iraj mahdavi

Circular economy as a new non-linear view in the fields of production and consumption. The macro- level includes policies and decisions related to it.it is the gathering place of all procedures and processes at the micro and meso levels. research, using the method of Meta-Synthesis, fuzzy Delphi & fuzzy Cognitive Maps, identify the effective components and measures at the macro- level and how the causal relationships between them are. the articles related to the circular economy from 2015 to 2020 have been used as criteria. 104 articles were studied from Web of Science and Scopus. In order to evaluate the agreement between codings, Kappa test were used. The effective components and measures were drawn in four categories of macro policies, measures, infrastructures and culture and awareness, categorization and integrative fuzzy cognitive maps of experts. The credit weight of the effect in the integrated fuzzy cognitive maps shows the efficient management of natural and environmental resources, providing training to customers and companies, formulating policies and supporting incentives and activating the markets of second-hand and recycled products. They are the most important.

Cardiovascular diseases remain a leading global cause of mortality, with ischemic heart disease projected to account for 23.3 million deaths by 2030. Heart failure and cardiogenic shock account for a significant proportion of these deaths and require timely treatment as medical emergencies. This study aims to predict mortality within one month in patients experiencing cardiogenic shock secondary to heart failure using a concise set of predictive features.

An analytical cross-sectional study was conducted at Babol Razi Hospital, involving 201 adult patients (≥18 years) treated for cardiogenic shock in 2020. Data from 34 clinical variables, including age, history of cardiac surgery, pH levels, lactate concentration, diabetes status, and blood pressure, were meticulously analyzed. Mortality outcomes within one month were assessed via structured telephone follow-up. Logistic regression and Gradient Boosting Machine (GBM) algorithms were used for predictive modeling.

The average age of patients was 69.44 ±15.71 years. Among them, 47.7% died. The study identified age, lactate levels, diabetes, and initial confusion as significant predictors of mortality risk. Each additional year of age was associated with a 7% higher probability of mortality. Diabetic patients faced more than double the mortality risk compared to non-diabetics. Confusion at presentation increased the mortality risk fourfold, while elevated lactate levels raised it by 1.5 times.

Logistic regression and GBM algorithms demonstrated effectiveness in predicting one-month mortality among cardiogenic shock patients with heart failure based on selected features. This approach holds promise for improving referral processes and reducing costs in healthcare settings.

The innovative self-collection service in urban logistics is proposed as an alternative to door-to-door delivery, offering environmental and social benefits. This research aims to investigate the level of awareness among the population regarding the new self-collection service for order deliveries by preparing a questionnaire and conducting interviews with residents of Tehran. It also aims to identify the factors influencing customers' choice of this service and to examine the relationships between these factors using the fuzzy DANP approach. Ultimately, the research seeks to present a mathematical model for covering self-collection centers within an urban logistics network, aiming to minimize the distance traveled by consumers. The study's findings indicate that the awareness of the self-collection service among the population in Iran is very low. However, there is a significant increase in the population's interest in this service when the collection points are located nearby, demonstrating the high potential of this innovative initiative. Furthermore, data analysis reveals that the criteria of 1) proximity of self-collection locations to customers, 2) cost differences between door-to-door delivery and self-collection services, and 3) the quality of services provided at these centers play a crucial role in advancing this new initiative. Finally, the research recommends public awareness and education about the environmental and economic benefits of this new service, mapping consumer purchasing habits, identifying strategic points, providing financial incentives and discounts, and enhancing service quality to increase customer acceptance of self-collection services, which will lead to improved performance of the urban logistics network.

Heart failure is a clinical syndrome resulting from structural or functional abnormalities of the heart, leading to reduced cardiac output or increased intracardiac pressure. When combined with cardiogenic shock, it becomes an emergency condition with a high mortality rate, necessitating immediate diagnosis and treatment. Accurate prediction of 30-day mortality in these patients is vital for timely care and patient survival. This study aimed to optimize the Random Forest algorithm by adjusting hyperparameters to more accurately predict 30-day mortality in heart failure patients with cardiogenic shock.

