سید محمدعلی خاتمی فیروز آبادی

The flexible job shop system is one of the most widely used scheduling systems in production environments, consistently attracting researchers' attention due to its diverse applications. Many studies in this field assume fixed and predetermined processing times. However, processing times can increase due to the deterioration effect, and after implementing rate-modifying activities (RMA), these times return to their original values. This study examines the flexible job shop scheduling system, considering job rejection policies, dual resource constraints (human and machine), and RMA maintenance activities.

The objective of flexible job shop scheduling is to assign each operation to a machine and a worker from a set of eligible machines and workers in a way that optimizes the sequence of operations on the machines. A mathematical model based on the mixed-integer linear programming approach was developed for this purpose. Literature review classifies the problem with the stated assumptions as NP-hard, making the use of meta-heuristic methods essential for finding near-optimal solutions. Thus, Variable Neighborhood Search (VNS), Simulated Annealing (SA), and a combined VNS-SA algorithm were employed to solve the problem.

Twenty sub-problems were analyzed, categorized into small, medium, and large-sized problems. The characteristics of each problem were defined by parameters such as the number of jobs, machines, workers, total operations, and buckets. Meta-heuristic methods, including VNS, SA, and their combination, were utilized to solve the problem. Seven neighborhood structures based on changes in assigned machines and workers, operation and job replacements, execution of RMA activities, and job acceptance/rejection were developed to enhance solution space exploration. The solution generation structure ensures feasibility within the flexible job shop system's requirements. The parameters of the meta-heuristic methods were tuned using the Taguchi method. Parameters related to the combined VNS-SA algorithm, such as initial temperature, number of neighborhood searches, and shake procedure counter, were reported. The results of the meta-heuristic methods were compared, and for small-sized problems, they were also compared with exact solutions.

The results of the twenty sub-problems solved using the three meta-heuristic approaches were compared statistically. The combined method of simulated annealing and variable neighborhood search showed superior performance in solving the problem.

The increased traffic has been followed by many problems in metropolitans, the key of which is air pollution and excessive fuel consumption. Paying attention to public transportation, particularly the bus rapid transit (BRT) system is one of the measures that may be taken, since besides reducing social expenses, it may be very effective in declining air pollution. The main objective of the present research is to study the fuel consumption rate and the emissions rate of various air pollutants including CO2, CH4, and N2O gases in various scenarios of BRT system.

Since traffic and congestion phenomena are complex and dynamic, it is very difficult and sometimes impossible to model them with common mathematical models. To this end, agent-based technologies, highly compatible with these characteristics, can be utilized. In the current research, BRT system’s performance, the fuel consumption rate, and the amount of air pollutants production are estimated using agent-based modeling. This study emphasizes what changes should be made in effective parameters such as bus speed and bus stop time at stations, as well as bus dispatch timing in order to control fuel consumption and reduce pollution factors. This research uses NetLogo software to code the model and run its simulation and considers three different scenarios in line one of BRT system in Tehran (Iran).

following the analysis and comparison of different scenarios, suggestions are made to decline fuel consumption and air pollutants, such as minor changes in the parameters of bus stop times at stations as well as changes in the dispatch time of buses from the terminal in order to reduce fuel consumption and air pollution rates. The results indicate that one of the improved situations was related to the situation of increasing the bus dispatch time parameter and in the bridge scenario, CO2, CH4, and N2O emissions are 1458.6, 1.122, and 11.781, respectively, in one hour of peak passenger time.

According to the results, achieving the goal of reducing fuel consumption and air pollution rates is more suitable in the bridge scenario compared to the other two scenarios. Furthermore, if possible, it is suggested to build bridges at intersections with high traffic, or put the smart traffic light system on the agenda.

