Reza Kiani Mavi

An extended VIKOR method using stochastic data and subjective judgments

Decision makers face several uncertainty levels through the decision making process.We extend the VIKOR method to solve multi-criteria decision making problems with stochastic data.We use the fuzzy analytic hierarchy process to define the weights of the decision criteria.Our method is used to rank several bank branches based on their stochastic efficiency.We compare our ranking with a stochastic super-efficiency data envelopment analysis model. Decision makers (DMs) face different levels of uncertainty throughout the decision making process. In particular, natural language is generally subjective or ambiguous when used to express perceptions and judgments. The aim of this paper is to extend the VIKOR method and develop a methodology for solving multi-criteria decision making (MCDM) problems with stochastic data. The weights of the stochastic decision criteria considered in our extended VIKOR model have been determined using the fuzzy analytic hierarchy process (AHP) method. We present a case study in the banking industry to demonstrate the applicability of the proposed method. We also compare our results with the results obtained from a stochastic version of the super-efficiency data envelopment analysis (DEA) model to exhibit the efficacy of the procedures and algorithms.

Indicators of Entrepreneurial University: Fuzzy AHP and Fuzzy TOPSIS Approach

The phenomenon of entrepreneurial universities has received considerable attention over the last decades. An entrepreneurial orientation by academia can lead to innovative and sustainable products and research mobilization which help in putting regions and nations in an advantageous position in emerging knowledge-intensive fields of economic activity. Many empirical researches focused on entrepreneurial orientation of university students, but study on antecedents and criteria of the entrepreneurial universities is scarce. The main contribution of this paper is providing a comprehensive criteria set for evaluation of entrepreneurial universities. In this context, environmental and internal factors of entrepreneurial universities were identified based on literature review and survey. Twelve academics and managers have participated in this study by weighing the criteria and scoring alternatives. Society, in particular, higher education institutions, and universities can use the results for creation of new businesses and spin-offs. Data was analyzed by fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order preferences by similarity to ideal solution (FTOPSIS).

Crude oil supply chain risk management with DEMATEL-ANP

Abstract Crude oil supply chain is extremely complex and vulnerable to various risks. Clear understanding of these risks can help supply chain managers to make effective decisions. This paper identifies main risks related to crude oil supply chain and implements decision making trial and evaluation laboratory (DEMATEL) method to determine the interdependency between risks and then applies analytic network process (ANP) method to evaluate the importance of each risk and to determine the best response strategy. The results show that the most important risk area is the regulatory and environmental risks and that the transference and cooperation policy is rated as the best response strategy. Contributions of this paper are fourfold: (1) it provides a comprehensive framework of risks that need to be considered in crude oil supply chain risk management (SCRM) context; (2) it shows how DEMATEL can be applied to analyze the interdependency between risks; (3) it illustrates how various risks and risk management strategies can been assessed through the ANP approach to aid managers in their decision making processes; and (4) most important contribution is customization of crude oil SCRM in Iran.

Using fuzzy AHP for evaluating the dimensions of data quality

The business work flow depends on the data quality which is applied in the organisation. Therefore, data quality has become increasingly important to many organisations such as semiconductor industry. Data quality depends strongly on organisation of the information system (IS) and how the data is processed. Measuring and improving data quality in an organisation is a complex task. The purpose of this paper is to provide a good insight into the use of fuzzy analytical hierarchy process (fuzzy AHP) to incorporate four aspect dimensions of data quality: 1) intrinsic; 2) accessibility; 3) contextual; 4) representational. Findings demonstrate that the intrinsic criteria and accessibility criteria are the preferred key decision dimensions of data quality in semiconductor industry. Fuzzy AHP as a decision-making analysis tool is used for handling uncertain and imprecise data.

A forecasting method in data envelopment analysis with group decision making

Service companies continually seek improved methods to measure the performance of their organisations because they are committed to improve efficiency and effectiveness in their operating units. Managers generally regard conventional methods inadequate. They struggle with the existing methodology as they are trying to implement the results obtained from such processes as performance ratios, regression analysis results and the like. DEA has proven itself to be both theoretically sound framework for performance measurement and an acceptable method by those being measured. In this paper, by combining group analytic hierarchy process (GAHP) into data envelopment analysis (DEA), a new approach for forecasting is developed. The efficiency of this approach is tested with application in bank branches. According to the results, this approach has more advantages over conventional methodologies.