In this research, data from 201 cardiac patients aged over 18 years who experienced cardiogenic shock at Rouhani Hospital in Babol in 2020, were used. Thirty-four selected features such as age, history of cardiac surgery, pH, lactate levels, diabetes, etc., were examined, and their one-month mortality was tracked through telephone follow-ups.

The results showed that increasing age (above 57 years), decreasing pH (below 7.3), and elevating lactate levels (above 2) significantly increased the risk of 30-day mortality. By optimizing the hyperparameters of the Random Forest algorithm (ntree=1000 and mtry=14), prediction accuracy improved from 66.0% to 71.8%.

This study demonstrates that the accuracy of the Random Forest algorithm depends on its input hyperparameters and that optimizing these parameters can lead to a more precise prediction of mortality in heart failure patients with cardiogenic shock. With appropriate optimization, this algorithm can serve as an effective tool for the early detection of high-risk patients and timely provision.

Recent promotions in online shopping have greatly increased the importance of the e-commerce industry. One of the main parts of this industry is electronic service systems, where businesses use different systems to sell products and services directly to consumers. Therefore, care should be taken to ensure that e-service systems are such that they can create a reliable and long-term relationship between businesses and consumers. Therefore, this study evaluates and prioritizes factors for customer satisfaction in successful electronic service systems. This study uses multi-criteria decision-making and to minimize any ambiguity and grayness in decision-making, fuzzy Delphi hierarchical analysis and gray theory, respectively, with the hierarchical analysis process and technique for order priority with similarity to the idea solution. Al merges. First, this study conducts a comprehensive review to screen for important factors reported in past studies. Five main factors and several sub-factors were selected for further prioritization. Then, the fuzzy Delphi hierarchical analysis model prioritized the factors based on their importance. Finally, based on the results of fuzzy Delphi hierarchical analysis, Gray's theory ranked five options (e-service systems). The proposed model based on fuzzy Delphi hierarchical analysis and gray theory showed the service quality factor as the most successful influencing factor in customer satisfaction.

Today, production, especially in the clothing industry, is undergoing rapid and continuous changes in customer needs. With the increasing customization of products, the production paradigm has shifted to increasing decentralization and socialization. Social manufacturing is a new paradigm in production systems that is decentralized, participatory, service-oriented, collectively-based, intelligent, and customized. In social production, the active customer is identified as a person who is both producer and consumer, and can contribute to the entire life cycle of a personalized product. This study seeks to explain the factors affecting the establishment of a social manufacturing system in the Iranian clothing industry. The purpose of this study is practical and the effective factors on the establishment and implementation of social manufacturingn system were identified by studying the literature and opinions of experts. 21 factors influencing the establishment of this system in the Iranian clothing industry were confirmed. In this paper, a fuzzy Dematel-ISM method is used to identify and investigate the interrelationship between criteria and to classify them. As a result, "shared production business model design" and "production credibility assurance" as well as the "macro clothing industry plan" have the most interaction with other factors such as "training", "cloud manufacturing platform", "configuration and organization platform" and "managing information system and shared data" will have the most impact on the system. On the other hand, factors such as "collaborative production management", "social networks", and "customer social relationship management" are the most influenced by the social production system.

The circular economy as new non-linear and circular view in fields of production and consumption has been able to create many benefits for its supporters and has attracted the attention of many researchers, companies, countries and business decision makers.This research has tried to develop concept with the aim of comprehensively identifying the dimensions and effective factors in the circular economy at the macro, miso and micro levels.present study is applied in terms of purpose and meta-synthesis qualitative method has been used to screen the characteristics. Published articles related to the CE ranged from 2015 to February 2020(search date).A total of 346 English language articles were obtained, of which 113 articles were related to the Web of Science database and 233 articles were related to the Scopus scientific database and after going through the meta-synthesis steps, 104 articles were finally studied. According to the three levels of circular economy, analysis of extracted topics, in the form of goal, strategy, components, requirements, enablers, tools, evaluation indicators, key success factors and classification facilitators dimensions and were presented in the conceptual model. In order to evaluate the agreement between the research codings, SPSS software and kappa test were used and the value of kappa coefficient was 0.847, which indicates the agreement between codings. Among the suggestions of the research, we can mention the education and awareness of the society, the formulation of support standards, the legal and ownership ambiguity of the industrial partnership, and retargeting based on the interests of the environment and society

