دکتر محمدمهدی موحدی

The present study presents a three-tier model with incomplete and uncertain information of supply chain needs, benefits and services. Objectives of the issue include determining the best decision to determine the optimal order amount and shortage for the manufacturer and the selling price of each player according to the shortage, discount and maintenance costs, purchase and marketing to achieve maximum revenue, minimum costs and The sum is the maximum possible profit for all the players participating in the chain. To solve the model, Gamz software and meta-heuristic algorithms have been used and finally, Given the complexity of the complexity of closed-loop supply chain problems, the problem ahead cannot be solved in a reasonable time for real-world dimensions. Therefore, to solve it, the meta-heuristic approach in the form of genetic algorithms and optimization of particle aggregation and the combination of these two algorithms have been used. The results show that the combined algorithm of genetics and particle swarming has a better situation compared to genetic and particle swarming algorithms.

In today's world, the role of the economy in shaping business models and the power of nations is highly significant. Exports play a vital role in enhancing productivity and economic development, particularly in developing countries. The automotive industry, as a key sector, contributes considerably to this process. This study aims to identify the key influencing variables on non-oil exports and explore how they affect productivity growth and export improvement.

This research employs a system dynamics approach to model the interactions among key economic variables. The study utilizes VENSIM software to simulate the system dynamics model of the automotive industry and non-oil exports. Causal loop diagrams and stock and flow diagrams were developed to analyze the relationships.

The main variables analyzed in this study include exchange rate, inflation, productivity, and competitiveness. The developed model was validated and tested under various scenarios. Results indicate how changes in these variables impact productivity and the performance of non-oil exports in the automotive industry.

Originality/Value:

 This study offers a dynamic model tailored to the automotive sector in developing economies like Iran, where over-reliance on oil has led to inefficiencies in other export sectors. The model helps policymakers and industry stakeholders understand complex interactions and make informed decisions to boost non-oil exports and overall productivity.

Nowadays one of most important organizations’ goals is to access compatibility to increase their market share and higher profitability. Therefore, it can be claimed that compatibility is considered as one of the most general organizations’ and industries’ outputs. The purpose of writing the current paper is to represent a model to measure carpet industry compatibility emphasizing quality and innovation. Statistical society in qualitative part includes 14 top managers and 582 employees in quantitative part from which 232 ones were selected as statistical sample. First of all, by the research model was obtained with three main dimensions resources and capabilities, quality and innovation with 30 indices through semi-structured and unstructured interviews with experts and during the coding process. Meanwhile resources and capabilities includes spiritual capital, human capital and technological capabilities, quality dimension includes raw materials, pre-production phase and production phase and finally innovation dimension includes product innovation, process innovation and sale innovation.

The aim of this research is to present an integrated method of data envelopment analysis (DEA) and fuzzy TOPSIS technique based on similarity to the ideal solution for the complete ranking of decision making units in a fuzzy environment. In this method, DMUs are considered as alternatives, input variables as cost criteria (negative attributes) and output variables as benefit criteria (positive attributes); Because the efficiency of a DMU increases by increasing the values of the outputs and decreasing the values of the inputs. In addition, the presented method can be used to rank DMUs with unfavorable outputs. The efficiency and simplicity of this method have been investigated via examples and case study. Also, the obtained results have been compared with the results in related articles

Today, the increasing progress in technology, the expansion and development of human needs for sustainable technologies have caused attention to electric energy to be in the center of attention more than in the past. Therefore, increasing the reliability of electrical systems in the power industry is very important. The purpose of this research is to provide a mathematical model to calculate and increase reliability in the power distribution network. This research is practical in terms of its purpose and results, and in terms of the method and nature of implementation, it is based on operational research that was conducted using mathematical modeling and using Python software based on data from 1398 to 1402. The findings show that parameters such as generators, high pressure and low pressure busbars, 20 kV to 400 V power transformers, communication cables, capacitors, generators and UPS are more important in calculating the reliability of this network. Therefore, according to the purpose and the corresponding limitations, a suitable mathematical model has been presented for each parameter. The results show that after 50 repetitions and simulations, the ultra-emergency line has a higher importance and rank in reliability among the four output feeders. Also, based on the presented model, it has been observed that the entire 20 kV line under investigation has 0.67 degrees of reliability. The results of this research can be considered as a suitable basis for the implementation of research and operational projects in wide radial networks in the electricity industry.

