فهرست مطالب taher kouchaki tajani

In this paper, we have proposed a model based on Mixed Integer Non-Linear Programming for the blood supply chain under conditions of uncertainty in supply and demand, from the stage of receiving blood from volunteers to the moment of distribution in demand centers. The challenges addressed in this optimization model are the reduction of blood supply chain costs along with minimizing the shortage and expiration rate of blood products. The Markov chain has been used to address the uncertainty of donor blood supply. To estimate the needs of medical centers, the received demand is considered fuzzy. Then, the proposed model is solved in small dimensions by GAMS software and in large dimensions by Bat and Whale meta-heuristic algorithms, and the results are presented. In addition, a case study is presented to show the applicability of the proposed model. The results show a reduction in the level of costs as well as a reduction in the shortage and expiration of blood products in the supply chain.

This paper aimed to design a robust blood supply chain model that includes the stages of collection, processing and distribution of blood and blood products taking into account the lifespan and age of demand, which seeks to reduce supply chain costs and reduce the shortage and waste of blood products.

In this paper, MINLP method is used to model the research problem and in order to face the uncertainty in the problem parameters, the MPFRP method based on fuzzy data is proposed. The designed model was first evaluated for validation with numerical examples in small and large size and using real data in a case study in GAMS software?

Using numerical examples and real data, the output indicates the performance of the proposed model. The output also had acceptable flexibility in the face of uncertainty in the parameters of the research model.

Originality/Value:

 In this study, in order to reduce the shortage of blood products in situations where blood product is not available in the same group as the requested blood product, the ABO-RH adaptability principle has been used to replace the received demand with a compatible inventory. And also, to deal with uncertainty in uncertain parameters in the supply chain a solution based onmixed possibilistic-flexible robust programming is proposed.

Artificial neural networks and fuzzy sets theory is one of appropriate techniques to solve engineering problems in order to predict variables of supply chains and, also, of systems with high complexity and implicitly which provide no sufficient data. The main advantage of this technique over others, which lies in the short time of data examination and algorithm discovery, is in the line with that prediction and/or its influence on the future. Adaptive neuro-fuzzy inference system (ANFIS) combines neural networks with fuzzy logic concepts and is able to use capabilities of both in one framework, the inference system of which is in conformity to fuzzy "if-then" rules having potential for learning how to approximate non-linear functions. Among applications of this technique are to define variables based on the past data and their impacts on the past temporal sequences in order to predict future conditions. This research, therefore, uses neuro-fuzzy technique in order to blood donation based on data from the past years. Since each technique has its own error rates, Metaheuristic Whale Algorithm is used to reduce errors of ANFIS by improving the parameter values of neuro-fuzzy systems. The obtained results show a reduction of the RMSE of prediction from 0.00261 to 0.00153 in the ANFIS-WOA and a 41% improvement over the ANFIS method.

Neglecting the supply chain management of perishable goods could create a lot of costs for organizations and companies. Blood is a perishable product in the healthcare supply chain, the  shortage of which could prove quite problematic and disastrous. Any improvements in the blood supply chain management operations may increase service efficiency and decrease the cost of the healthcare system, saving the lives of lots of people. In this paper, a mixed-integer nonlinear programming model is proposed for comprehensive blood supply chain management, which includes gathering, processing and distributing blood and blood products by taking into account the demand lifetime and age. This model aims at decreasing supply chain costs and blood product deficiency. Robust optimization is utilized to take into account the inherent uncertainty and volatility of the demand and supply. The proposed model is first tested on a small-scale numerical example in GAMS software. Then a large-scale problem is solved using Whale and Imperialist Competition algorithms and the results are compared. In addition, a case study is presented to show the applicability of the proposed model.