The increasing complexity of omnichannel retailing has necessitated retailers to redesign processes and forecasting methods and accept new approaches based on machine learning and artificial intelligence. Improving the accuracy of demand forecasting and managing customer needs from different channels due to reducing demand uncertainty are the most important challenges in omnichannel retailing that retailers should deal with. A better understanding of consumer behaviour patterns leads to more accurate demand forecasting, which in turn helps gain insight into transportation flows, improves distribution management, and enables better planning and execution of supply chain operations. This study aims to reduce the uncertainty of demand in omnichannel retailing by improving the accuracy of demand forecasting by considering customers buying behaviour through using machine learning methods.
Design/methodology/approach: In this study to forecast future sales based on customers buying behaviour, a cosmetics retailer’s historical data on the monthly sales from February 2020 to June 2022 is used. The ID of eight products has been selected to analyze the performance of proposed methods and the method that the company applied to forecast demand. Clustering has been implemented using the dynamic time-warping algorithm due to the unequal length of the products’ time series. Initially, the nonlinear autoregressive neural network (NAR) has been applied to the time series in each cluster and later, the nonlinear autoregressive neural network with exogenous input (NARX) has been applied to the time series. The performance of the methods has been evaluated by testing R-squared and all R-squared coefficients and root mean square error (RMSE) to analyze the accuracy measure.
The forecasting methods comparison, moving average (MA), the nonlinear autoregressive neural network (NAR), and the nonlinear autoregressive neural network with exogenous input (NARX) concerning testing R-squared coefficient, and also all R-squared and RMSE indicated that the nonlinear autoregressive neural network with exogenous input presented a good performance for all the products, so it confirmed that the application of the clustering to identification customers buying behaviour through the sales history of the products, integrated with artificial neural networks, to conduct demand forecasting, could be considered a good method for forecasting demand of omnichannel retailing supply chain products.
Practical implications: The proposed method of this study leads to uncertainty reduction in omnichannel retailing by understanding the buying behaviour of customers, identifying patterns and using its analysis in the processes and operations, and its integration with machine learning methods improves distribution management and provides better planning and implementation of supply chain operations. Managers can use the proposed method to accurately predict complex demand patterns in the retailing industry. Using business data in demand planning provides an extra advantage to managers to include important variables based on their judgments.
Social implications: Knowing the factors affecting the sale of a specific category of a product helps to effectively design promotions, advertising campaigns, the optimal combination of category displays and optimization of shelf space in retail stores. Also, accurate demand forecasts lead to better ordering policies, thus minimizing the cost of inventory management and optimal distribution and logistics planning to meet future demand.
Originality/value: The proposed method presents a predictive approach for an omnichannel retailing supply chain that leads to uncertainty reduction in omnichannel retailing by understanding the buying behaviour of customers, identifying patterns, and using its analysis in the processes and operations and its integration with machine learning methods to improve distribution management, and provides better planning and implementation of supply chain operations.

In this paper we try to simulate the life cycle of the electricity industry using the agent-based simulation method. For this purpose, 5 Agents were mentioned and they were simulated by Anylogic software .Then, four scenarios were investigated with experts' opinions. In the first scenario, it is assumed that the attractiveness of gas power and combined has been reduced and the attractiveness of the other two power has been added, the result of this scenario is the increase in hydroelectric power production, which is not cost effective due to the lack of water resources for the country. In the second scenario, with the arrival of a new technology, the household consumer decreased by 40%, and hydro and steam power with a capacity of 40% were working in the power generation cycle, the result of this scenario is the reduction of fuel consumption and also the shortage of electricity produced relative to the electricity consumption. In the third scenario, it is assumed that the consumption rate of all consumer factors has decreased, which results in the lack of investor factor investment. the fourth scenario assumes that all consumers' consumption is constant, as well as keeping the attractiveness developed by the government steady. As a result, the capacity of electricity generation has increased, but we are still facing power shortages.

Today, the manufacturing industry, with the expansion of the physical constraints of trade worldwide, has adopted modern information technologies to optimize the business process and achieve integration with geographically dispersed supply chain partners. Traditional supply chain models focus on optimizing physical flows. However, it is equally important to ensure that physical units can process appropriate information. This paper aims to propose a model for the optimization of information process performance in the IoT-based virtual supply chain. 

Design/methodology/approach:

 In this study, information processing performance in the closed-loop virtual supply chain has been optimized to maximize profit and information processing speed by considering costs of virtual, information security, and energy consumption. The final programming model has been optimized using meta-heuristic algorithms, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and the Strength Pareto Evolutionary Algorithm (SPEA-II).