This research aims to optimize humanitarian logistics to increase coordination between actors in the phase during and after the disaster and aims to minimize the cost of relief, minimize the time of relief and minimize the cost of rebuilding infrastructure and housing for the affected people.

This research, in terms of the research direction types, is developmental because it is trying to expand the existing models in the design of the humanitarian logistics network and consider the optimization of two phases during the post-disaster phase. The proposed model has been solved using two metaheuristic algorithms named multi-objective genetic algorithm and multi-objective particle swarm optimization.

The implementation of this study will lead to a reduction in the costs of locating, routing and reconstruction in the humanitarian supply chain, as well as reducing the time of providing aid to the affected people and increasing their satisfaction. It is also possible to reduce the inventory of relief products with the help of this issue. Appropriate planning in humanitarian logistics processes, especially in the coordination phase of reconstruction, will be done according to the limited budget of governments and the appropriate use of resources.

Originality/Value: 

One of the innovations of this study is reducing the cost of reconstruction after an earthquake. Several studies were conducted in order to recover from the disaster. Over the past two decades, response phase relief operations have been the focus of a significant number of researchers. However, the issue of post-disaster recovery and reconstruction programs has not been sufficiently discussed in scientific and practical forums.

Designing a multi-objective multiple allocation-routing problem in a humanitarian supply chain is important because it improves efficiency and productivity in the supply chain. By designing a multi-objective multiple assignment-routing problem, we can consider different objectives at the same time and seek to optimize them. As a result, designing a multiple multi-objective allocation-routing problem in a humanitarian supply chain can improve the overall performance of the company and lead to the economic and social growth of the region.

In this article, an integer mathematical model for the allocation of essential product to all destinations through multiple hub allocation-routing coverage is presented in a way that reduces the cost of allocation and the number of possible paths for allocation in a humanitarian supply chain. The proposed model is able to estimate well the binary optimal solutions of the problem that is route and vehicle allocation through the solution using the scattering search method. Based on the solution of the proposed model, the shortest possible route for allocating essential items to different considered cities is provided. Also, the lowest possible cost has been determined for allocating the route for relief. Finally, the allocation of each vehicle according to their type to each city has been specified. This allocation of vehicles to each of the cities has been done in such a way that each of the vehicles covers each of the cities at least once.

Based on the obtained results, the lowest routing allocation cost was gained using the suggested model. The results of the research show that the proposed model improves both the efficiency and effectiveness of humanitarian aid logistics, and its findings provide valuable guidance for improving decision-making regarding route allocation, the type of dispatch vehicle and cost in the field of humanitarian logistics, and for designing a suitable strategy for Dealing with disasters will be beneficial in the future.

This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities and waste treatment centers, vehicle fuel costs, and environmental costs due to pollutant emissions; 2) Maximizing the energy generated from the waste combustion process; 3) Minimizing the risk of virus transmission resulting from inadequate waste management; and 4) Maximizing the number of job opportunities in the established centers. It is important to note that existing uncertainties are addressed through the application of fuzzy set theory. Given the multi-objective nature of the model, two multi-objective algorithms, namely the Pareto archive-based Krill Herd Algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are employed to solve the defined problem. The results indicate that the proposed Krill Herd Algorithm converges to a solution with higher quality and dispersion compared to NSGA-II. Additionally, through a comparison of the spacing index and running time of the two algorithms, it is observed that NSGA-II explores the solution space with higher uniformity and solves the model in less time.

