ahmad jafarnejad

This study aims to apply automatic machine-learning approaches using genetic algorithms to enhance heart disease prediction. Heart disease has remained the major cause of mortality in the world, necessitating an effective and timely diagnosis. Most current diagnostic and assessment processes are lengthy and expensive, relying heavily on clinical expert knowledge. To help address these issues, machine learning approaches, which derive their utility from examining substantial datasets for the recognition of patterns, have emerged as a potential solution, providing solutions beyond those achievable by human recognition alone. Genetic algorithms are also suited to addressing these issues as they mimic natural evolution to perfect high-caliber machine-learning models, feature selection, and parameter selection in machine-learning applications. This study examines the utilization of genetic algorithms working alongside AutoML frameworks to improve accuracy in heart disease predictions. Reducing to the best combination of attributes and the optimum parameters for each attribute is a time-consuming task, so automating this aspect of the process allows for more accurate and prompt predictions, consequently reducing the manual work. The AutoML approach followed in this research is TPOT, which uses genetic algorithms to ascertain optimally designed machine-learning pipelines. The application of AutoML, together with genetic algorithms, is the most prominent finding that yields a significant improvement in the quality of the predictions for heart disease compared to the traditional assessment approaches, with an accuracy of 93.8%. This approach will enhance diagnostic accuracy and enable early diagnosis, thereby reducing the likelihood of misdiagnoses or ineffective treatments and ultimately lowering associated costs.

To effectively manage and guide inter-organizational innovation networks, the role of the orchestrator is crucial. Typically assumed by the focal firm, this position necessitates continuous assessment and enhancement of orchestration capabilities. For a strategic, long-term approach, these capabilities should be evaluated at two levels: static (during the network's launch and daily operations) and dynamic (during fundamental changes in the network's primary variables). This research aims to present a model that elucidates the dynamics of orchestration capabilities within large financial companies and banks transitioning to open banking. The study focuses on the shifts in orchestration capabilities in response to the open banking paradigm, introduces emerging capabilities within this new framework, and examines various dimensions of orchestration roles throughout this evolution.
 
This research employs a case study method focused on the banking industry, specifically targeting successful banks at the core of innovation networks utilizing open banking. The level of analysis is centered on the adoption of open banking, while the unit of analysis is the orchestration capabilities within these innovation networks. Recognizing the open banking paradigm as a disruptive factor, 19 semi-structured interviews were conducted to analyze five selected banks, chosen based on four criteria, acting as orchestrators of banking innovation networks. The orchestration capabilities were coded and identified through content analysis across three distinct time periods: closed banking, migration period, and open banking.
 
In this study, middleware capabilities were identified and explained through a dynamic model across three time periods: closed banking, the migration phase, and open banking. Four main themes of capabilities were identified for closed banking, while a single theme titled "network transformation management" emerged for the migration phase. For open banking, 22 new categories were identified under four main themes: network design and launch, platform network value induction, platform legitimacy, and platform network participation. By comparing and analyzing the results across these periods, emerging capabilities were introduced, certain traditional banking capabilities were eliminated, some capabilities remained unchanged, and others were enhanced by adding new and unique concepts and codes.
 
From a theoretical perspective, this research provides a comprehensive set of roles and capabilities essential for orchestrating innovation networks. It examines the darker aspects of orchestration with a dynamic perspective, considering technological paradigm shifts and improving previous theories. The study categorizes all the roles and capabilities required by orchestrator banks in both traditional/closed banking and open banking paradigms. Consequently, it identifies and presents the various dimensions of platform-based innovation network orchestration, relying on the orchestration capabilities of open banking platforms. According to the findings, it is crucial for the focal company to adapt its orchestration capabilities in response to technological paradigm shifts to maintain a competitive advantage.

The occurrence of events that lead to interruptions in the flow of materials, even if these events happen in a distant place, can cause disruptions on a large scale. Disruptions are sudden and unexpected disturbances caused by various factors such as natural disasters, fires, loss of vital supplies, war, cyber-attacks, economic recession, sanctions and economic shocks, terrorism, epidemics, etc. Humanitarian supply chain is a special type of supply chain with unique features, which in these conditions plays a very important role in facing, controlling and reducing the effects of disasters. Therefore, the aim of this research is to present a humanitarian supply chain resilience model in dealing with epidemic diseases such the Coronavirus.