Findings:

 The results indicated that there is an inverse relationship between virtual supply chain profit and information processing speed (delay). The results of model solving using NSGA-II and SPEA-II algorithms underlined the virtual supply chain profit of 9.93×106 and 4.23×106, and the data processing speed of 337.48 and 94.07, respectively. Thus, the NSGA-II algorithm contributes more to the supply chain profitability.

Research limitations/implications :

The proposed model can be used in manufacturing industries equipped with IoT. Unavailability of practical examples and insufficient data are the two main limitations of the study.

 Practical implications:

 The proposed model improves the production process and helps managers to plan better for their supply chain management and make timely decisions by sharing information across the supply chain and being aware of the flows of products and associated parts.Social implications - The Internet of Things in the virtual supply chain provides an opportunity to manage logistics systems and results in efficient online delivery with minimal cost. The information flow integrates all links and participants in the virtual supply chain. It enables each member to obtain the accurate information needed for logistics capability, reduces resource wastage, and improves customer satisfaction. 

Originality/value: 

One of the innovative aspects of this research is the use of IoT in the virtual supply chain for the integration and transparency of information in the supply chain, considering the importance of information in the virtual supply chain and examining the impact of IoT usage on closed-loop virtual supply costs and target functions. In addition to considering the physical flow costs of the closed-loop, including production costs, separation costs, repair, disposal, recycling, etc., in the cost objective function, virtual flow costs included IoT usage costs and information security costs. Energy consumption was also included in the objective function. Also, due to the virtualization of the supply chain and the significant role of information, optimization of information processing speed was considered in modeling the supply chain performance, which is another innovative aspect of research.

Technological innovation is the backbone of New Technology Based companies and one of the main factors for survival and gaining a competitive advantage for these companies. Creating a continuous flow of innovations in these companies requires the formation of innovation capabilities. This research has been done with an in-depth study of eight new technology based companies in the medical equipment sector of Fars province with a historical approach with the aim of discovering the path of formation and development of this capability in these companies. Using structured interviews and observing and studying the available documents, the required data were collected and using thematic analysis method, semantic expressions were classified in the form of conceptual codes and then sub-main and main themes. Finally, the thematic map of the research is drawn. The results show that the path of formation of technological innovation capability in these companies includes four stages of focusing on finding solutions to solve customer problems, technical and engineering knowledge, regulating management functions and creating internal discipline.

Given the importance of financing small and medium-sized manufacturing companies in order to provide working capital and their profitability, this study has presented a mathematical model for financing these companies by factoring in the supply chain. Factoring is one of the most important ways to finance the business world, especially for small and medium-sized enterprises. Therefore, considering the relationship between the enterprise and the bank, suppliers and buyers based on the integration of financial and physical flows in the supply chain, this research has financed the enterprise in the above-mentioned method. In order to analyze the validity of financing methods, goals, parameters and important variables in modeling, the perspective of experts based on the index of content validity ratio has been used. The goals of modeling are to maximize profits and achieve the desired liquidity in time periods, and in order to solve the model, goal programming has been used and in order to cover uncertainty conditions, interval programming has been used. Finally, the model was solved using GAMS software and CPLEX solver, and the results, in addition to proposing appropriate financial and physical flows in the supply chain, have proposed an appropriate factoring financing program to small and medium-sized manufacturing enterprises to provide the necessary liquidity in each period and increase profitability.

Relief of injuries is one of the most important stages of crisis management that should be planned for it before theincident. Timely and productivity relief will significantly reduce the financial and human losses. Considering thisnecessity, in this paper, proposed a mixed-integer mathematical model of multiple transfer points and facilities locationfor the formation of a relief chain during the earthquake crisis, which facilities includes hospitals, rescue centers,warehouses and shelters. Screening the injuries according to the triage system and providing outpatient health care atthe transfer points, transfer the yellow injuries to the relief centers, dual transfer of the red injuries to the hospital, payingattention to the type of goods needed at any point, and finally, adding budget constraint to set up facilities are the paper’sassumptions that developed the transfer points and facilities location model. To evaluate the model in real term a casestudy was conducted in south-central regions of Tehran. Model was once solved with specified number of facilities, andagain with consideration of budget constraint, in the second condition, better and more practical results were obtained. Asthe budget increases, the number of transfer points increases that indicating the importance of them in reducing the relieftime. Therefore, the establishment of transfer points is an effective strategy in the efficiency of the relief process duringa crisis. Attention to the distribution of hospitals in the city for the construction of new hospitals, the establishment of airrescue infrastructure in hospitals, identification of resistant buildings to change their use in crisis as a backup facility, andtraining the process of relief to citizens, are important solutions in the proper functioning of the relief chain

