چكيده به لاتين
Abstract: Today working alone on one field is not enough, and reaching good and worthy results requires the cooperation of several sciences. Health care supply chain is one of these sciences that links engineering and health care sciences. Despite the high importance and sensitivity of linking organs in the health supply chain, it does not receive significant attention in the literature. The importance of organ transplantation is important because in many cases it is the only way to treat chronic and even fatal diseases. There are many decisions in the organ transplant network, organ allocation is one of the most important and controversial decisions due to the high demand of organs. This research presents a mathematical model after stating a series of generalities, reviewing the literature on the subject of this research, categorizing them and extracting research gaps. With the aim of having a more effective system, it proposes a new non-linear mixed integer multi-objective mathematical model for allocation in the organ transplant network, which is one of the important fields in health and treatment supply chain. The first objective function tries to reduce the costs of the organ allocation process, and the second objective minimizes the average waiting time of each patient and tries to establish fairness in transplantation. The proposed model is about choosing the type of transportation (ground, air or drone transportation), choosing the type of allocation (local or regional), eligible pairs, choosing the liver transfer policy in the form of extracorporeal donor liver or cadaver and finally choosing the right transplant center for the act of grafting makes a decision. In this study, we will first present the mathematical model in deterministic form and then present it in non-deterministic form. Any time loss or time disruption, such as helicopter flight delay, heavy traffic on ground routes, weather changes and other cases will lead to uncertainty and loss of time and as a result lead to loss of membership. Due to the importance of uncertainty control in the member connection network, Bertsimas and Sim's stabilization approach has been used in this research. Due to the multi-objectiveness of the mathematical model the normalized normal constraint (NNC) method has been used to solve the proposed multi-objective optimization problem and find Pareto optimal solutions. In the following, a numerical example in a small size and with random data is considered to measure the accuracy and overall performance of the proposed model and the solution method, after that the model is implemented on the real events of Iran and the member of the liver. The results obtained and its comparison with the current policies of Iran show that by applying this model and the policies presented in this research, it brings a significant improvement in the costs, the waiting time of the patients, as well as the reduction of the patients on the waiting list due to the better use of the available livers. Finally, by changing different parameters of the model, the sensitivity analysis of the introduced indicators has been done to analyze the behavior of the model. Keywords: Organ transplantation, Liver, Geographical Disorders, Uncertain Programming, Allocation, Prioritization