The research method is qualitative. Based on this, using the theory derived from the data, the humanitarian supply chain resilience model was formed in organizations providing humanitarian services. The result resulted in deriving the humanitarian supply chain model in dealing with epidemic diseases.

Based on the results of the research, it was determined that the mentioned model has 28 sub-categories in the form of 6 main categories is (causal conditions of resilience, attention to the resilience of the humanitarian supply chain, background conditions of resilience, intervening elements of resilience, strategies of resilience and consequences of resilience).

The supply chain of cellophane includes an independent set of parts whose relationship has to be properly managed to ascertain efficiency, performance, and sustainability. This research aims to provide detailed information regarding the cellophane supply chain and, using the Supply Chain Operations Reference model, act as a benchmark to enhance the adaptability and growth of the supply chain. Therefore, it can help the cellophane supply chain to remove its performance gap.

Design/methodology/approach: 

Interpretive Structural Modelling (ISM) has been used along with the Multi-Criteria Decision-Making (MCDM) approaches, supported by the Analytic Network Process (ANP). ISM has been applied to analyze the interdependencies among different components of the supply chain; DANP has been implemented to prioritize the processes of the supply chain and potential suppliers. An investigation has been also performed on the intensity of influence and dependence among various methods in the supply chain.

Findings indicated that the current market, demand, and competition are the biggest known factors impacting supply chain management. While this is more about learning the present state of the market, it is also important to realize future trends, customer needs, and competition. It includes information on marketing research, customer surveys, and competitive studies. A five-level hierarchical structure of the supply chain processes is reported, with levels one and five as the key drivers. The first level is wherein the market understanding, customer demand, and competitor practices are known. The fifth level involves the identification of potential risks and working out strategies to mitigate the risks. This level is all about active risk management. This involves developing ways through which contingency plans and strategies can be drafted to ensure the impact of the identified potential hazards is reduced. This level is so critical because it has a tremendous impact on performance and sustainability in the supply chain. Identifying potential risks and formulating strategies to reduce them are seen as independent variables with enormous impacts on other processes. These are very important processes as they could affect the effectiveness and efficiency of the entire chain. Both require careful management and continuous improvement to guarantee the smooth operation of the chain.

Research limitations/implications:

 This study concentrated on possible constraints due to supply chain complexity and market changes. Since the cellophane supply chain is dependent on several factors, further research on optimization methods and improvement of decision-making processes can lead to more effective operational results.

Practical implications:

 Findings are likely to give insight into the supply chain of cellophane, with an understanding of the market, customer demand, and competitive landscape. It also goes on to highlight potential risks, and the strategies formulated to reduce them. The use of the DANP technique helped rank the supply chain processes and identify potential suppliers, hence assisting in the effective allocation of needed resources such as materials, labour, and capital. This will assist those individuals concerned with the cellophane supply chain in the process of planning and making strategic decisions. This study contributes to the knowledge of SCM since it has elicited how much one needs to understand the market, manage risks, and ensure compliance.

Social implications: 

Given the significance of sustainability and the need to lessen effects in the packaging sector, this study seeks to enhance procedures and mitigate risks, within the cellophane supply chain. Such efforts aim to safeguard the environment and uphold accountability.

Originality/value:

 This study presented a comprehensive analysis and optimization of the cellophane supply chain using ISM and MCDM approaches. The new integrated approach of ISM and DANP helps to improve management processes and reduce supply chain risks.

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.

Given the escalating complexity of various scientific fields and the competitive nature of markets, the focus on innovation has become crucial for the survival of businesses in the industry. Industries characterized by rapid new product development often undergo shorter cycles of innovation. Mining and mineral enterprises, recognized as long-wave innovators, must not only assimilate new knowledge and technologies but also effectively incorporate them within their organizational processes. The primary aim of this research is to propose a framework aimed at enhancing the technological capabilities of enterprises with long-wave innovation through the integration of novel technological insights.