Credit risk assessment is one of the key issues for banks and financial institutions and various models have been developed for this. This study uses Case Based reasoning (CBR) Model and considers a database of bank credit customers to assess the credit risk of bank applicants. For this, 9 criteria were selected based on the experts' opinion and were weighted using the Fuzzy Analytical Hierarchy Process (FAHP). Return check, housing situation and income level are the most important criteria for credit risk assessment of the bank applicants. Then, using the TOPSIS Technique, we could evaluates the similarity of the new item with actual past cases or evaluate the new applicant with the ideal option, and uses a case-based reasoning model to predict the likelihood of default or non-default applicants. Survey research was applied for this study and the research community was the records of previous bank applicants between 1390-94 years.

Brain circulation with the means of bilateral migration and the flow of brain among countries is one of the dimensions of globalization. Although Scholars found many benefits for the brain circulation, the unbalanced migration of brains from the country causes a sharp reduction in human capital as the main factor of development in the sending country. This study determines the effective level of intellectual property rights on increasing the brain circulation index using correlation analysis and Tukey method. The research data are obtained from the migration status of inventors in 98 countries in 2012 according to their nationality and residency. The findings of this study showed: firstly, although there is a significant inverse relationship between the level of intellectual property rights and the brain circulation, but their correlation coefficient is low, and secondly only if the intellectual property rights index increases to more than seven, there is a significant effect on brain circulation index. Further comparison of the level of intellectual property rights in Iran 2019, 4.4, with the results obtained showed that improving the level of rotation of elites using the policy of strengthening intellectual property rights requires legislation, regulation and spending large sums.

As nobody can prevent disasters like earthquake, tsunamis, hurricanes, floods and etc. and because of huge harmful effects of the events, in recent years, scientists mostly try to find the best way to control undesirable consequences of these events. According that Emergency decision making EDM is a way of emergency management to control the bad effects of these disasters. EDM used for rescue units that their mission is going to critical places and save the lives and properties, must be found location and allocated to places that crisis occur there. This paper presents a bi-objective decision support model to find a location to establishment of critical bases and allocate rescue units of primary nodes to each crisis or each area that floods existed there. During these action two main objectives of these kinds of problems considered. First goal of the (MILP) models is for minimizing sum of all severity of crisis delays of rescue unit to reach the critical places. As the second goal, the model minimizes total costs of this transportation. These two objectives ranked by priority. Then to illustrate the efficiency of the model a numerical example solved by GAMS and results is shown.

As nobody can prevent disasters like earthquake, tsunamis, hurricanes, floods and etc. and because of huge harmful effects of the events, in recent years, scientists mostly try to find the best way to control undesirable consequences of these events. According that Emergency decision making EDM is a way of emergency management to control the bad effects of these disasers. EDM used for rescue units that their mission is going to critical places and save the lives and properties, must be found location and allocated to places that crisis occur there. This paper presents two scenarios that each one contain multi objective decision support model to find a location to establishment of critical bases and allocate rescue units of primary nodes to each crisis. During this action three main objectives of these kind of problems considered. First goal of the (MILP) models for each scenarios is minimizing sum of all severity of crisis delays of rescue unit to reach the critical places. The second goal of the problem minimizes total arrival time rescue units at critical places. As the third goal the model minimizes total costs of this transportation. These three objectives ranked by priority. The difference between two scenarios is that first scenario crisis is predected and dosen't occured but in second scenario crisis existed by raid. Then to illustrate the efficiency of the model a numerical example solved by GAMS and results is showed.

The main purpose of this paper is to propose a multi-objective model for assigning service/product to clustered customers. The main practical objectives of this model from the perspective of the bank are reduced cost and risk and increased customer satisfaction. Design/methodology/approach: In this paper, five indicators of recency, frequency, monetary, loan and deferred have been identified and customers have been clustered, accordingly using K-means approach. Then, a three-objective mathematical model has been designed to assign optimal service/product as response to customer. Finally the model has been solved by simulation based optimization.

