atefeh amindoust

This research explores the use of machine learning to predict public transportation demand in Mashhad, Iran. Predicting demand is crucial for optimizing operational plans and ensuring efficient service delivery. The complex nature of travel patterns necessitates a model that can account for both spatial (geographic) and temporal (time-based) factors. The developed model utilizes various spatial and temporal data points, offering flexibility and adaptability. The study compares four models built with different datasets. The research employs Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to identify distinct travel patterns within each time period across the city's 253 traffic zones. Additionally, the random forest algorithm is used to identify and select the features that affect the demand variable. The most effective model leverages spatial data on an annual scale, resulting in highly accurate predictions (training error: 0.331, testing error: 1.095). This model allows planners to estimate public transportation demand across different traffic zones, both daily and annually, in response to potential changes in urban land use.

Due to the complexity of operating conditions, predicting corrosion in natural gas pipelines is a challenge, and therefore its use in the gas industry is problematic. So, the present study employs corrosion risk management in control systems and decision prediction with fuzzy inference systems to address the uncertain phenomena of critical factors affecting natural gas pipelines. In the study, 84 factors were derived and 14 critical factors were identified using the Lawshe approach. The Boehm risk management framework was used and the factors were analysed in pairs in a fuzzy inference system. The probability and consequences of each factor were determined according to the API 581 standard and the weight coefficient of each factor was determined using the FUCOM method. In this study, a fuzzy inference system with two fuzzy inference subsystems was developed to comprehensively address both aspects of evaluation and response. First, the system uses the API 581 standard risk matrix to identify the risk level, followed by the risk response strategy determination through the sensitivity risk priority index chart. The results show that at the first level of the system, the highest risk was classified as severe and harsh, while at the second level of the system, the maximum output behavior occurred during the wear-out phase of the pipeline. The proposed fuzzy inference system has the potential to significantly contribute to the effective management of pipeline corrosion risk and the economic productivity of the gas industry.

The area coverage of machines on the production line to address the scheduling and routing problem of autonomous guided vehicles (AGV) is an innovative way to improve productivity in manufacturing enterprises. This paper proposed a new model for the optimal area coverage of machines in the production line by applying a single AGV to minimize both the transfer costs and the number of breakpoints of AGV. One of the unique advantages of the area coverage employed in the present study is that it minimizes transfer costs and breakpoints, and makes it possible to provide service for several machines simultaneously since the underlying assumption was finding a path to ensure that every point in a given workspace is covered at least once. Since rail AGV is used in this study, AGV can only pass horizontal and vertical distances in the production line. The reversal of the AGV path in vertical and horizontal distances implies failure and breakpoint in the present paper. The simulation results confirm the feasibility of the proposed method.

Gas distribution networks are considered essential. Therefore, considering the importance of the implementation of such projects and the challenges raised in this field, the present study tries to evaluate the key criteria of risk management in the implementation of network projects by comparing two fuzzy multi-indicator decision-making techniques. Pay for the distribution of natural gas. For this purpose, by selecting a number of 36 experts and experts in the field of natural gas industry, using a researcher-made questionnaire, Lawshe method and multi-indicator decision- making techniques based on fuzzy SWARA and fuzzy best- the worst method were used. The obtained results indicate that, out of 32 risk management criteria, 17 criteria were identified as key risk management criteria. Also, based on the results, it is concluded is consistent that the final and definitive weight of the criterion of inadequacy or lack of technical information for design in carrying out the fuzzy best- the worst method multi-indicator decision-making technique with the second rank of the final and definitive weight obtained in the fuzzy SWARA multi-indicator decision-making technique and has been evaluated with the highest importance and the final and definitive weight of the criterion of the inappropriate evaluation process and the selection of suppliers and contractors in two multi-indicator decision- making techniques has been evaluated with the least importance. They share meaning with each other.