Social‎, ‎economic‎, ‎and environmental issues such as population growth‎, ‎reduction of natural resources‎, ‎climate change‎, ‎market fluctuations‎, ‎and changing consumer behavior have attracted the attention of politicians to the supply chain of agricultural products‎. ‎Designing an effective supply chain for each product can lead to optimal management of the agricultural sector and create coordination and links between activities‎. ‎In this article‎, ‎the design of a two-echelon supply chain network of shrimp in Golestan province is investigated‎. ‎The objective is to minimize the total cost associated with fixed opening and operating costs of shrimp farming companies and to determine the target market for these producers‎. ‎Also‎, ‎this study involves deciding on the amount of inputs purchased by each company and determining the best mode of transport‎. ‎To characterize and solve this problem‎, ‎we developed a mixed-integer programming (MIP) model that solves with GAMS software‎. ‎The results show that with the implementation of the MIP model‎, ‎the total costs of the chain are reduced by nearly 20 percent compared to the current situation‎. ‎In addition‎, ‎without increasing production‎, ‎it is possible to supply 0.053 percent of global market demand‎, ‎which is 76 percent more than before.

In this article, an approach to optimize opportunistic maintenance policies was presented to examine the use of opportunities created in preventive maintenance activities. After the operation, maintenance, and repair, a component never gets back to the status of a new one. Hence, assuming that the replacement case is not approached, a maintenance activity is referred to as an imperfect type. In this article, assuming the existence of the imperfect maintenance type, an opportunistic approach based on age threshold values of components is proposed. The maintenance activities in this research focus on the hybrid flow shop problem. Different threshold values are also introduced in this article for failure conditions for a machine. A harmony search algorithm is used to provide optimized values for this proposed approach. The simulation approach is used to calculate the average cost of maintenance. The cost analysis indicates that the proposed approach is better than the corrective policy widely in literature; otherwise, the proposed approach with about 25 percent saving is the best performance.

The design and establishment of a logistics network is a strategic decision that lasts several years to work and the parameters of customer demand and return may be changed during this time. Therefore, an efficient logistics network should be designed in a way that can respond to uncertainties. The applications of such a network can be found in different industries like the battery industry. This study aims to determine the number of products sent among the centers at each time so that the total cost of reverse logistics and delay time is minimized. To address the uncertainty in the reverse logistics network (RLN), a fuzzy programming method is utilized. To tackle the complexity of the problem, the cuckoo optimization algorithm (COA) and genetic algorithm (GA) were developed. To compare these two optimization algorithms and find the superiority of them, a series of problem instances were generated. The obtained results demonstrated a satisfactory efficacy for both meta-heuristic algorithms. It was also revealed that the sum of values sent to the main manufacturer is equal to the values obtained from the exact solution method.

A Cellular Manufacturing System (CMS) is a suitable system for the economic manufacture of part families. Scheduling the manufacturing cells plays an effective role in successful implementation of the manufacturing system. Due to the fact that in the CMS, bottleneck machine and human resources are two important factors, which so far have not been studied simultaneously in a mathematical model, there should be a model to consider them. Therefore, this research develops a bi-objective model for CMS in a three-dimensional space of machine-part and human resources. The main objective is to minimize the maximum completion time of all tasks in the system and reduce the number of intercellular translocation based on bottleneck machines’ motion and human resources. Due to the NP-hardness of the studied problem, applying the conventional solution methods is very time-consuming, and is impossible in large dimensions. Therefore, the use of metaheuristic methods will be useful. The accuracy of the proposed model is investigated using LINGO by solving a small example. Then, to solve the problem in larger dimensions, a hybrid Multi-Objective Tabu Search-Genetic Algorithm (MO-TS-GA) is designed and numerical results are reported for several examples.