The method of this research was based on a case study of successful projects in the mining and mineral industries (as an industry with similar characteristics to industries with long-wave innovation). In this study, the analysis focuses on latecomer firms in industries characterized by long-wave innovation, with the unit of analysis being successful projects involving the integration of new technological knowledge. Therefore, an exploratory single-case study method was used. The unit of analysis was selected based on specific criteria, and ultimately, four successful projects in the mining industries were selected and analyzed. For this analysis, 12 semi-structured interviews were conducted, implemented, and coded using the content analysis method.

The findings of this study include a set of activities and initiatives identified by latecomer firms in industries with long-wave innovation in organizational (functional and managerial) and technological (product-centric and process-centric) dimensions for success in technological knowledge integration. These activities and initiatives are introduced in three main stages of identification and transfer of new technological knowledge (including three sub-sections of identifying new technological knowledge, identifying suitable knowledge sources, and transferring new technological knowledge), combining new technological knowledge with the firm's knowledge base (including three sub-sections of external new technological knowledge interpretation, external new technological knowledge sharing, and external new technological knowledge consolidation), and exploitation of new technological knowledge (including two sub-sections of implementation and reconfiguration).

This study reveals that activities such as aligning new technology with operational functions, adopting new technology at the right time to match the firm's knowledge base, maintaining equipment, evaluating product compatibility with international standards, and implementing integrated technology information systems are crucial for firms to achieve greater success in knowledge integration. This study can create opportunities for further analysis and studies for future researchers. It can also serve as a foundation for ongoing research and further studies on technological knowledge integration.

‎In emergency situations‎, ‎accurate demand forecasting for relief materials such as food‎, ‎water‎, ‎and medicine is crucial for effective disaster response‎. ‎This research is presented a novel algorithm to demand forecasting for relief materials using extended Case-Based Reasoning (CBR) with the best-worst method (BWM) and Hidden Markov Models (HMMs)‎. ‎The proposed algorithm involves training an HMM on historical data to obtain a set of state sequences representing the temporal fluctuations in demand for different relief materials‎. ‎When a new disaster occurs‎, ‎the algorithm first determines the current state sequence using the available data and searches the case library for past disasters with similar state sequences‎. ‎The effectiveness of the proposed algorithm is demonstrated through experiments on real-world disaster data of Iran‎. ‎Based on the results‎, ‎the forecasting error index for four relief materials is less than 10\%; therefore‎, ‎the proposed CBR-BWM-HMM is a strong and robust algorithm‎.

Product quality often implies attempts made to exceed customer expectations in the production of goods or provision of services, and incorporates various facets such as conformance, reliability, stability, price, taste and beauty. Product quality includes three variables: design quality, conformance quality and application quality. The present study was an attempt to offer a new classification of product quality factors with respect to three quality variables and provide an interpretive structural model of the quality factors using Meta-synthesis study. The present study falls into the category of applied studies in terms of objective and into the category of qualitative studies in terms of methodology. In the qualitative phase, the meta-synthesis method is used to systematically review 66 researches on product quality factors which have been conducted in 2000 – 2023 interval. The population of the study in the qualitative phase included all valid domestic and foreign researches available in scientific databases. In the Delphi technique, a survey form was used to elicit the opinion of 10 industry and university experts to validation of product quality factors. According to the findings of the study, 40 open codes and 6 axial codes including "management factors", "resources (employees-infrastructure-environment)", "product design and development", "customer focus", "marketing and sales" and "product standardization" were identified as the principal product quality factors.

In recent years, there has been a growing global concern regarding the escalating production of waste. As countries continue to industrialize, the challenge of managing and disposing of waste properly has become a daily issue. Consequently, the fate of industrial goods and products has become a topic of significant interest to consumers. Waste management operations and network design in the automotive industry differ in certain aspects from those in other industries. This difference basically comes from the complex structure of the supply chain in the automotive industry. A large number of sectors are involved in the supply chain, which makes it difficult to control and manage the reverse network. In addition, the high customization in cars means that the parts or components are not the same, and for this reason, it is difficult to predict the recycling of parts or materials. Also, the world is currently confronted not only with the challenges of environmental preservation and sustainability but also with rapid technological advancements in digitization and automation.‎ This study aims to develop an industrial waste reverse supply chain network ‎mathematical model (Iranian automotive industry) using the vehicular ad hoc ‎network (VANET).

