A fuzzy optimization approach to strategic organ transplantation network design problem: A real case study

Abstract

Designing an efficient supply chain for organ transplant networks which is intimately related to humans’ life plays a primary role in improving the network’s performance. This research is focused on proposing a new multi-period location-allocation modeling approach to make appropriate strategic decisions for designing organ transplant networks under supply and budget uncertainties. To serve this purpose, a bi-objective possibilistic programming model is formulated the aim of which is to maximize network responsiveness and minimize the total cost. A fuzzy goal programming approach is adopted to solve multiple objective function models and control their deviations from the corresponding aspiration levels. As an important contribution of this study, the chance of success of transplantation processes is taken into consideration by proposing appropriate utility functions according to transportation criteria. Moreover, for the purpose of coping with the inherent uncertainty of the input parameters, a possibilistic programming model based on Me measure converted to three optimistic, realistic and pessimistic models is developed. Three new formulations have also been developed to tackle equality chance constraints. Finally, the optimal solutions of the developed models are analyzed through conducting a real case study in Iran. According to the results, for the considered organ transplant network, the possibilistic programming model based on the realistic measure is better than the optimistic and pessimistic measure in most confidence levels.

Location of organ procurement and distribution organisation decisions and their impact on kidney allocations: a developing country perspective

“…Under uncertainty, the stochastic model results are similar to deterministic models with the centralization of facilities for congested areas. For sparse areas, one stochastic model yields reliable results with more TCs accessible to patients Aghazadeh et al ( 2017 ) Problem: Design of transplant networks including clinical factors; Objective: Reduce total cost, maximize the number of expected donors and minimize total organ shipping time; Method: Multi-objective MILP; Organ: Multiple; Factors: Clinical and Non-clinical Total organ shipping times exhibit more sensitivity to parameters followed by the number of expected donors; Total cost of transplant is the least sensitive of all Hodgson & Jacobsen ( 2009 ) Problem: Capturing Irrational behavior of recipients in location-allocation design; Objective: Minimize the negative effect of irrational behavior (patrons traveling to farther facilities termed as irrational); Method: Hierarchical P-median model; Organ: None specified; Factors: Non-clinical Modeling the irrational behavior of recipients results in the total distance traveled by recipients increasing slightly Rouhani et al ( 2021 ) Problem: Organ transplantation network design; Objective: Maximize network responsiveness while minimizing total cost; Method: Possibilistic programming; Organs: Multiple; Factors: Clinical and Non-clinical Among the three possibilistic programming methods proposed, the realistic approximation method provides better solutions …”

“…Recently, Rouhani et al ( 2021 ) proposed a bi-objective model for organ transplant network design, focusing on maximizing network responsiveness while minimizing the total cost of transplantation with due consideration of clinical and non-clinical factors. These studies examine the impact on equity or clinical efficiency in a multi-organ context.…”

Organ Transplantation: A Dual Supply Chain Perspective

“…For decades, the widening gap between organ supply and need (demand) has created a major global organ shortage, complicating transplant operations worldwide (Mamzer-Bruneel & Hervé, 2016). As supply often does not meet demand, the gap between demand and supply widens and keeps organ seekers on long waiting lists, leading to the deaths of many every year (Rouhani et al, 2021). In addition, when the organ is available, organ removal and transplant surgery must be carried out under severe time constraint (Genc, 2008), the constraint being understood as "a factor that limits the performance of a system" (McCleskey, 2020).…”

“…Thus‚ quite naturally, academic research has focused on improving the transplant supply chain (Zahiri et al, 2014), seeking to optimise all the time-consuming points. Savaser et al (2018) propose a model maximising potential compatible organ donor-recipient pairings within the time limits of ischemia, while other works are focused on issues related to hospital location and allocation and transplant centres (Beliën et al, 2012;Syam & Côté, 2012;Rouhani et al, 2021) 2019) study the optimisation of the organ transportation mode (rail and air) as well as the medical team transportation between hospitals and transplant centres. Furthermore, while some of the individuals on the organ waiting list die while waiting for the organ they need, Kempf et al (2005) show that the supply chain coordination between transplant centres and hospitals can allow an increase in the number of pancreatic islet transplant recipients thanks to the increase in organ donor pools, the optimised allocation of islet grafts and maintaining a high rate of pancreas tenders.…”

Minimizing operating expenditures for a manufacturing system featuring quality reassurances, probabilistic failures, overtime, and outsourcing

“…The researchers estimated system reliability regarding the expected number of failures and their occurring probabilities, time, and distances between failures. Other research (Youssef & ElMaraghy, 2008;Iqbal, 2020;Mabrouk, 2020;Chiu et al, 2021a,b;Dewi et al, 2021;Di Nardo et al, 2021;Patil et al, 2021;Rouhani et al, 2021;Gupta et al, 2022;Kahar et al, 2022;Kaviyarasu and Sivakumar, 2022) studied diverse characteristics of equipment failures' impact and their corresponding actions of product defects on the controlling and managing fabrication systems.…”