Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design

Fazli-Khalaf, Mohamadreza; Khalilpourazari, Soheyl; Mohammadi, Mohammad

doi:10.1007/s10479-017-2729-3

Cited by 74 publications

(42 citation statements)

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“…MCDM Techniques (5): John et al 2018;Laguna-Salvado et al 2018;Venkatesh et al 2018;Malekpoor et al 2018;Mediouni et al 2018 Optimization ( 34): Anparasan and Lejeune, 2017;Baharmand et al 2017;Baidya and Bera, 2018;Bao et al 2017;Cankaya et al 2018;de Mattos et al 2018;Elluru et al 2017;Fazli-Khalaf et al 2017;Griffith et al 2017;Han et al 2018;Hoskins and Medal, 2018;Ivanov and Sokolov, 2019;Jana et al 2018;Kaur and Singh, 2016;Khalilpourazari and Khamseh, 2017;Kim, Lee and Moon, 2018;Kim, Ramkumar and Subramaniyan, 2018;Leigh et al 2017;Li and Teo, 2018;Lodree et al 2018;Nilsang et al 2018;Pyakurel et al 2018;Prasad et al 2017;Rahmani, 2018;Sabouhi et al 2018;Salehi et al 2017;Samani and Hosseni-Motlagh, 2018;Shareef et al 2018;Sharma et al 2017;Tayal and Singh, 2017;Turkes et al 2017;Yahyaei and Bozorgi-Amiri, 2018;Yang et al 2017;...…”

Section: Future Research Directionsmentioning

confidence: 99%

Disaster relief operations: past, present and future

2019

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Section: Future Research Directionsmentioning

confidence: 99%

Disaster relief operations: past, present and future

2019

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“…The changes in the supply chain after the disaster and the optimum strategies to be followed are examined in this sub-study area. Jabbarzadeh et al ( 2014 ), Fahimnia et al ( 2017 ), Samani et al ( 2018 ), Glasgow et al ( 2018 ), Diabat et al ( 2019 ), Fazli-Khalaf et al ( 2019 ), Sharma et al ( 2019 ), Salehi et al ( 2019 ), Rahmani ( 2019 ), Khalilpourazari and Khamseh ( 2019 ), Haghjoo et al ( 2020 ), Hamdan and Diabat ( 2020a , b ), Wang and Chen ( 2020 ) are current examples on this topic.…”

Section: Literature Surveymentioning

confidence: 99%

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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“…Sharma et al ( 2019 ) extended a location-allocation approach for temporary blood centers after and during the disaster in which the proposed model seeks to minimize the maximum distance with hospitals. Fazli-Khalaf et al ( 2019 ) extended a multi-objective formulation for a BSCN design in disaster with the objectives of minimizing the time of transportation and cost of considered network and maximizing the reliability of blood tested at laboratories. Khalilpourazari et al ( 2020 ) presented a BSCN design in crisis considering different transportation modes.…”

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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Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design

Cited by 74 publications

References 50 publications

Disaster relief operations: past, present and future

Disaster relief operations: past, present and future

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

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

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