To maximize the economic benefits and minimize the environmental and social ‎impacts, a multi-objective (multicriteria) mixed integer programming (MOMIP) ‎facility location mathematical model was developed in the present study for a ‎sustainable supply chain. The economic goal includes aspects such as income and costs within the supply chain, the environmental goal focuses on factors like carbon emissions during transportation and operations, and the social goal encompasses various elements such as annual accident rates, the well-being of drivers, the residential locations of the workforce, and workforce recruitment and termination. Also, to calculate more accurately, the amount of carbon emissions based on the duration of transportation in the supply chain used vehicular ad hoc ‎network (VANET). Real data for the Iranian automotive industry and ‎GAMS were used for model solution.

The proposed multi-objective mathematical model was solved using the ‎enhanced epsilon constraint method in GAMS based on data obtained from the ‎Iranian automotive industry. The results demonstrate the model's validation accuracy and indicate that the proposed model exhibits strong efficiency, making it well-suited for the Iranian case study.

The results showed that the reverse supply chain is efficient over time by ‎considering the recycling of waste products at the same time as the economic, ‎environmental, and social dimensions, as well as taking into account VANET. The model could determine the centers of dismantling facility, processing facility, and recycling according to the objectives of selection and the flow of materials between the centers. ‎Sensitivity analysis also showed that changes to the parameter of culture ‎making to advertise used car sales have a significant effect on the objective ‎functions.

Since the advent of the mobile data network, Iran has more than 35 million social media users. Developing social media among the general public (with a penetration rate of 49% in Iran) and their diverse functions can be the starting point for using social sensing. In this case, users provide data, like a sensor, for analysis. One of the applications of social sensing is crisis management. To achieve this goal, big data collection as a defense system can reduce the human, economic and social costs of crises and events, and can be used as a tool to raise situational awareness and enhance national security. The data set of the present study was collected based on the crawling and text mining of verbal violence in one million and four hundred thousand general Persian channels of Telegram messenger in 1397 SH and after refinement, it was modeled based on the time series of the moving average. To identify crisis signals in this model, the oscillator following the momentum trend (which is mostly used in financial analyses) and the moving average of divergence convergence (MACD) are analyzed. This is the first time in the computational social sciences that this tool has been used to predict security crises and political events and to allow government surveillance.The research findings confirm that at least six social protest in the country in 1397 SH were identifiable and manageable before the event. In addition, a system that can use such analyses in social media big data in real-time would have the necessary efficiency to issue an early warning and to measure the political and security risks of society.

In order to manage and coordinate inter-organizational networks, an orchestrator role is essential. Hub firm typically performs this role in a network and therefore needs orchestration capability. Most conceptualizations of orchestration capabilities in literature are focused on network management, and this meta-capability is often viewed statically. In this study, focusing on the identification and comprehensive collection of orchestration capabilities, the orchestration skills and processes have been categorized.

Systematic literature review is conducted through structured screening, coding, and categorization of concepts based on Prisma. It is classified as an applied research with a qualitative approach using Thematic analysis over the selected articles in innovation management orchestration and checking with Sandelowski and Barroso method.

The orchestration of innovation networks requires 84 capabilities, which have been identified and collected, and a coherent framework and classification was derived by comparing and analyzing different concepts and drawing clear lines in three dimensions namely design, manage and control of the network and seven categories. We also introduced the third dimension of orchestration capability of innovation networks including the analysis of changes and reconfiguration.

Considering the dynamic nature of internal, external and technological factors, The network orchestrator should also review his activities and roles in accordance with this dynamic and update his behavior in order to increase the efficiency of the network. In this way, benefiting from the capabilities of the network control category is mandatory for orchestration.

The impact of cyberspace on human life and the employment of the obtained big data in all social, political, and security aspects of society are among the global trends. The present study seeks to answer the question concerning how governments can become aware of the trend of sensitive issues, which can result in crises, and plan against them using the big data of social media. The goal is to define an early warning system (EWS) architecture in the observation of social media for the early detection of the signals of sensitive and critical issues and assess them. Given the importance of such a system, which is among the governance requirements in the current century, along with its security sensitivities for pioneering countries, no clear information about the architecture of similar examples is available. Thus, creating such a system requires designing from scratch. Accordingly, in the present study, by taking advantage of multi-grounded theory, the early warning system's model is identified. The model is validated through fuzzy Delphi, which approves the proposed model for EWS.