The purpose of this research is to analyze the role of logistics quality management in creating the value chain of knowledge management in the supply chain of defense industries. Because this research was done in a real organization and its results can be used practically and it is a practical research.

The present research is applied from the point of view of the traversal and descriptive-survey from the nature of the research. The statistical population of this research includes all managers (high, middle, operational) of defense industries, whose number is 400. The sampling method in this research will be simple random and with the help of Morgan's table, a sample size of 196 people was selected to measure the variables. The research used a standard questionnaire. The method of collecting library and field information and the tool of collecting information is a questionnaire; the validity of which has been confirmed based on content and final validity using Cronbach's alpha coefficient, structural equation test and multivariate regression have been used for data analysis.

The results show that logistics quality management has an effect on the creation of the value chain of knowledge management in the supply chain of the defense industry.

One of the most important problems for distribution companies (especially large online stores) is the design of docking stations, vehicle routing, and supply chain optimization. In this research, the distribution centers of Digi kala online store have been located. A multi-objective location-routing problem has been developed by meta-heuristic methods considering the principle of stability and time constraints. Based on the results of the proposed model, the effect of demand on the three objectives of time, cost, and environmental and social issues was investigated and it was shown that if demand increases, there will be a greater effect on time. In other words, increasing demand can create gaps that result in increased delivery time, and therefore this can lead to customer dissatisfaction in the long run. Increasing employment also leads to better environmental issues, but has no effect on cost or time. Also, the cost of transporting goods has no effect on environmental issues and time, but leads to increased costs.

The purpose of this study is to investigate industry 4.0 advancement in various application areas of Iranian industry using Internet of Things -based achievements and technologies. In this research, 112 articles were studied by searching for specialized keywords related to the topic and using valid research sources. In the following, 88 abstracts of articles related to Internet of Things applications were reviewed. In the review we finally made up the 32 title of our article on the subject literature. According to the subject literature, various applications of iot were extracted and categorized in a table; Then, by forming a target group of experts and designing a paired comparative questionnaire for the AHP method and using Expert Choice software, the results showed that according to infrastructure criteria, financial support, senior management support, training training, firmware availability., Communication Security, Expertise and Desire for Innovation, Intelligent Manufacturing Including Automotive Industry, Intelligent Industrial Control and Manufacturing, Supply Chain Management and Support, Smart Networks, Intelligent Logistics, Intelligent Manufacturing Services and Processes, etc. Industry 4.0 with Internet of Things technology. There is also a low level of environmental and recycling usability that requires senior managers to support Internet of Things technology transfer strategies, the use of Internet of Things specialists, and the financial support of upstream managers to leverage the Internet of Things -compliant infrastructure. This area will pave the way for industry 4.0.

The decision to allocate the optimal route for ambulances affects the success of disaster response and the safety of victims. In this paper, a multi-objective optimization model is proposed to determine the route of vehicles in response to humanitarian relief supplies. 

Two objective functions are formulated to improve efficiency and effectiveness. The first goal is to minimize the total cost of each route. The second goal is to minimize the total time of vehicle repairs to provide limited relief to victims from all injury areas. The Epsilon (EC) constraint method was used to solve the proposed model.

The applicability of the proposed model is demonstrated through a case study. The results obtained from solving the proposed model are compared with the existing routing scheme. Comparisons show that the results obtained from solving the proposed model are better than current routing programs.

In addition, the results of this study can provide an advantage for decision makers in order to consider appropriate strategies for responding to disasters.

Cross Dock operation an execution tactic to deliver cargoes with an aggregation-consolidation approach in such a way that the logistics system faces minimal inventory accumulation or lack of inventory execution. Accordingly in order to increase agility at the supply chain level, logistics engineers are designing and implementing cross docking at the supply chain level. Logistics executives are always striving to find solutions that improve speed and improve supply chain safety. Observing all of these indicators can provide a lean supply chain. Cross Dock as an intermediary warehouse can play an important and influential role in improving the flow of raw materials, products and logistics services. The main purpose of this paper is to present a mathematical model of a trailer scheduling approach with a stochastic planning approach to provide the optimal solution to reduce the expected total logistics operation time and thus reduce logistics costs. These start from the stage of receiving pallets (suppliers) from suppliers or warehouses of other logistics cluster sites at the supply chain level and, following internal logistics processes performed on inlet docks, by reloading in outlet docks. Dock will be completed to fulfill the orders of consumers of these consignments. The mathematical model used is integer programming and genetic algorithm and MATLAB software was used to analyze data.

