چكيده به لاتين
Organs are considered as valuable national resources due to their significanct role in saving patients’ lives. Hence planning for organ transplantation network in different strategic, tactical and operational levels is one of the most important issues in healthcare management sciences. Efficient and fair allocation of organs is one of the most sophisticated decision problems in the operational planning level. The present study proposes a two stage patients’ ranking and organ allocation model that takes into account not only medical criteria but also nonmedical ones (e.g., transportation time, transportation cost, etc.) in ranking patients unlike models developed so far. The proposed method is also the first allocation method which is able to tackle the inherent uncertainties in medical and logistical parameters of the allocation problem. In the first stage, patients are ranked according to their efficiency scores calculated by the use of a credibility-based fuzzy common weights DEA method. In the second phase, a bi-objective integer programming model under mixed uncertaintiy of input logistical data is solved with the aid of improved augmented ε-constraint method. The second stage model outputs best allocations, transplant centers and transportation modes by maximizing total efficiency and minimizing network costs. In order to approve the applicability and validation of the presented model, it is applied to a real dataset comprising kidney, liver and heart which is adopted from Iran’s organ transplantation network. The obtained results from proposed method are evaluated and compared with those obtained from the current IRNOPT’s allocation method. Our method improves the average transportation time, average transportation cost and average efficiency of chosen pairs by 31%, 57.2% and 24.4% respectively. It also makes a remarkable improvement in the value of medical transplantation criteria and consequently makes a rational tradeoff between involved equity and efficiency measures.
Keywords: Organ allocation, DEA, Fuzzy mathematical programming, Random fuzzy variables, Improved augmented ε-constraint method.
