the oldest item. Using data from the literature, we validate our model and carry out a case study based on data from a large blood supplier in South America. The study helped achieve an overall increase of 4.5% in blood donations in one year.

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“…Keeping the balance between shortage and outdate of blood is the major challenge related to the management of the blood inventory at hospitals [41]. Due to the perishable nature of blood, having an excessive number of blood units in inventory would inevitably increase wastage [20].…”

Section: Introductionmentioning

confidence: 99%

Proactive transshipment in the blood supply chain: A stochastic programming approach

Dehghani

Abbasi

Oliveira

2021

Omega

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The main objective when managing inventories in blood supply chains is to establish an efficient balance between the wastage and shortage of blood units. The uncertain demand and the perishable nature of blood units can result in over-or under-stocking and increase wastage and shortage costs. In this study, we analyze how a proactive transshipment policy can avoid future shortages in addition to mitigate wastage. We consider a network of hospitals with uncertain demand in which each hospital makes decisions on the quantity to order from a central blood bank and to transship to other hospitals in each review period. We formulate the problem as a two-stage stochastic programming model. To generate scenarios, the Quasi-Monte Carlo sampling approach is employed and the optimal number of scenarios is determined by conducting stability tests. We performed numerical experiments to evaluate the performance of the proposed model and investigate its potential benefits of the outlined proactive transshipment. The developed model is used to compare the optimized policy with the current practice in some hospitals in Australia and with a no-transshipment policy. The numerical results indicate significant potential cost savings in comparison with the current policy in use and the no-transshipment policy.

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Postdisaster Volatility of Blood Donations in an Unsteady Blood Supply Chain*

Cited by 17 publications

References 56 publications

Re-design of a blood supply chain organization with mobile units

Re-design of a blood supply chain organization with mobile units

Optimisation and control of the supply of blood bags in hemotherapic centres via Markov decision process with discounted arrival rate

Proactive transshipment in the blood supply chain: A stochastic programming approach

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