مقالات رزومه دکتر مهدی یوسفی نژاد عطاری

Crisis management holds significant importance during the occurrence of disasters to ensure  maximum reduction in damages and casualties. From a crisis management perspective, emergency logistics occupy a special position because swift transportation of relief personnel and necessary supplies to the affected area, as well as the evacuation of the injured and others during and after a disaster, are amongst the critical needs. This research addresses the locating, routing, and        distribution of emergency supplies in the context of earthquakes. Objectives of this research include maximizing the probability of successfully navigating routes, minimizing emergency response costs, and reducing discrepancies in the allocation of relief personnel to affected regions. The proposed mathematical model, enhanced with an augmented epsilon constraint approach, has been solved using the GAMS software for a case study of Tehran's 11th district. The consideration of various types of injured individuals, including those with minor injuries and those in critical condition, as well as the homeless and relief workers in a concurrent manner, and the development of a three-objective, multi-commodity, multi-vehicle mathematical model with scenarios incorporating uncertainty, are among the innovations of this study.

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.

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.

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.

To locate hospitals, several points including the population of the area under the covering, transportation costs, and physical distance among the hospitals are very important. In the present study, health service networks were classi ed into two levels of low-level hospitals (provision of public health services) and high-level hospitals (providing specialized health services). In high-level hospitals, patients require professional services, and in the low-level ones, hospitals do not have the power to respond to specialized health services demands. They refer the patients to high-level hospitals in the case of patient visits or in emergency situations by ambulance. In the present case, patients are divided into two categories including the high priority (the category in which immediate service delivery is needed) and low priority. Regarding this problem, a stochastic robust dynamic mathematical model for location and allocation of health network regarding limited capacity and disturbance is developed which tries to reduce the total costs including the real features of a real problem such as limited capacity. The limited capacity of hospitals revealed that the health network needed rede nition of dierent layers for the network in the disturbance situation. In this study, we try to reduce the total costs by reducing costs of hospitals and costs such as transportation and service to patients. To solve the model, two metaheuristic algorithms including Non-dominated Sorting Genetic Algorithm II (NSGAII) and Particle Swarm Optimization (PSO) are applied. Taguchi method design is applied to minimize the cost of parameter tuning including the level of factors related to the proposed. The results demonstrated the applicability of the model to large-sized problems. For example, the total cost is minimized in conditions that are considered in the genetic algorithm, the population parameter at the highest level (150) and the intersection parameters, and the probability of mutation at the lowest level (0.7 and 0.1).

This study investigates the effect of land use change for bioenergy production by considering the cost of value added and water quality in random conditions using waste and wheat residues that have produced biofuels. Therefore, a linear model for land use change was presented with the aim of reducing total costs and increasing production efficiency. In random conditions, the model was solved using GAMS software and the computational results and comparisons show the optimal performance of the effect of land use change for biofuel production based on value added cost and water quality in different scenarios. By selecting Ajabshir city as a study area and selecting 9 different products and examining the products in terms of costs and added value of each product, we came to the conclusion that planting and producing wheat for biofuel production It is both cost-effective and cost-effective and has less of an impact on water pollution. Due to the need for less fertilizer, which leads to a lower amount of nitrate in the soil, which also reduces the percentage of water pollution during biofuel production, less damage to the environment and be economically affordable for the residents of Ajabshir city.

In this research, a novel method is proposed to optimize the costs associated with the supply chain of short-lived blood products, which is different from other existing methods. According to the novelty of this approach and the lack of familiarity with its difficulties, a scaling is applied in order to reduce the size of problem space, which can lower the accuracy of the solution. On the other hand, with regard to the problem solving dimensions, the number of solution iterations would be limited. Ultimately, after solving the above problems, the collected data are entered into the formulation of Markov’s decision-making process and solved using the successive approximation approach. The solution of this approach helps the decision-maker to choose one of (LIFO-LIFO), (FIFO-FIFO), and (LIFO, FIFO) policies. By investigating different costs and comparing various (LIFO-LIFO), (FIFO-FIFO), and (LIFO, FIFO) policies, it can be concluded over iterations that policies (LIFO-LIFO) and (LIFO -FIFO) policies have better performances than the (FIFO-FIFO) policy, and will cost much less in the long run.