Integrating production scheduling and scheduling is one of the most important decisions in any production organization that leads to increase the effectiveness of operations management. Long-term production planning decisions (determination of production quantity, inventory level, etc.) and short-term operational scheduling decisions (order of work processing and maintenance activities) have a direct effect on each other. In this study, a new mathematical model for simultaneous decision making of production planning and scheduling is presented with the aim of minimizing the total costs, time and delay in the hybrid flowshop. The concept of imperfect maintenance and considering several types of repair maintenance sources is presented in the proposed model. Two multi-objective meta-heuristic evolutionary algorithms based on the harmony search algorithm and the exact Epsilon-constraint solution method are proposed to solve the proposed model, which continuously searches the decision-making solution space. For this purpose, a sample of test problems of literature review and three performance criteria of Pareto number of solutions, mean ideal distance and Spacing metric are used to compare the results of meta-heuristic algorithms. The results show the advantages and effectiveness of the methods used in determining the Pareto optimal solution for optimization problems.

One of the important issues in supply chain management, vehicle routing and scheduling problem has always been which has so far attracted considerable attention from researchers. This paper attempts to optimize routing and scheduling for a heterogeneous transportation system with considering stochastic supply and demand, various traffic conditions, maximum continuous driving time constraint and soft time window constraint of each customer delivery. The consideration of pollution in routing decisions gives rise to a new routing framework where measures of the environmental implications are traded off with business performance measures. Also, the aim of this study is to minimize energy consumption and to maximize customer satisfaction, simultaneously. At first, this problem is presented in the form of a multi-objective linear programming model and then a novel integrated approach is proposed based on combination of chance constraint method and goal programming. Finally, the results of numerical example are analyzed with sensitivity analysis.
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Companies and firms, nowadays, due to mounting competition and product diversity, seek to apply virtual cellular manufacturing systems to reduce production costs and improve quality of the products. In addition, as a result of rapid advancement of technology and the reduction of product life cycle, production systems have turned towards dynamic production environments. Dynamic cellular manufacturing environments examine multi-period planning horizon, with changing demands for the periods. A dynamic virtual cellular manufacturing system is a new production approach to help manufacturers for decision making. Here, due to variability of demand rates in different periods, which turns to flow variability, a mathematical model is presented for dynamic production planning. In this model, we consider virtual cell production conditions and worker flexibility, so that a proper relationship between capital and production parameters (part-machine-worker) is determined by the minimum lost sales of products to customers, a minimal inventory cost, along with a minimal material handling cost. The problems based on the proposed model are solved using LINGO, as well as an epsilon constraint algorithm.

In this competitive world, understanding the competitive structure of the market is the mystery of the survival and success of organizations, and the companies are trying hard to increase their sales. Distribution channels in marketing are one of the key issues in modern companies especially in distribution companies, therefore it is vital to have enough information about it.Transmission of manufactured goods to the target market is one of the most important challenges for marketing managers and manufacturers.The purpose of this research is to identify and rank the effective measures in capillary distribution that have the greatest impact on attracting and retaining customers, so that they can help the manger to make marketing decisions by identifying and prioritizing them. In terms of purpose, it is an applied research within the descriptive research’s framework, because the researcher describes the parameters affecting the capillary distribution channel which have been done in some food distribution companies such as Kale, Gela and Lia. The desk research and questionnaire method have been used to identify the effective criteria in capillary distribution channel, according to the Morgan table among the 200 experts 127 numbers were studied as examples. According to the advertising results, the same price across the whole distribution range and high margin for retail and wholesale are ranked first to third, also in this ranking market dynamics and continuous introduction of new products, the length of distribution channel and distribution spread in distant distance were the least important parameters.

This article examines quality management on the value of the company in the life cycle and compares it with counterparts without quality control. For this purpose, in order to measure the quality management pattern among quality systems, ISO 9001 certification, which is related to the quality control system, was used. And companies that did not have ISO 9001 certification at the time of review were also companies that did not have quality control. Ciotobin ratio criterion was used to measure the value of the company. The statistical population of the study is the manufacturing companies listed on the stock exchange that had received ISO 9001 quality control certification before the research period from 2012 to 2018 and also the companies that did not have ISO 9001 certification during the research period. To analyze multivariate regression equations, the panel data method was investigated and tested with the fixed and random effects approach. The results of this study showed that obtaining ISO could have a significant effect on the value of the company in the growth period. But in the maturity period could not have much change in the value of the company.