In the case study, all information about five characteristics of customers was extracted from the database, 31953 customers were placed in seven clusters and the validity of these clusters was measured. A three-objective mathematical model was designed based on the characteristics of 13 types of bank products/services. Then, the simulation modeling solutions were improved using the simulated annealing algorithm. In this study, Weka and R-Studio, Arena and Longo were used for data mining, simulation and optimization, respectively.

The limitations of this study include inability of simulation instruments for drawing, solving all probable states (more scenarios) and solving the model for those states. It is recommended to develop the mathematical model with respect to customer, so that after problem solving, the bank would be able to make decision on providing services and products to its customers. Simultaneously, the objective functions would be fitted within their most reasonable states and ultimately, using a model, the parameters related to each product can be set for the new customer referring to the bank.

Products/services were assigned according to customer needs in a way that cost and risk were reduced and the utility of assignment was increased through the proposed model and simulating the behavior of each cluster of customers.

Paradigm shift in the banking industry is changing from e-banking to digital banking. In digital banking, assigning/customizing products/services, regarding the needs of customers, is very difficult .The banking industry is not well equipped to respond to the digital banking expectations of most consumers. One of the most important challenges of banks is recognizing customers, clustering and assigning a service/product to each of the different clusters. The main policy in the banking industry is to increase customer satisfaction and reduce cost and risk in sales service. Therefore, each customer should have a dedicated service/product.

In this paper, authors attempted to use one of the clustering approaches in multi-objective programming. In addition, they proposed an approach for assigning product/service to customer by simulating and analyzing the behavior of each customer cluster.

Today, many requests are presented to business companies by various stakeholders of the maritime supply chain such as shareholders, banks, commodity owners and regulatory agencies to review their duties and responsibilities by considering some important aspects such as Environmental, economic, social and governance issues. In fact, the concept of sustainable strategic development also refers to promoting the effective collaborations and interactions between these elements in the maritime supply chain. The main purpose of the research is to provide a comprehensive framework for sustainable strategic development in the field of maritime industry in the country which has been carried out by taking into account some key components such as stakeholder demands and supply chain performance. The method of collecting data in this study is a field-library one. After identifying various measures of the three main components of this research, namely, requests received from the stakeholders, sustainable strategic development and enhancement of the performance in shipping industry, ultimately, the results obtained by using the Delphi technique were moderated. Then, based on the five-option Likert Scale, a questionnaire is developed and distributed among the statistical samples. Meanwhile, structural equation modeling and path analysis techniques are used for data analysis. At the end, an appropriate model has been presented for explaining the relationship between the three main components of the conceptual model in this industry, so that the demands received from the beneficiaries directly have a significant relationship with the concept of enhancement in the performance of the maritime supply chain. Also, the mentioned requests indirectly and through the role of sustainable strategic development has a significant relationship with the enhancement of the performance of the maritime supply chain

To help the injured during the crisis, the planning must begin before the crisis. The aim of this study is to provide a model for locating and optimally allocating transfer points, relief centers and hospitals during an earthquake to help the injured including screening, first aid services and transfer to the facility. Quantitative modeling through the presentation of the mathematical model is used in this paper. For this purpose, the mixed integer, single and double-objective mathematical programming models are presented. The single-objective model seeks to minimize the injured transmission time, and the double-objective model minimizes the fines due to the failure in transferring the injured as well as minimizing the time. The models are applicable based on two approaches of locating and optimal allocation in a 6-Richter earthquake with fewer injuries and based on the selection of a limited number of facilities and optimal allocation approach in the 7-Richter earthquake with a large number of injuries and the use of total capacity of the facilities. Models were applied based on the information of south-central districts of Tehran including districts 10, 11, 12, 16, 17, and 19. Transfer points, relief centers and selected hospitals in each model, manner and degree of the allocation of the injured to these points were specified. To provide proper relief in real conditions, adding relief centers to the problem due to the large number of injuries against hospitals capacities, the screening of the injured, the provision of outpatient treatment services at the transfer point and rejection of the injured in Hospitals are among the basic assumptions that should be considered in the models. Moreover, unbalanced distribution of hospitals in the city and the lack of basic infrastructure should be resolved before a possible crisis.