In warehousing, products are collected from suppliers by incoming trucks and then transported to customers by outgoing trucks by unloading the load simultaneously. In case of accident, the timing of trucks plays an important role in the simultaneous loading unloading system. In this study, a case of simultaneous unloading in random conditions has been investigated in which the incoming truck can also be used as an output truck. This has the advantage of reducing unloading, loading and rental costs. In order to investigate this state of the problem, a mathematical model has been developed in order to obtain the appropriate answer to solve the mathematical model and to determine the validity and accuracy of modeling from gams optimization software in low and medium and high sizes that optimization software can finding the optimal answer matlab software and ant meta-algorithms of ant lion, memetic and ant colony have been used to solve the mathematical model. The results indicate the usability of the proposed proposal and the good efficiency of the anthill algorithm in solving this problem.

In this research, a novel method is proposed to optimize the costs associated with the supply chain of short-lived blood products, which is different from other existing methods. According to the novelty of this approach and the lack of familiarity with its difficulties, a scaling is applied in order to reduce the size of problem space, which can lower the accuracy of the solution. On the other hand, with regard to the problem solving dimensions, the number of solution iterations would be limited. Ultimately, after solving the above problems, the collected data are entered into the formulation of Markov’s decision-making process and solved using the successive approximation approach. The solution of this approach helps the decision-maker to choose one of (LIFO-LIFO), (FIFO-FIFO), and (LIFO, FIFO) policies. By investigating different costs and comparing various (LIFO-LIFO), (FIFO-FIFO), and (LIFO, FIFO) policies, it can be concluded over iterations that policies (LIFO-LIFO) and (LIFO -FIFO) policies have better performances than the (FIFO-FIFO) policy, and will cost much less in the long run.

The supply and demand management and planning of the blood supply chain have complexities considering the uncertainty in demand and supply. The supply uncertainty often results from the random demand as well as unpredictable and irregular behavior of the people involved in blood donation as the only source of blood supply.  In addition, the perishability and short lifespan of blood and blood products are the problems that should be considered.  Adequate and healthy blood supply plays an essential role in health systems.  Therefore, the prediction of demand to prevent endangering the patients’ health due to the inventory shortages on one hand, and increasing inventory which increases wastages and government expenses on the other hand, have attracted the attention of researchers.  Accordingly, the aim of the present study is to predict the demand for blood bank products using neural networks.  Neural networks have the ability to properly predict the future based on certain trends of the past by adjusting some parameters.  To collect the necessary data for this research, the statistics, databases and computer networks of the blood transfusion network of Zanjan province have been selected. For data analysis, the existing facilities and functions of the MATLAB software have been used. Based on previous demands of blood products, the research findings demonstrate that the best neural network model to predict demand has two delays and five neurons in the hidden layer.  The results also show that the error values in all three blood products are close to each other but have different values.

Reducing water resources and increasing demand for safe drinking water requires attention to water resources that can be returned to nature or can be used in industry or agriculture. In this regard, the use of optimal and effective methods for wastewater treatment and development is very important. In order to increase the efficiency of the wastewater treatment system and in order to reduce the pollution load of the effluent, it is very important to predict the quality of the treated effluent. In this research work, using genetic algorithm and neural network method, the effluent treatment system of Azerbaijan Pegah factory has been modeled in order to optimize the results using genetic algorithm and neural network method. The effluent treatment process should be carried out in order to anticipate the removal and disinfection of the remaining carbon materials and microbial contaminants according to the BOD5 and COD data that determine the quality of the effluent. The results show that the combination of the above two algorithms has been successful in predicting the output data compared to the actual data and there is an 87% matching of the data.