In any country's economy, the distribution is one of the most important industries and infrastructure. The industry's 8% portion of national income, explains the wide range of this industry and consequently the key role of this industry in the supply chain of many industries in the country. Not using all the capabilities of the distribution network and ignorance about distribution enablers will lead to chain costs failure and increase supply. In today's condition, maintenance and continuity of activities in the distribution network are considered as important subjects in a supply chain, and the reliability of the distribution network in a supply chain is grounded in recognition of the enablers. Identifying and prioritizing the distribution network enablers precede the development and proper implementation of the strategies and plans of a distribution network. The fuzzy logic has become a convenient tool for prioritizing due to the necessity of the comprehensive view to the supply chain, the uncertain space of it, and inconsistency in the views of decision-makers. This research tries to identify and prioritize the empowerment in order to direct and supply resources and thus to increase the productivity and effectiveness of the country's industrial distribution network.The theoretical framework of the study is based on the extracted enablers from the literature and selecting a final set of them by using the Lawshe method. The group of experts is comprised of 11 experts in the welding and cutting industry. The enablers are prioritized using the fuzzy BWM method and Lingo software. The results indicate that the most influencing factor on the distribution network is "on-time delivery" and the "logistic infrastructures" factor is the least important among the factors. The resulted prioritization could be used as a guideline for a better perception of the activities related to the distribution network.

Advancement of jobs towards professionalism, doubles the importance of encountering with moral conflicts and adopting decisions which are in accordance with the engineering ethics. Observing the mentioned ethics becomes possible by determining the factors of professional competency and then observing them. Thus, the current research aims to identify and prioritize the components of professional competence on the basis of engineering ethics using the fuzzy Delphi approach combined with the exploratory mixed method. At the first stage, professional competencies of engineering, in the form of 14 components, were extracted from the literature of related research and 9 codes in the area of engineering ethics. Professional competencies of industrial engineering were identified by the analysis of content and interviewing 15 experts from the industrial engineering community. The fuzzy Delphi approach has been used in order to reach a group consensus among the experts. The findings have demonstrated that “safety, society’s well-being, sustainable management of resources, responsibility, rendering services in accordance with one’s level of competence, avoiding the conflict of interests, neutrality and honesty, justice, experience, maintaining and developing the level of knowledge and skill, having a cognizance of technology, tendency to learning and nurturance, improving the status of engineering profession, being respectful to people’s personalities, and supporting the professional councils”, have consecutively the highest priority. The results of this research have been effective in framing the moral ethics codes of industrial engineers.

The effective selection of maintenance tactics in the industry according to the factors and conditions affecting each of the equipment, shows the need to use innovative approaches. The purpose of this study is to obtain a matrix similar to the TRIZ contradictions matrix so that with the help of this matrix, appropriate maintenance tactics and functional repairs can be selected according to the condition of each equipment. In this study, in order to compare and analyze qualitative data, the , Meta-synthesis method has been used, so for this purpose, various databases, publications and search engines between 1960 and 2022 have been studied. The result of the studies performed using the , Meta-synthesis method is the extraction of 115 valid and related articles. In this research, the relationship between TRIZ principles and operational maintenance tactics has been obtained using the , Meta-synthesis method.The result of this research is the achievement of a matrix similar to the TRIZ contradiction matrix with the ability to provide and select maintenance tactics, corresponding to the parameters affecting each equipment. In order to make the right decision, the final results in each of the matrix cells of this study are prioritized using decision making methods. This matrix assists managers and maintenance specialists in decision-making and increases the speed of decision-making and selection of appropriate tactics. . Finally, the resulting matrix has been studied for evaluation in the National Iranian South Oilfields Company.