The around world With globalization and growing consumption supply chain networks were shifted to very large networks and components of massive networks had caused serious environmental problems. Evaluaing the green closed loop supply network can ensure a minimum of material loss by following the maintenance rules during the material life cycle.The purpose of this article is to design a multi fuzzy decision making model for the evaluating Green Closed Loop (GCL). In this article identified the important criteria by reviewing literature review and interviewing with experts and using the fuzzy screening method to selecting very important criteria have been determined. Then provided a the MADM fuzzy decision making model based on the fuzzy Decision Making Trial and Evaluation Laboratory based on Analytical Network Process (ANP) (FDANP). At present research Development of a manual for separating recyclable parts and items important the evaluation and Selection of suppliers acquired second rank and the rest of the criteria were weighed and ranked. The results showed of the causal relationship study Responding to stakeholders and battery manufacturers the most effective criteria and Recycling, reproducing the product the most effective criterion were recognized in the evaluation GCLN. The results are based on a case study in the car battery recycling industry. MADM fuzzy decision making model based on FDANP technique has been provided. Which  The around world With globalization and growing consumption supply chain networks were shifted to very large networks and components of massive networks had caused serious environmental problems. Evaluaing the green closed loop supply network can ensure a minimum of material loss by following the maintenance rules during the material life cycle.The purpose of this article is to design a multi fuzzy decision making model for the evaluating Green Closed Loop (GCL). In this article identified the important criteria by reviewing literature review and interviewing with experts and using the fuzzy screening method to selecting very important criteria have been determined. Then provided a the MADM fuzzy decision making model based on the fuzzy Decision Making Trial and Evaluation Laboratory based on Analytical Network Process (ANP) (FDANP). At present research Development of a manual for separating recyclable parts and items important the evaluation and Selection of suppliers acquired second rank and the rest of the criteria were weighed and ranked. The results showed of the causal relationship study Responding to stakeholders and battery manufacturers the most effective criteria and Recycling, reproducing the product the most effective criterion were recognized in the evaluation GCLN. The results are based on a case study in the car battery recycling industry. MADM fuzzy decision making model based on FDANP technique has been provided. Which manufacturing companies can use to have an accurate the assessment of the GCLN and allocate limited national and natural resources efficiently to promote the social responsibility and protect the living environment.

The subject of the present study is production scheduling taking into consideration the payment of penalties. The purpose of production scheduling is to allocate limited resources over time to perform a group of activities. Therefore, the aim of the research was to develop an appropriate schedule in a production system that deals with issues such as various interruptions in production, lack of forecasting of raw materials required and time required for production, inability to decide on the composition of production which will lead to damages caused by delay and work stoppage. If the work is completed after the delivery date, there will be a penalty for late payment due to customer dissatisfaction, a contractual penalty or loss of credit. Reliability engineering must ensure that components and subsystems meet the defined requirements over a period of time. The research design was based on solving standard sample problems. In this regard, this research provided a mathematical model for multiple purpose scheduling. The scheduling problem was solved in the form of a reliability problem. Findings showed that solving the problem of production scheduling in the form of reliability performed well in terms of finding optimal solutions. The reliability of the system was also confirmed in 14 sample problems.

