A multi-stage stochastic programming approach for blood supply chain planning

Zahiri, Behzad; Torabi, S. Ali; Mohammadi, Mehrdad; Aghabegloo, Mohsen

doi:10.1016/j.cie.2018.05.041

Cited by 88 publications

(45 citation statements)

References 44 publications

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“…The solution for large-scale problems in the study is provided by simulated annealing. Zahiri et al ( 2018 ), with their multi-objective mixed-integer programming model, offer a priority queue system for blood product congestion in each hospital and a new meta-heuristic solution is developed for large-scale problems. Bruno et al ( 2019 ) reposition the supply chain facilities in a region of interest.…”

Section: Literature Surveymentioning

confidence: 99%

“…Uncertainties in demand and supply in blood supply chains are handled mainly as stochastic optimization problems in Gunpinar and Centeno ( 2015 ), Ensafian et al ( 2017 ), Dillon et al ( 2017 ), Zahiri et al ( 2018 ), Hamdan and Diabat ( 2020a , b ) and as robust optimization problems in Zahiri et al ( 2015 ), Ramezanian and Behboodi ( 2017 ), Ensafian and Yaghoubi ( 2017 ), Heidari-Fathian and Pashandideh (2018), Jafarkhan and Yaghoubi ( 2018 ), Diabat et al ( 2019 ). In addition, the blood supply chain problem is in the form of fuzzy linear optimization in Zahiri and Pishvaee ( 2017 ), Eskandari-Khanghahi et al ( 2018 ), Samani and Hosseini-Motlagh ( 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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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: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…The studied supply chains are either single or two-echelon chains with centralized or decentralized configurations, a single perishable or unperishable product, and (r, S) or (s, S) policies. Zahiri et al [9] present a multi-stage stochastic program (MSSP) for a four-level blood supply network with uncertain donation and demand. The model is reformulated and solved while using a hybrid multi-objective meta-heuristic.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic Approaches to Inventory Management Optimization

Perez

Hubbs

et al. 2021

Processes

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An inventory management problem is addressed for a make-to-order supply chain that has inventory holding and/or manufacturing locations at each node. The lead times between nodes and production capacity limits are heterogeneous across the network. This study focuses on a single product, a multi-period centralized system in which a retailer is subject to an uncertain stationary consumer demand at each time period. Two sales scenarios are considered for any unfulfilled demand: backlogging or lost sales. The daily inventory replenishment requests from immediate suppliers throughout the network are modeled and optimized using three different approaches: (1) deterministic linear programming, (2) multi-stage stochastic linear programming, and (3) reinforcement learning. The performance of the three methods is compared and contrasted in terms of profit (reward), service level, and inventory profiles throughout the supply chain. The proposed optimization strategies are tested in a stochastic simulation environment that was built upon the open-source OR-Gym Python package. The results indicate that, of the three approaches, stochastic modeling yields the largest increase in profit, whereas reinforcement learning creates more balanced inventory policies that would potentially respond well to network disruptions. Furthermore, deterministic models perform well in determining dynamic reorder policies that are comparable to reinforcement learning in terms of their profitability.

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“…A lot of effort is required to collect blood for transfusion (Elalouf et al, 2018); blood donation is free and a voluntary activity in most countries. The rate of blood donation varies across different countries; for example, only 5% of the people in the United States donate blood, of which 8% do it regularly and about 62% of them never visited BCC again (Zahiri et al, 2018). In addition, an increase of 10.7 million blood donations from voluntary unpaid donors between 2008 and 2013 has been reported by the World Health Organization.…”

Section: Introductionmentioning

confidence: 99%

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

Haeri

Hosseini‐Motlagh

Samani

et al. 2019

Int Trans Operational Res

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Unlike its inherent beneficial attributes, the blood supply chain is very complex due to its uncertain nature and irregular donation besides a high vulnerability to disruptions. In this regard, this study provides a multi‐objective integrated resilient‐efficient model to design a blood supply chain network under uncertainty. To evaluate the efficiency, an augmented form of the data envelopment analysis model is incorporated into the extended model. On the other hand, to improve supply chain resilience, different resiliency measures as optimization tools are introduced and inserted into the considered network. As blood donors have a main role in the blood supply chain, this paper undertakes motivational social aspects to stimulate blood donation for reducing possible shortages. Furthermore, to handle the multi‐objective problem, an interactive fuzzy approach is utilized. Finally, to indicate the applicability of the extended model and its solution approaches, a case study is provided considering realistic data. The results revealed that simultaneously considering all three measures of efficiency, resilience, and cost can assist in improvement of blood supply chain network design and a trade‐off among these three measures can be set by decision maker's preferences.

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A multi-stage stochastic programming approach for blood supply chain planning

Cited by 88 publications

References 44 publications

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

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

Algorithmic Approaches to Inventory Management Optimization

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

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