The application of renewable energy sources such as Photovoltaic S ystems (PV) can be effective in minim izing damage to the environment . As the use of PV systems increases, questions and concerns about higher quality an d reliability have been raised. The aim of this study, which has been conducted in the high - tech electronic industry, is to select the optimal components for designing photovoltaic systems. It has been done to achieve goals such as increasing customer satisfaction and system efficiency, reducing the overall cost and procurement time of the system. In this regard, after extracting Customer Needs from the first stage of the systems eng ineering process, they have been interpreted to Functional Requirements using the first matrix of QFD. Then, the FRs have been prioritized by use of Analytical Network Process and entered the second matrix of QFD. They have been examined along with leveled components based on the alternatives available for each component. Also, the Design Structure Matrix has been used to evaluate the effect of elements upon each other. Finally, a mathematical model is developed to select optimal components according to the defined objective functions and constraints. After solving the model in GAMS software, the results indicate that type B of Solar Panels, a type E of Controller, a type F of Combiner Box, a type H of Inverter, type L of Batterie s, type Q of Disconnects, and type T of Miscellaneous Components must be selected to achieve mentioned objectives.

The overall equipment effectiveness index is a key indicator and basis used for measuring the effectiveness of equipment and examining the effectiveness of the maintenance system. Improvement of the OEE index is of great importance in production operations management, since it can enhance and upgrade the level of organizational equipment management. In this study, data envelopment analysis model has been developed to evaluate the overall equipment effectiveness index.

The present descriptive-analytical study has been conducted on the kiln of Mazandaran Cement Factory. The overall equipment effectiveness indices, including three inputs and one output, have formed the axis of the model.

Improvement of the OEE index is of great importance in production operations management, since it can enhance and upgrade the level of organizational equipment management. In order to achieve better efficiency, it is recommended to focus on the key components of the overall productivity maintenance and productivity system, implement the SMED method, integrate the two approaches of Lean Production and Six Sigma production, and improve the Process Capability (PC).

Originality/Value:

 Given the importance of achieving equipment effectiveness, there is a need to evaluate the overall equipment effectiveness. Using data envelopment analysis models, in addition to relative efficiency, determines the weaknesses of the organization in various indices, and by providing the optimal amount of them, leads the organization's policy towards improving efficiency. Due to rapid developments in today's world as well as the importance of the overall equipment effectiveness and its obvious impact on the organization's performance and, consequently, its success, the researchers decided to evaluate the overall equipment effectiveness using data envelopment analysis

This study examines the cost, customer satisfaction and environmental protection to design a multi-objective optimization model using system dynamics and to provide a system dynamics model for multi-objective production-inventory-routing in the green supply chain under uncertainty. Considers.By customer demand for several periods, the model can make decisions by focusing on decisions such as the choice of supplier and retailer according to the distance between them, production models and the nascent level of transportation technology. For this purpose, to collect information, first using previous studies, the variables affecting the model (20 variables) were determined, then according to the experts, these variables were analyzed, then the relationships between the selected variables using the model. Cause and effect were identified and then the research modeling was completed by designing a system dynamics model and evaluating it through tests defined by execution in vensim software. Finally, three scenarios were developed to determine the strategies affecting the model. The results indicate the most effective strategies in achieving the desired situation, maximum customer satisfaction, minimum cost and inventory and maximum production with proper implementation of ongoing projects in the direction of green production using appropriate technical knowledge.

The capability to receive and deliver customer demand on time in today's competitive world is a significant concern for all industries. In particular, demand management has entered a new era with many companies competing in the last decade. Customer demand management is one of the contemporary issues. The main goal of demand management is to improve supply chain effectiveness, and it is important to note that it is complementary to distribution management and product demand management. Therefore, demand forecasting is essential. For this purpose, in this study, the modeling of the combination structure using autoregressive integrated moving average models and multilayer perceptron neural networks in the field of demand with benchmark data is investigated. The data sets used in this study are two well-known benchmarks of the total product revenue of the Taiwan machinery industry and the sales volume of soft drinks. Eviews and Matlab software have been used to determine the unknown parameters of the proposed model. The experimental results of the research show that the performance of the proposed hybrid model is more accurate than its single components. In addition, results indicate that intelligent models can perform better than classic statistical models.