Common oil and gas fields are among the most valuable income and national wealth sources, so production delays cause irreparable damage to the national economy. The existing functions of revenues and production costs of gas fields have been identified and extracted from the current literature to optimize the production strategy. Cost func-tions include exploration, development, operation, production facility and wellhead, facility depreciation costs, and revenue functions include demand and income functions. After designing the model using mathematical optimiza-tion, the decision variables’ values have been calculated as optimal production, selling price, and profit for each play-er. For this purpose, two strategies of cooperation and non-cooperation were considered for each player. Solving the designed games showed that the best strategy and Nash equilibrium for the research case study is the strategy of co-operation. Also, according to the results of the equilibrium of designed games based on cooperation strategy, it is suggested that one of the main approaches of research case study in common fields is the process of multilateral and cooperative development.

The use of ecosystem metaphor in different branches of management, business, innovation and entrepreneurship, are growing increasingly. The evolution property of ecosystems has highlighted. The aim of this study is to identify the stages of the evolution of business ecosystems. The major of previous studies about ecosystem’s evolution are based on case studies and affected by context. Then they are not generalizable. In this study we present a comprehensive model for evolution stages of industrial business ecosystems by qualitative Meta synthesis approach. 16 studies are identified by literature review that they were about evolution stages. Then 105 codes were extracted and translated in 19 conceptions and a 4 stages model was presented. This model, by highlighting the value chain and industrial platforms, is a good model for industrial business ecosystems. Also industrial researchers can use it to design a pattern for a special industrial ecosystem, or they can use it for studying and evaluating of the existent industrial ecosystems.

Today, productivity is one of the most important drivers of economic growth. At the organizational level, a high level of productivity has indicated optimal performance to gain a competitive advantage. Despite the significant role of the productivity of service organizations in economic growth, few studies have been conducted in this context. Therefore, this study aims to develop a model for a comprehensive analysis of productivity in the service industry. 

In this research, the Multi-Grounded Theory (MGT) approach has been used. First, the Meta-synthesis approach (theoretical grounding) has been used to accurate an in-depth review of a wide range of separated quantitative and qualitative research results; then, in empirical grounding, 13 experts were interviewed (the integrated methods of purposive sampling and snowball was selected). To identify the factors affecting the evaluation of service productivity data has been collected from the industry experts. The findings were then combined to achieve a comprehensive view of the various dimensions of service productivity assessment. 

The results of this study have led to the development of a model for analyzing and evaluating service productivity. In the developed model with a comprehensive view of internal and external causal factors, the quantitative and qualitative indicators were determined for service productivity evaluation from the perspective of both service provider and customer. Also, internal and external organizational measures and strategies, intervening conditions, and internal and external organizational contexts affecting internal and external organizational strategies and consequences resulting from the correct evaluation of productivity in the service industry have been analyzed. Research limitations/implications: One of the limitations of most qualitative studies is the generalizability of the findings. Since the findings of this study included the result of the analysis of the views and experiences of selected experts and previous theories, the generalizability of the findings should be performed with caution. To prevent the limitations related to the analysis and the analyst, and to ensure more certainty in the findings, experts participated in the study in two stages, and a valid questionnaire was developed. The results were evaluated from the perspective of "comprehensibility", "comprehensiveness", "applicability" and "innovation". The results of the surveys also indicated the validity of the final model. Due to the time of the research in the period of the outbreak of COVID-19 disease, the researchers faced many limitations for interviewing experts, and therefore most of the interviews were conducted by calling the respondents via telephone. Practical implications: This study helps to use an integrated approach in evaluating organization productivity, despite the quantitative and qualitative indicators in both the customer and service provider dimensions. It leads to increased productivity with full knowledge of this indicator. Also, considering that the proposed model can show a picture of the company's productivity status, the results of the implementation of the introduced model are helpful in carrying out strategic planning and adopting improvement approaches to increase the organization's productivity. Social implications: Due to the significant growth of services in GDP and considering the influencing contribution of productivity in the economy and improving living standards, this study recommends a comprehensive view of service productivity. The proposed model is recommended for implementation in the service organizations depending on their type (i.e., mass, educational, professional) to adapt the indicators and productivity evaluation criteria. 

