Optimization in science and engineering
Elham Nejati; Mahdi Yousefi Nejad Attari; Asgar Hajibadali
Abstract
Purpose: One of the most vital subcategories of the health care system is organ transplantation, and since organ transplant centers deal directly with surgical operations and, as a result, human lives, the importance of this issue has received more attention. One of the major differences between the ...
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Purpose: One of the most vital subcategories of the health care system is organ transplantation, and since organ transplant centers deal directly with surgical operations and, as a result, human lives, the importance of this issue has received more attention. One of the major differences between the organ transplant supply chain and other supply chains is the possibility of corruption of related products. Therefore, the time and also the location of organ transplant centers are of special importance. On the other hand, due to the rapid growth of the demand for organ transplantation and the lack of resources, the patient's waiting time to complete the transplantation process plays a vital role in the organ transplantation system.Methodology: This study presents a robust bi-objective mathematical model for the location problem of allocating organ transplant centers under uncertainty, which includes the total costs of the organ transplant system as well as the average patient waiting time for organ transplantation, which follows a G/G/m queuing system.Findings: To solve this model, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) has been used. Finally, the applicability of this model and the efficiency of the mentioned algorithm compared to the defined indicators have been shown through numerical experiments.Originality/Value: Since each organ can spend a certain amount of time outside the body and there is a possibility of corruption or a decrease in the quality of the transplant, the time between the organ leaving the body and the completion of the transplant operation plays an essential role in the transplant system.
meta-heuristic algorithms
Vida Karbasi bonab; Mahdi Yousefi Nejad Attari; Ensiyeh Neishabouri
Abstract
Vendor managed inventory (VMI) is one of the popular strategies to manage inventory control system, in this strategy, the vendor is responsible for controlling and replenishment the inventory of retailers. In this paper, a bi-objective vendor managed inventory (BOVMI) model with fuzzy demand was investigated ...
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Vendor managed inventory (VMI) is one of the popular strategies to manage inventory control system, in this strategy, the vendor is responsible for controlling and replenishment the inventory of retailers. In this paper, a bi-objective vendor managed inventory (BOVMI) model with fuzzy demand was investigated for a supply chain problem with multiple vendors and retailers, the fuzzy demand is formulated using trapezoidal fuzzy number (TrFN) where the centroid defuzzification method is employed to defuzzify fuzzy output functions. The vendor confronts two constraints: number of orders and available budget and minimizing the total inventory cost and optimizing the warehouse space are the two objectives of the model. Since the proposed model is formulated ino a bi-objective integer nonlinear programming (INLP) problem, an non-dominated Sorting genetic algorithm-II (NSGA-II) has been developed to find Pareto front solution. To improve the performance of algorithm has been calibrated using Taguchi method. Finally, conclusions are made and future research works are recommended.