Here, a new mathematical model for cellular manufacturing systems considering three important features of part priority, levels of machine’s technology, and the operator’s skill is developed. Simultaneous consideration of these features provides a more realistic analysis of the problems in cellular manufacturing systems. A model with multiple design features including cell formation, human resources flexibility with different skills, machines flexibility, operational sequence, processing time, and the capacity of machine and manpower is proposed in this article. Ourfocus is on the design of cells to implementtwo dissimilar goals. The first goal is the reduction of inter-cellular movements of parts and workers. The second goal is the creation of efficient cellsby making cell’s quality level identical for produced products so that the production of all the different parts have good quality. Two approaches of augmented ε-constraint and non-dominated sorting genetic algorithm II (NSGA-II) are used to solve this model. By comparison of these two approaches, we realizethat the multi-objective evolutionary optimization algorithm creates a Pareto-optimal front in a reasonable amount of time forlarge-scale problems

One of the most important aspects of human resource management is the allocation of the workforce to activities. Human resource assignment to project activities for its scheduling is one of the most real and common issues in project management and scheduling. This becomes even more significant when human resource assignment to multiple projects simultaneously is considered. On the one hand, workforces can have multi skills due to technological and scientific development so that they can be assigned to project activities based on their skill level. On the other hand, the learning effect is also taken into account to make the model more realistic. These factors can affect completion time, total cost and execution quality of projects. In this study, a multi-objective optimization model for multi-project scheduling and multi-skilled human resource assignment problem based on the learning effect and activities' quality is presented. A mixed-integer linear programming model (MILP) is developed for the proposed problem and solved by the ε-constraint method in GAMS software. Managers can select a solution based on their priority. Finally, a sensitivity analysis is done on the learning and forgetting effect to investigate their impacts on each objective function.

Recently, public bicycle sharing system (PBSS) has become one of the most favorite urban transportation systems that can help governments to decrease environmental problems such as pollution and traffic. This paper studies a sharing system that includes two types of stations. The first category contains stations that users can rent or return back bicycles and each bicycle can be rented by any new user who arrives to the stations. The second group is the stations which are near shopping centers, historical and other places that users and tourists can stop and visit them. These stations are used only for parking the rented bicycles for a period of time and after that, the users must ride their bicycles and turn them back to their destination stations. After discussing the network of the model under the closed Jackson network, the Mean Value Analysis (MVA) method will be used to calculate the mean queue of each station and analyzing the proposed model.

Public Bicycle Sharing System (PBSS) is used as a way to reduce traffic and pollution in cities. Its performance is related to availability of bicycles for picking up and free docks to return them. Existence of different demand types leads to the emergence of imbalanced stations. Here, we try to balance inventory of stations via defining maximal response rates for each type of rental request. If the maximal response rate for a destination is lower than 100 percent, a part of the proposed destination requests is rejected in the hope of balancing the inventory. The goal is to minimize the mean extra inventory and the mean rejected requests by providing proper amounts of the maximal response rates. An approximation method named as Mean Value Analysis (MVA) is used to develop a genetic algorithm for solving the problem. Different examples are worked through to examine the applicability of the proposed method. The results show that the proposed policy leads to a significant improvement and reduces the users’ dissatisfaction.

One of the main concerns of all industries such as mine industries is to increase their profit and keep their customers through improving quality level of their products; but increasing the quality of products usually releases air pollutants. Nowadays the management of air pollutant emissions with harmful environmental and health effects is one of the most pressing problems. In this paper, authors study the decision behaviour and coordination issue of a mining metal three-level supply chain with one supplier (extractor), one mineral processor and one manufacturer. We compare the decentralized and the centralized systems and reduce air pollutant emission by designing a revenue sharing contract for the mentioned decentralized supply chain under cap-and-trade regulation. Finally, a numerical example shows that the designed contract not only provides win-win condition for all supply chain members and increases whole supply chain profit but also reduces harmful air pollutant emissions in the supply chain.