In order to reduce costs and increase the efficiency of the supply chain system, cross docking is one of the most important warehousing management strategies for combining products from different providers to different customers. Products in inbound trucks are gathered from suppliers and then are passed through customers using outbound trucks through the cross docking. Cross docking is one of the innovative product distribution strategies to minimize unnecessary inventory and improve the customer service level. Truck scheduling plays an important role in cross docking system. Cross docking works best for companies that distribute large volumes of merchandise and/or operate a large number of stores. Cross docking systems handle a large number of items in a short amount of time. The advantages of cross docking systems include increased inventory turnover, thus reduced inventory, increased customer responsiveness, and better control of the distribution operation. In this study, a special state of cross docking is considered in which inbound truck can also be used as outbound truck. The advantages of this state are reduced loading, unloading time, and reduced vehicle costs. To investigate this problem, the developed mathematical model was used to obtain the appropriate solution to solve the mathematical model and to determine the validity and accuracy of the modeling using optimization software such as GAMS and MATLAB. At small sizes to which optimization software can find the optimal solution, GAMS optimization software was used to solve the mathematical model. For medium and large sizes where optimization software cannot find the optimal solution, MATLAB software and ALO, Memetic, and ACOR algorithms were used to solve the mathematical model and the efficiency of this algorithm was tested to solve such problems. The results showed that the feasibility of the proposed approach and demonstrated good performance of the ALO algorithm in solving this problem.

In multi-layered facility location models, customers receive different services at different layers. When the customer enters the system, he must receive all services at different layers; in fact, the customer will not leave the system in the middle layers. In this study, we are seeking to provide a facility location model with multiple service layers respect to the density of the system. The proposed model is a nonlinear integer programming model and it is in the field of highly complex problems. In order to solve the mathematical model, discrete event simulation approach has been used to increase efficiency. Interactions and complexities of the system, makes it difficult or impossible to predict the performance. Simulation models are able to show variability, interactions and complexities of the system. In this regard, the demand has considered as random and objective functions consist of minimization of customer’s travel time to desired facility, customer’s waiting time in queue and the possibility of unemployment of a facility which has the highest rate of unemployment. According to the results of simulation and testing 4 different scenarios, it can be stated that in scenario (4), only by adding 1 source to each available facility in the fourth layer, which is totally increasing 4 source, costumers wait time in queue will be improved about 46%.

There have been many studies on how to model problem types, develop problem assumptions to adapt to real-world application conditions, and develop or develop problem-solving methods to obtain better results. In this paper, we aim to use single-target bat algorithm for drug distribution process in Tabriz metropolitan area in order to solve the problem of vehicle routing with time window. Due to the continuous nature of the metadata algorithm, how it is generated and decoded, a new routing solution is designed that gives the shortest transport path in the shortest possible time. In this regard, the distribution of the drug has been considered by Mohyadaru Distribution Company, a non-technical business-service company. In order to evaluate the performance of the proposed method, issues of different dimensions have been applied based on the actual issues related to drug delivery to existing pharmacies in the city of Tabriz by Mohyadaroo Company. The results show that the optimal routing is done due to the nature of the meta-algorithm optimization and the costs spent for this task are significantly reduced compared to the previous work.

One of the reasons for the high waiting time for patients in hospitals is the the lack of sufficient staff in the hospital, so the inefficiency of costs and job satisfaction of hospital nursing staff stems from the use of traditional and unscientific methods in allocating nurses to shifts. The present study is designed to determine the minimum number of nurse required according to the number of patients referred at different times, determine the shift schedule with the least required hours and schedule shifts for nurses in each shift with the lowest cost for the emergency department. The research method of the present study is of the mathematical modeling and research community, patients referring to the emergency department and nurses of a medical center. Data analysis is a combination of predictive methods, queuing theory models, and linear numerical programming. To predict the number of patients referring to the emergency, the time series method and ARIMA tools were used, and the M/M/C/K model was used to examine the queue system with limited capacity. One of the most important results of this study is to determine the maximum number of nurses available in each shift. Another result of this study is the comparison of the performance of each of the meta-heuristic Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Bee Algorithm (BA) with respect to the defined indicators.

Reducing the cost of wastage and shortage of hospitals' blood products with regard to the compatibility of blood groupsBlood supply chain management is considered one of the main components of the health system of each country. This chain consists of two-component blood collection and supply of products for blood donors has been formed. This article focuses on the issue of the supply of blood products; that tries to provide a mathematical model to reduce waste costs and a shortage of blood products to hospitals. In this model, while meeting the needs of different groups of hospitals, blood in hospitals is minimized inventory costs. A mathematical model in five hospitals affiliated Blood Transfusion Center of East Azerbaijan is implemented. With regard to the compatibility of blood groups and red cell products supply costs and inventory decreased 18% o + blood group also declined to be compatible with other blood types.Keywords: blood supply chain, RBC, compatibility, BTC