In this study, to develop a model for productivity analysis in the service industry, an attempt was made to identify the quantitative and qualitative indicators affecting productivity evaluation, comprehensively. Also, in this study, productivity evaluation was performed from both service provider and customer perspectives, because the customer is a part of the service process. Given the important role of productivity in the growth of organizations in any operating industry, and the small number of studies conducted in this field in Iran, the main contribution of this research is to create a deeper understanding of the field of service productivity.

This study was conducted with the objective of analyzing genotype × environment (GE) interaction on grain yield of 20 bread wheat promising lines. The experiment carried out using randomized complete blocks design with three replications in eight experimental stations in temperate regions of Iran in two cropping seasons (2015-2017). To assess the yield stability and adaptability of genotypes non-parametric ranking statistic and some univariate stability statistics as well as GGE biplot method were used. Combined analysis of variance revealed that genotype × year × location and year × location interaction effects on grain yield were significant. Mean comparison showed that promising lines; G16, G12, G10 and G15 had the highest grain yield, respectively. Nonparametric ranking analysis showed that genotypes; G4, G12, G14, G15 and G16 with the lowest ranks and standard deviation of ranks had the highest yield stability. The GGE biplot analysis differentiated environments into three sub-environmental groups with top-yielding genotypes. Using GGE biplot showed that genotypes; G2, G3, G13, G14, G18, G4 and G1 had higher grain yield stability, respectively. Based on non-parametric ranking statistic and GGE biplot, genotype G14 was identified as genotype with highest yield stability. According to the results of this tsudy genotypes G14: KAUZ/LUCO-M//PVN/STAR/3/Yaco/2*Parus/4/Pishtaz (Amin), G15: FRNCLN/ROLF07 (Farin), and G16: MUU/KBIRD had high grain yield potential and stability performance and have been recommended for release as new cultivars for the temperate agro-climatic zone of Iran.

The majority of Drilling equipment in oil gas industry is considered as Complex Products and System. Specific capabilitiesIn the oil industry, most downhole drilling pieces of equipment are considered complex product systems. During the last decades, the National Iranian Drilling Company has made considerable efforts to acquire knowledge and technology of manufacturing related to directional drilling and reserves evaluation equipment. In this regard, this study uses the qualitative case study research strategy to examine the stages of technological capability formation for the production and development of diversion drilling equipment in this company during 27 years (1992 to 2019). According to the research findings, the company has achieved the necessary technological capabilities to manufacture equipment, despite the limitations in international cooperation. Also, these capabilities have been formed in three stages: 1) cooperation with foreign firms; 2) purchasing, installation, and exploitation of the imported equipment; and 3) independently manufacturing by developing an innovation network.

In recent years, the relationship between the concepts of operations management and finance management has been an attractive area of research among researchers. One of the emerging areas at the beginning of the 21st century in the literature of operations and supply chain management is the topic of supply chain finance (SCF). SCF is a new concept that provides efficient financing of the supply chain, where all parties can balance the working capital and improve cash flow at a reduced cost by utilizing the buyer's or other parties' credit rating. Hence, in this study, an approach to optimize financing based on the Stackelberg model in a three-level supply chain, considering the circumstances in which the supplier is financially constrained for fulfillment the buyer's order and funded by the bank as another member of the supply chain based on the purchase order financing (POF) is discussed. For this purpose, a nonlinear mathematical programming model has been developed to maximize the payoff function of the partners.