Today's the world is more competitive than ever therefore in order to survive in such an environment for organizations having with a supply management network has become an essential issue a recyclable and environmentally friendly product. The purpose of this article is to design Green Closed Loop Network (GCLN) to integrate financial- physical flows. The innovation of this article designing a four objective model to minimize deviations in financial indicators from desired limits; Costs; The rate of emission of pollutant gases throughout the environmental network and maximizing profits of shareholders using mathematical modeling of Fuzzy Goal Programming (FGP) with considering financial flows and its integration with physical flow. Modeling problem of FGP in a multi level multi period and multi product supply network with aim of maximizing shareholder wealth. To deal with multi-objective nature of FGP has been used. The model is designed and implemented in the dairy industry. The proposed model has been designed with GAMS24 software CPLEX solver tested using epsilon method of constraint. The results of modeling and solving numerical model show importance of the role of financial ratios and at the same time consider operational financial dimensions in mathematical model to gain a stable competitive advantage.

The current study with the purpose of identifying and categorizing the driving factors influencing on consumers’ acceptance of SMS advertising was accomplished in a society include 259 customer of Agriculture Bank in Tehran. The results of using structural equation model show that Financial Rewards, Credibility, Perceived Social Utility, Control, Perceived Utility and Context Utility affect significantly on SMS advertising acceptance which creditability, finance rewards and control were the most important ones. Continuously by applying fuzzy Topsis technique, 22 sub criteria were categorized that “permission to sending SMS”, “delivering SMS match private needs” and “encourage to more usage of mobile phone and pay attention to SMS” with approximate weights 0.624, 0.471 and 0.448 were selected as the most important sub criteria. Finally, by using AHP technique, 3 criteria of advertising acceptance were ranked which “positive attitude about advertising” (0.37) was the most and “tending to accepting advertising by SMS” (0.31) was the least important ones. Attending to the results, managers are advised to send related SMS in terms of customers’ needs and desires.

One of the most important issues in the blood supply chain is meeting the demand for hospitals' blood supply, which becomes more sensitive in times of crisis. Given that access to blood supply is often restricted in critical situations due to a sudden increase in demand, the supply chain is in severe trouble during and after the crisis. The aim of this study was to investigate the factors affecting blood donation by donors in the blood supply chain under critical conditions.

This descriptive-survey study was performed on all blood donors in Mazandaran, Iran province during the first four months of 2018. Using cluster sampling method, 384 samples were selected. Data were collected using a valid and reliable researcher-made questionnaire. Data analysis was performed using structural equation modeling in SPSS 20 and LISREL 8.50 software.

In this study, 92.97% of 384 persons were male and the age group of 31-40 years with the frequency of 36.20% had the highest age range. Evaluation of the research model showed that all fit indices were within acceptable range. Factor loadings for variable effects of advertising and informing, motivation, awareness and subjective norms on blood donation in crisis conditions were 0.98 (t=10.31), 0.80 (t=9.45), 0.66 (t=8.60) and 0.57 (t=4.75) were estimated. Therefore, advertising and information had the greatest impact on blood donation by donors in critical conditions.

The results showed that advertising, motivation, subjective norms, and donor awareness have a positive and direct effect on blood supply in the crisis supply chain, and advertising and informing have the most impact.

The main goal of the present study is to evaluate the total energy productivity and the effective factors on 134 manufacturing industries in Iran with a four digit code and also ISIC international classification among 1385 to 1394. To do this, with data envelopment analysis and Malmquist method the technical, technological efficiency and the total energy productivity are calculated on the manufacturing industries in Iran. And then by fixed effects method and generalized method of moments  (GMM),the effect factors on the total Energy Productivity was calculated. The results of the model showed that the total Manufacturing Industries experiences a technological positive growth, but some have been technically efficient. In all, the findings showed the growth of the total energy productivity in the manufacturing industries. The findings based on the model showed that variables such as skills, information communication technologies (ICT), research and development, physical capital and machinery and equipment , export and time are meaningful effect and just variable education is meaningless effect on the total energy productivity. Based on the results, reviewing the work style, improving managerial skills ,Government subsidized support of production and investment, imports tariff reduction of capital equipment and machinery of the industries with less energy consumption is recommended.