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Sustainable supply chain management (SSCM) is a tactical concern for managers seeking a longterm result. Different managers are attempting to apply SSCM in order to get a competitive advantage. One of the critical processes is the identification of strategy implementation hurdles, which has been a research focus. As a result, this paper identifies these obstacles and analyzes their interrelationships. A list of obstacles was initially established by a survey of the literature and expert judgment in the form of the fuzzy Delphi approach. Next, an investigation was designed to collect expert opinions on the interrelationships between these obstacles. The fuzzy DEMATEL (FDEMATEL) approach was employed for examining the causal linkages and interdependencies of these obstacles. Subsequently, the interpretative structural modeling (ISM) technique was utilized to create a classified structure and to identify the driving and dependent connections. A fuzzy MICMAC analysis was applied for categorizing the obstacles based on driving and dependent power. The results reveal that barriers, "cost of implementation sustainability (B1)", "lack of financial resources (B2)", "institutional complexity (B3)", "complexity in measuring and monitoring sustainability practices (B4)", "lack of effective regulations (B5)", "lack of a proper evaluation system for suppliers (B6)", "lack of sustainability knowledge (B7)", and "strategic and structural restraints (B8)", are the influence-forwarding obstacles. These obstacles influence "inadequate government support (B9)", "high investments for sustainability and less return-oninvestments (B10)", "old equipment and machinery (B11)", and "lack of management commitment (B12)", which are the greatest influences on SSCM practices. Identifying the important obstacles and their interdependences can help policymakers in the manufacturing sector minimize or overcome them, boosting the possibilities of effectively incorporating sustainable principles into these projects. [ABSTRACT FROM AUTHOR]

Shopping center plays an important role in distribution system and marketing. These canters provide an appropriate atmosphere for customers; so that, the customers achieve the best service within a short time. However, there is an intense competition among shopping centers to attract more customers for increasing the profit. Therefore, a powerful model assists authorities in identifying the critical competitive aspects and directing their efforts toward performance improvement. However, a number of strategies have been developed to identify the most relevant components. The Delphi technique under intuitionistic fuzzy environment, called intuitionistic fuzzy Delphi method (IFDM) study, is a group-based technique that can simply formulate the uncertainty imposed by decision making circle. On the other hand, multi criteria decision making (MCDM) method such as analytical network process (ANP) is a mathematical tool for taking into account mutual relationships in order to rank a number of criteria. Nonetheless, the ANP is unable to account for the uncertainty involved in the decision-making process. Similarly, the intuitionistic fuzzy set (IFS) can express ambiguity and vagueness by utilizing the given scale. Because the IFS is robust in dealing with complexity and ambiguity, the IFS-GANP (an integrated model of the IFS and ANP methods under group decision) can result in a more specific description of the situation. As a result, the IFS-GANP approach outperforms both conventional ANP and fuzzy ANP. To demonstrate the model's feasibility, a case study rating the essential aspects impacting the attractiveness of retail centers is shown. The result demonstrates factor C31 (Location) with value of 0.202 plays the greatest role in attracting customers. [ABSTRACT FROM AUTHOR]

In the post-recession period, the business environment in developing countries is still relatively undesirable. Based on the reports published, the number of closed entrepreneurs are significantly more than the number of those newly established. These statistics also reflect that the development of entrepreneurship faces several challenges. However, a strategic management program must be accurately conducted to effectively control entrepreneurial challenges based on the limitation of time and financial resources. On the other hand, the program should provide an appropriate opportunity for competitiveness by producing better quality goods and services successfully marketed to consumers. The aim of this paper is to propose a robust model with a high potential for evaluation of entrepreneurial challenges in order to identify and control the most critical ones. To achieve the aim, the most critical parameters should be identified to prevent wasting the resources. Likewise, the analytical network process (ANP) method, a branch of multi-criteria decision making (MCDM) techniques, is a powerful tool that can consider all interrelationships and interdependencies between criteria. On the other hand, neutrosophic sets are among the most widespread approaches in coping with vagueness and unknown environment. The methodology employed in this paper uses a combination model based on ANP and neutrosophic sets using sustainable development indicators to prioritize the challenges of entrepreneurship. The results show that the proposed model has a high potential to provide a process for decision-making under a vague environment. [ABSTRACT FROM AUTHOR]