Cell formation problem (CFP) is one of the main problems in cellular manufacturing systems. Minimizing exceptional elements and voids is one of the common objectives in the CFP. The purpose of the present study is to propose a new model for cellular manufacturing systems to group parts and machines in dedicated cells using a part-machine incidence matrix to minimize the voids. After identifying the exceptional elements, the machines required for processing the remained operations of corresponding parts which are not processed in the dedicated cells are specified. This results in a new matrix called part family-machine. Then, by clustering the part family-machine incidence matrix, the part families that should be assigned to a specific cell to achieve the highest similarity can be determined. The similarity can be translated to sharing machines required for completing the processes and form new cells called shared cells to minimize the number of exceptional elements and voids. Unlike previous models in which the similarity is considered only in the dedicated cells, in the proposed model, the similarity would be monitored and observed in the entire production process. Due to the complexity of our model, two meta-heuristic algorithms including artificial immune system (AIS) and simulated annealing (SA) are proposed. The efficiency of the algorithms is compared to that of exact solutions. Also, the algorithms are compared regarding the quality of solutions. Finally, according to grouping efficacy measure, SA algorithm has a superior performance in comparison with AIS by spending less CPU time.

Today, customer attention and tips that can improve communication with them, is considered a very important issue for all organizations. Meanwhile, the quality of work life of employees is considered as one of the most important factors that can affect customer relationship management. Therefore, in the present study, we decided to investigate the effect of quality of work life on customer relationship management among sales staff of Kaleh Company so that we can finally provide useful information to managers and policy makers of organizations. The present study is applied in terms of purpose and in terms of collecting survey information and using a questionnaire, which is used to assess the quality of work life from the 34-item Walton (1973) questionnaire and to assess customer relationship management from the 14-item Abbasi questionnaire (2009). ) Used. The statistical population in this study is all employees of the sales department of Kaleh Company, whose number is 100 people and using Krejcie and Morgan table, 80 sample people have been selected by simple random sampling. Spss 24 and Smart PLS software were used to analyze the data and test the hypotheses and research model. The research results indicate that the quality of work life and all its dimensions and components have a positive and significant effect on customer relationship management among the sales staff of Kaleh Company.

Analyzing eyes performance is essential for effective functioning of human. Therefore, following their motion could help doctors to make quick and accurate diagnoses for disorders like Autism, schizophrenia, or attention deficit hyperactivity disorder. Recently, several studies investigated autism disorder diagnosis and treatment. Meanwhile, various algorithms have been provided for eye tracking. In this paper, it is intended to identify diagnosis parameters of autism disorder using eye tracking concept. The eye tracking algorithm that has been used in this research is simple and sufficient accurate to appropriate function on videos with varying quality. The direct analysis of gaze and study of the interactions of its features are employed a useful method for diagnosis of autism. For this purpose, two separate groups of ordinary children and children with autism are considered. By tracking their eyes while watching television and performing the necessary analyses then their eye movements are compared and discussed. To identify pupils is face detection Viola Jones algorithm is implemented.

This paper describes an application of fuzzy multi-objective quadratic model with flexible constraints for optimal allocation of limited available water resources among different water-user sectors. Due to the fact that, water resource allocation problem is one of the practical and essential subjects in real world and many of the parameters may be faced by uncertainty. In this paper, we present α - cut approach for transforming fuzzy multi-objective quadratic programming model with flexible constraints into a crisp form. By using this approach a multi-parametric multi-objective programming model corresponding to α and parameters of flexible constraints is obtained. One of the advantages of this model is that the α - cut level is not determined by the decision makers. Actually, this model itself can calculate the α - cut level. In order to achieve a desired Pareto optimal value of multi-parametric multi-objective model, we use goal programming method for illustration of water resource allocation with sensitivity analysis of lower bound of parameters in flexible constraints. To illustrate the efficiency of the proposed approach, we apply it for a real case problem of water resource allocation.