To achieve agile manufacturing in any manufacturing industry, needed to measure agility in that industry. The purpose of article is to identify and explain the main factors for measuring agility in the automotive industry as the most important non-oil industry. This research is an applied research in terms of purpose and a mixed qualitative-quantitative research in terms of method. The statistical population of the research in qualitative section is the academics and the automotive industry managers, and in the quantitative section is the experts of the mentioned industry. The method of collecting information has been through a questionnaire. In the qualitative section, First, by researching related articles by meta-synthesis method, 89 sources were selected and 27 criteria for measuring agility were identified. Then the criteria were generalized to the automotive industry by surveying 16 experts using fuzzy Delphi method. Then these criteria were categorized into 4 general clusters of agility drivers, agility enablers, agility strategies and agility capabilities using cognitive mapping method and with the help of 5 industry and university experts. In the quantitative part, by distributing questionnaires and surveys of 286 experts in the automotive industry, the extracted factors were validated by confirmatory factor analysis. These factors and criteria can be a good tool for managers of manufacturing industries to assess and gap analysis of the agility of their organizations. Agility enablers, agility strategies and agility capabilities measure the current level of agility of the organization and agility drivers measure the level of agility required by the organization.

This paper aims to design a three-level sustainable dairy supply chain under uncertain conditions. Mixed-integer nonlinear programming proposed to optimize the total costs taking into account the environmental and social considerations of a multi-period and multi-product chain applied to a dairy company. The delivery time and the first-in-first-out (FIFO) policy of the products are of particular importance in the proposed model.

Design/methodology/approach: 

A dairy supply chain in Iran investigated as a real case study and the authors considered various numerical experiments, and evaluated the developed model. The primary sources of data used in this study were from the Pak Company. The suggested mathematical model coded in the GAMS 2017 software. Due to the uncertainty of economic parameters and demand, a robust-heuristic optimization approach used to solve the proposed model.

Results indicated that the values of the objective function were close together in the deterministic and robust conditions. This finding outlined the proper performance of the model under uncertainty.The outputs of the three solution methods (MINLP, MILP, and the heuristic method), indicated a higher heuristic approach performance regarding the two other approaches based on the value of the objective function and the time to solve the problem. Besides, based on the sensitivity analysis, the objective function displayed a direct relationship with the demand change, and as demand increased at different levels of uncertainty, costs increased either. Furthermore, the impact of product life cycle on cost target was different and in each scenario, increasing product lifespan led to reduced costs of the supply chain with different scenarios.

Research limitations/implications: 

The most significant research limitations were the lack of issues related to production planning such as integrated production strategies and overtime, consideration of equal volume and weight capacity for vehicles, and integration of three aspects of sustainability in a goal function. Studying issues related to production planning, including production under different strategies of integrated production and overtime; the use of diverse and heterogeneous vehicles and the selection of a suitable one for transporting raw materials and products; multi-objective mathematical modelling; and the development of meta-heuristic algorithms to solve the model are suggestions for future research.

Practical implications: 

According to the results, conducting robust models can lead managers to make integrated decisions in uncertain situations. They can decide about the optimal flow of materials in the production of flexible products in a dairy supply chain. The perishability of products (especially products with a short shelf life) adds the complexity of inventory. Therefore, managers can minimize inventory costs by considering the exit issuance system in the order of product entry. Besides, if the company attempts to move to the triple bottom line evaluation of economic, environmental, and social performance, it can use the proposed structure and research solution model.

Social implications: 

The results of the model presented to the meeting of the company's board of directors. Since the proposed model provides a framework for measuring social responsibility, highly recognized by managers, and all agreed on its applicability to the company. This model creates values that lead the company to move towards social responsibility, from which it was far in recent years. Due to the various lifespan of products and their importance in environmental pollution, production managers in the dairy supply chain should adopt appropriate decisions in the production system with highest durability and minimal environmental pollution, which will lead to economic growth in this industry.

Originality/value:

 Compared to previous studies, this study is one of the first quantitative studies wherein, a multi-level/multi-product supply chain considered in the dairy industry.In this paper, the social costs of sustainability such as job creation cost for individuals, layoffs, and lost working days simultaneously investigated concerning economic and environmental perspectives in the uncertain environment (demand, transportation costs, production costs, holding costs, and capacity of facilities considered as uncertain parameters). Since dairy products widely used in delivering food in the daily diet plan, due to the risky distribution of dairy products, the authors recognized the loss of quality and expiry date of products in the proposed model. Due to the high demand for dairy products, subjects such as time for delivery, and first-in-first-out (FIFO) policy of products were of particular importance in the proposed model.

