A Comprehensive Mathematical Model for Designing an Organ Transplant Supply Chain Network under Uncertainty

Abstract

One of the most important issues in area of health and hygiene is location-allocation of organ harvesting centers and transplant centers according to coordination between supply and demand. In this paper, a mathematical model is presented for location-allocation of organ harvesting centers and transplant centers. The proposed model does not only minimize the present value of the total system costs, but also minimizes the geographical inequalities. The presented model is a bi-objective nonlinear mathematical programming and some of the problem parameters, such as cost, transport time and the like are associated with uncertainty and considered as fuzzy sets in the mathematical formulation. In this paper, an Organ Transplant Supply Chain (OTSC) has been designed and the ε-constraint method has been used to solve the problem and Iran is considered as a case study. The results show that the patient's family satisfaction rate is more important than the viability rate in the number of transplant operations performed and for a transplant operation to be performed, the minimum satisfaction rate () should be 0.4 and organ viability rate () should be 0.2.

Connecting the dots: uncovering the relationships between challenges confronting Africa's organ transplant supply chain systems

“…In addition, the findings emerging from this study have implications for the theory of constraints (Kim et al , 2008; Rahman, 1998; Simatupang et al , 2004), a technique used to determine the primary constraint to a system’s optimisation and then sequentially improving that constraint to the point where it is no longer the limiting factor. For instance, given that our research has used the STS theory to shed light on an inherent interaction between challenges in organ supply chains, the theory of constraints can facilitate identifying the most significant system constraints and prioritising resource allocation to effectively and efficiently provide critical transplant services to those in need.…”

“…Likewise, few studies have demonstrated how supply chain management practices improve organ transplantation by focusing on time, geographical location and allocations (Cole, 2021;Aleccia, 2020). Others have developed models to minimise organ transport time (Beliën et al, 2011) and effectively match organ demand with supply (Rabbani and Talebi, 2019). However, due to their greater emphasis on developed nations, these studies demonstrate that our understanding of organ donation and transplant supply chain issues are still limited.…”

An Overview of Fuzzy Techniques in Supply Chain Management: Bibliometrics, Methodologies, Applications and Future Directions

“…Charles et al (2019) formulated a probabilistic fuzzy SC network problem and solved it with three different approaches. Rabbani and Talebi (2019) proposed bi-objective nonlinear mathematical programming to minimize the present value of total system costs and the geographical inequalities in the location selection. Babbar and Amin (2018) proposed a mathematical model comprising a two-stage QFD and a stochastic multi-objective mathematical model to select suppliers and assign the order quantity.…”

“…Dey et al (2016) used a fuzzy decision making model to deal with the cross-docking location problem. Rabbani and Talebi (2019) established a mathematical model for locationallocation of organ harvesting centers and transplant centers. Zhao et al (2019) proposed an MCDM model considering cost, infrastructure, market, policy, technology and natural resources to solve a distribution center location problem.…”

Operating Room Scheduling Optimization Based on a Fuzzy Uncertainty Approach and Metaheuristic Algorithms

“…Health system management in the world involves different subjects such as supply chain management [42,43], data management [44,45,46], risk management [47], routing and scheduling management [48,49] and home healthcare management [50,51,52].…”