The complexity and dynamics of the executive projects have coped contractors with substantial hazards and losses. Project risk management is a critical tool for authority to improve its performance and secure the success of the organization. However, a number of standards and approaches have been developed to formulate the projects based on their risks. The Elena guideline is a systematic standard developed by Iran Project Management Association. This guideline provides the full cycle of the risk management process. Risk evaluation is the key part of the risk management process. On the other hand, different techniques have been developed to model a risk evaluation problem. Fuzzy inference system is one of the most popular techniques that is capable of handling all types of the uncertainty involved in projects. This paper proposes a three-stage approach based on the fuzzy inference system under the environment of the Elena guideline to cope with the risky projects. Finally, an illustrative example of the risk evaluation is presented to demonstrate the potential application of the proposed model. The results show that the proposed model evaluates the risky projects efficiently and effectively. [ABSTRACT FROM AUTHOR]

Cost estimation is an essential issue in feasibility studies in civil engineering. Many different methods can be applied to modelling costs. These methods can be divided into several main groups: (1) artificial intelligence, (2) statistical methods, and (3) analytical methods. In this paper, the multivariate regression (MVR) method, which is one of the most popular linear models, and the artificial neural network (ANN) method, which is widely applied to solving different prediction problems with a high degree of accuracy, have been combined to provide a cost estimate model for a shovel machine. This hybrid methodology is proposed, taking the advantages of MVR and ANN models in linear and nonlinear modelling, respectively. In the proposed model, the unique advantages of the MVR model in linear modelling are used first to recognize the existing linear structure in data, and, then, the ANN for determining nonlinear patterns in preprocessed data is applied. The results with three indices indicate that the proposed model is efficient and capable of increasing the prediction accuracy. [ABSTRACT FROM AUTHOR]

Nanotechnology plays a significant role in construction industry. The construction industry has been employed nanomaterials to improve the performance of construction components and the safety of the structure and to reduce the energy consuming and the cost of maintenance. In other words, nanotechnology has a substantial impact on the construction industry. Therefore, it is necessary to identify and evaluate the critical factors of the application of nanotechnology in construction in order to concentrate on the most critical factors. However, several techniques have been developed to prioritize the evaluation criteria. Analytical network process (ANP) technique, a branch of multi criteria decision making (MCDM) methods, is a powerful tool to rank a limited number of criteria. This technique takes into account both tangible and intangible criteria in the process of formulation of a decision making problem. This method is capable of handling all types of independence and dependence relationships. On the other hand, intuitionistic fuzzy set (IFS) is a well-known technique in considering the inherent uncertainty involved in the process of modelling a decision making problem. In this paper, a new model based on the IFS and ANP technique is proposed to evaluate the critical factors of the application of nanotechnology in the construction industry. The results demonstrate that the proposed model has a high potential for taking into account the uncertainty in the form of a three dimension function, including membership, non-membership, and non-determinacy. [ABSTRACT FROM AUTHOR]

Evaluation of business processes plays a significant role in business development and improvement. Therefore, organizations need a systematic approach to evaluate all the changes through robust and powerful techniques that can formulate the relationship between the available information and the degree of the inherent uncertainty. In this paper, a set of operational variables are defined. Then, the SPSS software package is utilized to validate the gathered data. After that, the variables are categorized by the use of a clustering technique. Finally, five major factors are determined as the most effective components. According to the inherent uncertainty involved in the process of modelling, fuzzy set theory, a powerful mathematical tool is applied to handle the vagueness. In order to construct a knowledge base based on the fuzzy set theory, the linguistic concepts for each variable are defined. Lastly, membership functions are described and a set of fuzzy rules based on input-output parameters are written in MATLAB software environment. To demonstrate the potential application of the proposed approach, a real case study is illustrated. The results reflect the capability and effectiveness of the approach proposed in this paper. [ABSTRACT FROM AUTHOR]

The problem of handling equipment selection plays a significant role in the total cost of a mining project; so that it can affect the activity and continuity of the project and is a strategic problem. In this study, an integrated model based on two fuzzy multi-criteria decision making techniques for handling equipment selection is proposed. The proposed evaluation model is derived from group decision making, fuzzy set theory, analytical hierarchy process (AHP), and Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The fuzzy AHP (FAHP) method is utilized to calculate the relative importance of the evaluation criteria, then, fuzzy TOPSIS (FTOPSIS) is applied for evaluating the feasible handling equipment in order to select the best handling system among a pool of the possible alternatives. The model is applied for a real world case study to demonstrate the capability and effectiveness of the proposed model. To investigate the result sensitiveness to the changes of the criteria weights, a sensitivity analysis is finally conducted. [ABSTRACT FROM AUTHOR]