Owing to the importance of sustainability in the supply chain of project-oriented companies, this study aimed to design a model for identification of factors that may influence supply chain sustainability in project production at Pars Garma Industrial and Constructional Company. To serve the purpose, the interpretive applied study commenced with extensive library research to scrutinize existing theories and findings from conceptual and applied research and identify enablers and key factors that may impact supply chain sustainability. An interview protocol and a questionnaire were further developed to collect the research data. These instruments were validated based on the views of 10 experts serving as managers and decision makers of Pars Garma Company who were selected using a judgment sampling procedure. The research population comprised 155 individuals out of whom a sample of 120 were selected according to the Cochran formula. Having collected and analyzed the data, the findings indicated 7 main dimensions of project characteristics, contractors’ characteristics, managers’ characteristics, communication features, characteristics of supervisory organizations, performance evaluation and characteristics of the society, each with constituent components. The implications of the findings will be discussed.

Agility is a prerequisite for improvement in the industry. Academics and practitioners have long acknowledged the importance of agile manufacturing in achieving sustainable competitiveness. In that regard, the general policies of the Resistance Economy as an upstream document can be a good guide for the agility of country's manufacturing industries under the current sanctions. Therefore, the purpose of this paper was to identify the agility elements and to explain agility strategies in the automobile industry. This research is an applied research in terms of purpose and a qualitative research in terms of method. Thematic analysis was done and document data was coded manually and using software, which led to the identification of 37 basic themes. Then, by using fuzzy Delphi method, a questionnaire was surveyed by selected experts of the automobile industry. The results confirm the strong association of the 15 themes with the industry under study. These basic themes were categorized into 3 organizer themes, using the cognitive mapping method, which was mapped into a thematic network. According to the research findings, 1- Agility of Human Resources through: Empowerment, upgrade of Training, increase of Skills, Experience and Flexibility enhancing, Jihad Work Culture, Standard Work Culture, and Strengthening Team Work, 2- Agility of Production and Operations through: enhancing Production Factors, Production and Product Flexibility, and Production and Product Quality, and 3- Supply Chain Agility Strategies through: Enterprise Resource Management, Competency Management, Variety in Supplying, broadening Co-operations and Partnerships, have been introduced as agility Strategies of the automotive industry.

One of Iran's problems in the production of common oil and gas fields is unequal extraction‎. ‎Therefore‎, ‎the production of common oil and gas fields in onshore and offshore is essential for Iran‎, ‎so this must carefully monitor‎, ‎which can be considered as a game-liked approach‎, ‎that each player tries to increase its payoff‎. ‎Therefore‎, ‎the purpose of this study is to apply game theory in examining Iran's approaches to extracting from common oil fields‎. ‎For this purpose‎, ‎the present study seeks to design a mathematical model to optimize the production of Iran against a competitor using a game‎. ‎Since the proposed model is in the field of mathematical modeling‎, ‎the research strategy is a case study‎. ‎Meanwhile‎, ‎the data-gathering tool is descriptive‎. ‎The results showed that Iran's equilibrium in Forouzan oil field is cooperation‎, ‎and the equilibrium of Saudi Arabia is non-cooperation‎. ‎Finally‎, ‎the executive policies based on research results presented‎.

  The industrial clusters are a group of enterprises and related organizations in a particular area, which are geographically close and have relevance with each other. Industrial clusters have an important role in economic development both at the national and regional levels. This paper aims to present a native model for sustainable cluster development.

 In this research, the "Grounded Theory" method has been used. The required data has been collected through a theoretical sampling method and a semi-structured interview with Cluster Development Agents (CDA(. Meanwhile, the number of research samples was 18.

In this research, a native model is presented for the sustainable development of industrial clusters. After collecting the research data, three coding steps were performed including open coding, axial coding and selective coding. The proposed model has 7 main dimensions and 25 sub-dimensions, and their relationships in the model are based on causal relationships.

The results of research show that sustainable development of industrial clusters should be carried out continuously and comprehensively and in the process of development, in addition to economic factors, environmental and social factors must be considered. The proposed model has a multidimensional and multidimensional approach to the development of industrial clusters and can be used by development agents in order to achieve sustainable development. In developing the present model, the business climate and the conditions for the development of industrial clusters in Iran have been considered.