Impact of supply chain management practices on innovation and organizational performance in Iranian Companies

Supply chain management (SCM) is effective way to improve innovation and organizational performance. This research conceptualizes and develops five dimensions of SCM practice (strategic supplier partnership, customer relationship, information technology, information sharing, and Supply chain integration) and tests the relationships between SCM practices, innovation and organizational performance. A survey is conducted on 186 Iranian managers. Data are analyzed using principal components analysis and relationships are tested using linear regression. Results show the importance of supply chain management practices adoption. It reveals their positive impacts on innovation and organizational performance.

Developing Common Set of Weights with Considering Nondiscretionary Inputs and Using Ideal Point Method

Data envelopment analysis (DEA) is used to evaluate the performance of decision making units (DMUs) with multiple inputs and outputs in a homogeneous group. In this way, the acquired relative efficiency score for each decision making unit lies between zero and one where a number of them may have an equal efficiency score of one. DEA successfully divides them into two categories of efficient DMUs and inefficient DMUs. A ranking for inefficient DMUs is given but DEA does not provide further information about the efficient DMUs. One of the popular methods for evaluating and ranking DMUs is the common set of weights (CSW) method. We generate a CSW model with considering nondiscretionary inputs that are beyond the control of DMUs and using ideal point method. The main idea of this approach is to minimize the distance between the evaluated decision making unit and the ideal decision making unit (ideal point). Using an empirical example we put our proposed model to test by applying it to the data of some 20 bank branches and rank their efficient units.

Modeling corporate entrepreneurship success with ANFIS

Albeit the application of Adaptive Neuro-Fuzzy Inference System (ANFIS) in many real world problems, it is not used in the entrepreneurship studies. The aim of this paper is to develop an ANFIS model to forecasting corporate entrepreneurship (CE) success at industrial organizations. A conceptual model consisting critical success factors (motivations) and critical failure factors (barriers) of CE success is proposed. 11 experts (academic and industrial) validated the CE success and failure factors with the help of ANFIS rules. 464 MBA graduates who are working at industrial organizations have participated in this research. For the sake of ANFIS, data was divided into two groups (training and checking). Findings reveal that ANFIS testing error with training data is 0.057103 and ANFIS testing error with checking data is 0.03342. So that, the developed fuzzy inference system has the best applicability and predictability for CE success.

Talent pool membership in sport organisations with fuzzy analytic hierarchy process

The ability to attract and retain talent is rapidly becoming one of the key issues for human resource managers and their organisations across the globe. For organisations across the globe, talent management of knowledge workers and managers is of strategic importance. A growing number of organisations are recognising that they must find ways to recruit and retain new talent who will stick around long enough to make their tenure worth underwriting. Sport organisations are not exception from the case. This paper presents a fuzzy analytic hierarchy process (FAHP) for scouting and evaluation of young sport talents. Fuzzy logic is implemented in order to make the results more acceptable. Based on the knowledge of several sport experts, various attributes for membership in talent pool are ranked. Four main criteria in this paper are knowledge and experience, drive and energy, pride and responsibility and ability to deliver results. Findings show that knowledge and experience is the most important element for talent pool membership. Finally, a linear function developed that can illustrate the suitability of candidates for talent pool membership.

Ranking football teams with AHP and TOPSIS methods

Managers continually seek improved methods to measure the performance of their organisations because they are committed to improve efficiency and effectiveness in their operating units. The sporting performance of professional football teams has often been assessed considering their results in the major regular competition, namely the national league. On the other hand, ranking teams is a multi-criteria decision-making (MCDM) problem. Therefore by taking data for the season 1999/2000 from Haas et al. (2004) we study the efficiency of football teams in the German Bundesliga by MCDM techniques. Based on the analytic hierarchy process (AHP) and the technique for order preferences by similarity to ideal solution (TOPSIS), this paper applies an MCDM approach to evaluate the performance of football teams in the German Bundesliga. The non-parametric Spearman test of relationship (rs) and the Kendalls Tau test (τ) of correlation verify the results of DEA and TOPSIS.