Brand extension is one of the most popular strategies in marketing. This is due to the fact that consumers usually take into account the brand first because it indirectly reflects the design, quality, and functions. According to the significant role of brand in creating benefit and diminishing risk for a new product, this study intended to develop a novel model for selecting the most appropriate strategy in brand extension. However, there are a number of criteria influencing the possible strategies, and they often are interdependent. Therefore, a multi-criteria decision making model based on Analytic Network Process and Additive Ratio Assessment methods has been developed to systematically clarify the interdependent relationships among the evaluation criteria of brand extension and then, scientifically evaluate the feasible strategies and rank the priorities of brand extension strategies. The results of the proposed model show that “Production cost” is the most important factor, followed by “Quality of parent brand” and “Perceived risk” while “Ice cream” is the highest satisfaction of brand extension. [ABSTRACT FROM PUBLISHER]

Tunnels are artificial underground spaces that provide a capacity for particular goals such as storage, under-ground transportation, mine development, power and water treatment plants, civil defence. This shows that the tunnel construction is a key activity in developing infrastructure projects. In many situations, tunnelling projects find themselves involved in the situations where unexpected conditions threaten the continuity of the project. Such situations can arise from the prior knowledge limited by the underground unknown conditions. Therefore, a risk analysis that can take into account the uncertainties associated with the underground projects is needed to assess the existing risks and prioritize them for further protective measures and decisions in order to reduce, mitigate and/or even eliminate the risks involved in the project. For this reason, this paper proposes a risk assessment model based on the concepts of fuzzy set theory to evaluate risk events during the tunnel construction operations. To show the effectiveness of the proposed model, the results of the model are compared with those of the conventional risk assessment. The results demonstrate that the fuzzy inference system has a great potential to accurately model such problems. [ABSTRACT FROM AUTHOR]

Renewable energies are well-known as one of the most important energy resources not only due to limited other energy resources, but also due to environmental problems associated with air pollutants and greenhouse gas emissions. Renewable energy project selection is a multi actors and sophisticated problem because it is a need to incorporate social, economic, technological, and environmental considerations. Multi criteria decision making (MCDM) methods are powerful tools to evaluate and rank the alternatives among a pool of alternatives and select the best one. COPRAS (COmplex PRoportional ASsessment) is an MCDM technique which determines the best alternative by calculating the ratio to the ideal solution and the negative ideal solution. On the other hand, analytical hierarchy process (AHP) is widely used in order to calculate the importance weights of evaluation criteria. In this paper an integrated COPRAS-AHP methodology is proposed to select the best renewable energy project. In order to validate the output of the proposed model, the model is compared with five MCDM tools. The results of this paper demonstrate the capability and effectiveness of the proposed model in selecting the most appropriate renewable energy option among the existing alternatives. [ABSTRACT FROM PUBLISHER]

Developing a precise and accurate model of gold price is critical to assets management because of its unique features. In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been used for modeling the gold price, and compared with the traditional statistical model of ARIMA (autoregressive integrated moving average). The three performance measures, the coefficient of determination (R 2), root mean squared error (RMSE), mean absolute error (MAE), are utilized to evaluate the performances of different models developed. The results show that the ANFIS model outperforms other models (i.e. ANN and ARIMA model), in terms of different performance criteria during the training and validation phases. Sensitivity analysis showed that the gold price changes are highly dependent upon the values of silver price and oil price. [ABSTRACT FROM PUBLISHER]

Selection of the working strategy is a critical problem and it plays a significant role in the success of organization development. On the other hand, selecting the most appropriate working strategy among a pool of alternatives is a multi-criteria decision making (MCDM) problem. Since every working strategy has its benefits and costs and may bring a company different opportunities and risks, which kind of working strategy is the most appropriate for a company to accomplish is a sophisticated and complex decision with a high degree of uncertainty. Therefore, the current paper proposed an integrated evaluation model based on the analytic network process (ANP) and the complex proportional assessment (COPRAS), to help the decision makers or managers with the selection of proper working strategy in a fuzzy environment where the fuzziness and uncertainties are handled with linguistic terms parameterized by triangular fuzzy numbers (TFNs). In this paper fuzzy ANP (FANP) is utilized to take into account interdependence and dependencies and determine the importance weights of benefit, opportunities, cost and risk (BOCR) factors, and fuzzy COPRAS is applied to rank the alternatives. To show the potential application of the proposed model, a real world application is conducted to illustrate the use of the proposed model for the working strategy selection problem. The results show the capability and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

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