A location-allocation model for quality-based blood supply chain under IER uncertainty

Abstract

Providing blood with high quality at the lowest cost and the shortest time is main challenge of blood supply chain management. This paper presents a new model for designing a dynamic and three level blood supply chain incorporating the quality issues. The proposed model intends to locate facilities, and to determine the best strategy for blood allocation by minimizing both cost and time and maximizing the customer satisfaction based on quality of blood delivery. In order to deal with consideration of real world, intricacies such as blood freshness, both separation and apheresis extraction methods, Cross match to Transfusion ratio (C/T) and equipment failure have been involved. Also, Interval Evidential Reasoning (IER) approach is applied to handle the uncertainty of blood product demand. Since the proposed model is NP-hard, MOPSO and NSGAII algorithms are utilized to solve it. Finally, to demonstrate the applicability of the problem some numerical examples are designed in different sizes and the most favorable algorithm is determined using TOPSIS method.

Blood supply chain network design: a systematic review of literature and implications for future research

“…It has also been observed that there are many studies devoted solely to the distribution of the platelets product. Besides, some studies follow the strategy of collecting blood products directly by apheresis (Ghatreh Samani et al , 2020; Ghatreh Samani and Hosseini-Motlagh, 2021a; Haeri et al , 2020; Hosseini-Motlagh et al , 2020a; Jemai and Sarkar, 2019; Jin et al , 2021; Larimi and Yaghoubi, 2019; Moslemi and Pasandideh, 2021; Osorio et al , 2018b). Apheresis is used to draw out only the needed components (platelets and red blood cells).…”

“…The researchers use some metrics to measure Pareto's front quality. These are: quality metric, divergence metric, spacing metric, mean ideal distance metric, convergence metric, diversity, hypervolume, CPU-time, lexicographic weighted Tchebycheff, multiobjective coefficient of variation and number of Pareto Blood supply chain network design solution (Behroozi et al, 2021;Eskandari-Khanghahi et al, 2018;Ghorashi et al, 2020;Khalilpourazari et al, 2020;Moslemi and Pasandideh, 2021;Mousavi et al, 2021;Shirazi et al, 2021;Zahiri et al, 2018). For converting multiobjective models to single objective models, the researchers mostly prefer the «-constraint method.…”