Today, one of the requirements in modern warfare is the rapid and deliberate deployment of combat forces. The new phenomenon of combined wars and the drastic changes of the current battle scenes and the need to embark on a new operational approach cause that mobility, agility, and rapid displacement of units become particularly important, in a way that units with higher mobility under equal conditions will be more efficient. Therefore, this study aims to provide a model for military mobility based on the principles of operational research. In this research, different scenarios for the outbreak of war and subsequent military mobility are investigated, then two mathematical modeling based on supply chain are developed for two scenarios. In these multi-objective mathematical models, the primary goal is to reach the destination with the least delay and the lateral goal is to minimize the total costs. The problem is solved using GAMS Operational Research Problem Solving Software and its performance is proven. Finally, by using an example the computational results of this problem are evaluated.

Two main drivers have a dramatic effect on the structure of the manufacturing industry in the world; the first is the rapid advancement in manufacturing technologies and the second is the globalization of markets. In order to protect their competitive edge, industrial firms have shown a great passion for advanced manufacturing technologies. However, successful implementation of these technologies is a huge challenge for the managers. Based on the content analysis methodology, this study has a systematic approach to the literature review. By reviewing four cases, the aim of this study is identifying key success factors in Iran’s automotive industry and also providing a more realistic image of these factors within the industry. The results show that there are 13 types of key success factor in this area. These factors can be divided into five categories: 1) understanding the implementation process and external interactions; 2) supportive and cognizant top management; 3) a planning and implementation system that aligned with strategy; 4) system integrity in the organization; 5) empowerment of human resource. Presence of these factors can lead to not only technical improvements but also succeeds at higher business levels. Findings of the study are approved by an expert panel.

Supply chain management association, in a comprehensive framework, defines supply chain as a strategic asset that can create value and consequently a competitive advantage for the organization This research aims at recognizing the formation and impact of supply chain complexity as an asset on its performance. The sub-goal of this research is to analyze the position of financial performance in the impact of supply chain complexity. Thus, after reviewing theoretical literature, the dimensions and terms of the model were identified and formulated as a reference model for supply chain processes. Then, a questionnaire was designed to assess the complexity of the supply chain and its impact on performance. CRONBACH alpha was used to assess the reliability and content, and construct validity was used to assess the validity. The statistical population of this research is home appliance companies. The statistical sample consists of 50 companies in the cities of Tehran, Isfahan, Tabriz, Mashhad, Shiraz and Ahvaz. In this research, 250 questionnaires were distributed in deferent layers of supply chain and 197 questionnaires were completed. Finally, by using Factor Analysis and Structural Equation Modeling method, proposed conceptual model was analyzed. Based on research findings, the supply chain complexity of the population has significant and negative impact on performance.

Knowing customer behavior patterns, clustering and assigning them is one of the most important purpose for banks. In this research, the five criteria of each customer, including Recency, Frequency, Monetary, Loan and Deferred, were extracted from the bank database during one year, and then clustered using the customer's K-Means algorithm. Then, the multi-objective model of bank service allocation was designed for each of the clusters. The purpose of the designed model was to increase customer satisfaction, reduce costs, and reduce the risk of allocating services. Given the fact that the problem does not have an optimal solution, and each client feature has a probability distribution function, simulation was used to solve it. In order to determine the neighbor optimal solution of the Simulated Anneling algorithm, neighboring solutions were used and a simulation model was implemented. The results showed a significant improvement over the current situation. In this research, we used Weka and R-Studio software for data mining and Arena for simulation for optimization. The results of this research were used to develop Business Intelligence software for customers in one of the private banks of Iran.

Many methods have been proposed in the literature for solving Multiple Attribute Decision Making (MADM) problems, which may create different prioritization for a unique problem. The validity of the methods depends on preserving the rankings in the case of adding or removing irrelevant alternatives. Irrelevant alternatives are those which their evaluations regard to any attribute is worse than the best selected alternative or any other alternative which has not been chosen. In other words, the validity of prioritization not only depends on not to create a different prioritization with removing the worst alternative, but also depends on to create an upward prioritization for other alternatives when the best alternative elemintates from the set of alternatives. Furthermore, when diverse normalization process use for the problem, the results must not change. Furthermore, different normalization processes do not have any effect on the rankings. In this paper, a zero-one programming model with a weighted sum minimization objective function has been constructed. Any constraint which relates to each attribute needs to have a slack or surplus variable for standardization. These constraints with real world constraints (for instance, budget constraint) forms the model constraints. Three case studies adapted from literature review, which have been normalized with different normalization procedures, solved with new model. The results demonstrate rank reversal phenomena has not been occurred.