The aim of this study is a ranking of risk factors in knowledge-based companies. Risk factors in knowledge-based companies have been extracted from the different and authentic papers and after verification by experts, related questionnaire is designed. Then, using questionnaire and multi-criteria decision-making techniques by combining methods, DEMATEL, Network Analysis Process and VIKOR in a fuzzy environment marked degrees of importance of these factors is ranked. The main research tool is a questionnaire which its validity and reliability of research by experts with Cronbach's alpha coefficient was proved (0.86). The population of these research managers, knowledge-based companies in the Science and Technology Park Tabriz by using a random sampling of more than 10 active projects selected in 1394. The highest risk of external factors and financial risks affecting knowledge-based companies were in business. Indeed, knowledge-based companies of the Science and Technology Parks should be how to deal with the risk of their working agenda.

In this research, a novel method is proposed to optimize the costs associated with the supply chain of short-lived blood products, which is different from other existing methods. According to the novelty of this approach and the lack of familiarity with its difficulties, a scaling is applied in order to reduce the size of problem space, which can lower the accuracy of the solution. On the other hand, with regard to the problem solving dimensions, the number of solution iterations would be limited. Ultimately, after solving the above problems, the collected data are entered into the formulation of Markov’s decision-making process and solved using the successive approximation approach. The solution of this approach helps the decision-maker to choose one of (LIFO-LIFO), (FIFO-FIFO), and (LIFO, FIFO) policies. By investigating different costs and comparing various (LIFO-LIFO), (FIFO-FIFO), and (LIFO, FIFO) policies, it can be concluded over iterations that policies (LIFO-LIFO) and (LIFO -FIFO) policies have better performances than the (FIFO-FIFO) policy, and will cost much less in the long run.

Due to the economical restrictions, blood centers need to detect approaches to improve their efficiency. Improving efficiency in the collection and processing of blood products at blood centers can decrease costs while assuring that demand for blood products is happened. One of these approaches can be allocating resources more effectively Study designs and This study uses data envelopment analysis (DEA) to evaluate the efficiency of 31 Provincial units of Iranian Blood Transfusion Organizations (IBTO) to determine to what extent efficiency can be improved. Input variables included Number of blood collection and processing centers (BCPCs), Number of blood collection centers (BCCs), Number of mobile teams(MTs), Population density of province (population per square mile), Number of donor beds and Area of blood transfusion center (BTC). And the output variables were produced Units of Packed cells (PCs), FreshFrozen Plasma (FFP) and platelets (PLTs) for use inside province. Efficiency grades were computed
with DEA linear programming techniques and Provincial units of IBTO characteristics that important affect efficiency determined by principle components analysis (PCA). For two consecutive years, with Number of BCPCs, BCCs, MTs, Population density of province, Number of donor beds and area of BTC as inputs, technical efficiency was (range, 0.39- 1). for two consecutive years, 22 Provincial units of IBTO were efficient (efficiency, = 100%), and 9 were inefficient. Otherwise based on PCA results, Efficiency was mainly affected by Number of BCPCs, BCCs, MTs, Efficiency did not directly relate to Population density of province, Number of donor beds and area of BTC. Conclusions: The major reason of inefficiency was excess allocating resulting from a suboptimal combination of Number of BCPCs, BCCs, and MTs. Further research is required to manage factors affecting efficiency, such as the variation of demand inside and outside the provinces.

In this paper, three-machine scheduling problem in robotic production cells that producing different parts and ‎assuming the tool change has been investigated. The main issue in this article is make decisions about how to move ‎the parts by a robot between machines, the order of parts to robotic production cells as well as how to tool switch on ‎the machine is to achieve the optimum cycle time. A new approach to calculate the number of tool switching is ‎provided. Then a programming model for minimizing cycle time is presented in this problem and the results showed ‎that tool changing and parts sequencing has a direct impact on robot movement policy and cycle time. Finally, ‎numerical examples have been studied using Genetic algorithms. The results were compared with software GAMS. ‎It has been proven that GAMS does not answer for more than eight parts in a reasonable time, but examples up to ‎‎80 different parts using Genetic Algorithm reach to answer in a reasonable time.‎