Nowadays, energy consumption management is a vital strategy for societies to optimize energy consumption. Considering the widespread functions of electrical energy in human lives,a high proportion of consumption management procedures deals with the management of electrical energy consumption. It is important to study the state of electrical energy and provide managerial and optimal management solutions in this industry. The main purpose of this study is ranking the key solutions of reducing electrical energy consumption in Isfahan Sepahan Cement industry. In this research, firstly, by studying and investigating conducted researches, interviewing with cement industry experts and alsoa researcher-made questionnaire, 20 solutions and 5 indices are chosen. Then by utilizing the indices weight and fuzzy TOPSIS method, solutions are ranked and in conclusion, after conducting needed calculations, “Hot exhaust gases heat recovery and electricity generation” obtained the highest rank and “Revision of various energy tariffs” scored the lowest rank.

Competition is the driving force of today's economy and it is vital for organizations to gain competitive advantages. In recent years, outsourcing has been the focus of managers as a tool for developing organizations and improving productivity. This study deals to analyze the outsourcing risks of the supply chain in the automotive industry using fuzzy inference systems. For this purpose, in the first stage, outsourcing risk identified using research literature, then to summarize, a questionnaire is designed and provided to the experts of the automotive industry. Some of the factors were eliminated by experts’ opinion and finally, nine main criteria (preparedness, selectivity, implementation, organizational output, supply, production, distribution and environment risk) and 43 sub-criteria were identified. Then, for each risk, three components of severity, probability and seriousness were measured and performance analysis criteria were identified and weighted by experts in the fuzzy hierarchical process. Research results after prioritizing, show that "inappropriate planning" with the weight of 0.029921 is in the first priority and "production capacity" and "the danger of war" risks with the weight of 0.02964 jointly were in the second priority.  Finally, using a fuzzy inference system, the outsourcing risks in the supply chain of Saipa automotive company is analyzed to get the performance evaluation.

The main purpose of this study is designing an open innovation model in active small and medium-sized enterprises (SMEs) of national food industries. The research is conducted through grounded theory method. Semi-structured interviews with 25 managers (practical field) and academic experts (scientific field), selected by theoretical sampling method, were used for creating the sample. Codes and components of open innovation (355 key concepts, 34 sub-categories, and 11 main aspects) were derived from the interviews and were codified in a conceptual model based on grounded theory. Based on the research findings, the central category was “open innovation”. The casual conditions contained two dimensions, namely internal factors (key capabilities, organizational dimensions, modern research, management of intellectual property management, characteristics of SMEs, and characteristics of food industries). External factors contained business model and environmental changes. The desired strategies contained facilitative strategies (searching, boundary-spanning, outsourcing, networking, monitoring partnership), technological opportunities strategies including input, output, and interactional. The contextual conditions included organizational strategies (manager’s personal characteristics and cultural infrastructures), and the environmental conditions (market dynamism and legal infrastructure). The interfering conditions affected strategies via challenges of resources, dynamic capabilities and uncertainty. Finally, they led to internal consequences (improvement of organizational preparation, recruitment capacity and cultural infrastructures), external consequences (observation, environmental intelligence, improvement of business model, improvement of cooperation capabilities and improvement of outsourcing), and interactional consequences (co-creation).

The organizations providing health services have a significant role in the society. Organizations the utilization of the electronic process can improve the quality and capacity of services and so the level of in the health society. This study identifies and prioritizes the key factors to implement the electronic logistics in the Isfahan Health City.
by reviewing the literature, 28 potential factors for implementation of E-logistics were extracted. After that, the degree of significant of these factors was determined according to the expert using group decision making. to screen the in significant factors a method based on K-means clustering was used. Then using a recent method for ranking the fuzzy numbers, which is based on the interval of the fuzzy numbers and the center of gravity, the space the factors were prioritize according to the overall cost effectiveness index.
by K-Means clustering method 8 factors have been screened and the 20 remaining factors were prioritize according to the overall cost effectiveness index. After calculating the distance of each factor with maximum and minimum range of factors, the vision factor with score 3.761 and the Maintenance factor with score 0.093 were identified as the most important and the least important factors respectively.
In this study it was found that vision, create project teams, strategy and management support are the most importance factors for the implementation of E-logistics.