The aim of this study was to investigate and analyze the performance of 13 hospitals in Semnan province using data envelopment analysis (DEA) technique.
In this research, dynamic linear programming was used to measure the performance of units under the supervision of medical sciences, including hospitals and health centers. For evaluating the performance of hospitals in Semnan province, input and output indices of each hospital, their values in the center and the information of each hospital were available. GAMs software was also used to solve the dynamic model obtained in this paper.
By solving mathematical models for each hospital, the efficiency of the hospitals has been achieved, and then the efficient and inefficient units have been identified. If each hospital was inadequate, a suitable model was given to the hospital. Finally, a ranking methodology was used to distinguish between efficient hospitals. Hospitals 2, 3, 6, 7, 8, 12 and 13 were known as efficient units for the whole of these three years, and hospitals 1, 4, 5, 9, 10, and 11 were known as inefficient units.
With the implementation of this research, it is possible to introduce the functional models to the top managers of the health sector, providing more accurate planning for the development of capacity for health services and saving resources.

E-business as one of the enablers of the economy and a subset of information technology has grown substantially in the past decade. Today’s global markets is changing and transforming from industrial market to a science and technology one. In other words, today's business environment has reshaped from physical to electronic and has transformed traditional and offline markets. Successful investors have accepted transformation of traditional business to e-business so that policies of commercial enterprises in adoption and deployment of e-business are effective and efficient in order to enter international markets and attract new customers. The importance of e-business environment and high degree of its acceptance by people and businesses, have led to feel the importance of planning and development of e-business strategies and frameworks. This study, presents the results of a project administered by Ministry of Information and Communications Technology of Iran, named “Electronic business development strategies in the context of facilitating and improving the business environment “.The purpose of this project is to extract and codify the business development strategies in the field of communications and technology, which has been designed and implemented in four phases.The fourth and final phase of this project, presents a new model based on e-readiness frameworks and was confirmed by using Smart PLS software. Finally, the e-business development strategies have extracted and classified based on confirmed model and SWOT analysis.
JEL classification: O16, O17, O21, O33, O53.

In the single-mode resource-constrained project scheduling problem, it is assumed that each activity has a specified known execution time and resource consumption can be done only in one way. In practice, there are many cases in which the make span can be reduced by providing additional resources activities. In this case, each activity can be done in one of the procedures, which is called the multi-mode resource-constrained project scheduling. In this paper, the problem includes determination of the basic schedule for the project activities which may be done in several models and the precedence relations are met, While the project make-span, cost and resource fluctuations are minimal. In this research project scheduling problem network will model using ED simulation software and the results of the simulation and a meta-heuristic algorithm has been compared. Finally, management strategies to optimize the scheduling, i.e such minimize total time, cost and resource leveling, will be offered.

Enterprise architecture is considered as one of the effective ways of aligning information technology with business. In order to use the benefits of Enterprise architecture, the growing trend of its implementation in the organization should be considered. For this purpose, it is necessary to use control and evaluation tools. Enterprise architecture maturity assessment, as one of the effective methods of managing enterprise architecture process, is very important. The goal of maturity assessment is to determine the current status of the organization and to draw a clear picture of the future growth path of the organization. Today, the importance of maturity assessment in various organizational areas is becoming increasingly evident. However, in this area, in the field of financial and banking institutions, due to the complexity of the architecture of information technology and organizational architecture in these institutions, no significant activities were undertaken. Therefore, the present study aims to provide a new and customized model, in order to measure the maturity level of enterprise architecture, in one of the largest banks in Iran. For this purpose, after the extraction of the subject literature, the study and observation of the target bank have been dealt with. The descriptive approach and the structural equations have been developed and then the conceptual model has been extracted. The model includes 3 dimensions, 23 variables and 75 indicators .