Yazar "Zandieh, Mostafa" seçeneğine göre listele

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    Development of dynamic balanced scorecard using case-based reasoning method and adaptive neuro-fuzzy inference system
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022) Khanmohammadi, Ehsan; Safari, Hossein; Zandieh, Mostafa; Malmir, Behnam; Tirkolaee, Erfan Babaee
    In recent years, selecting the strategies has been recognized as one of the most challenging issues facing senior and strategy managers of companies. In this regard, this article introduced an integrated framework that helps strategy managers to determine the organization's strategy by analyzing the long-term objectives and visions. The proposed methodology is based on developing and enhancing the performance of the balanced scorecard (BSC) by covering its limitations through being combined with system dynamics (SD) simulation, case-based reasoning (CBR) method, and adaptive neuro-fuzzy inference system (ANFIS) model. The SD model is built based on the company's strategy map to predict the future status of the company based on its selected strategies. The CBR method and ANFIS model are also to develop the sensitivity analysis and policy-making stage utilizing learning and human memory application. An Iranian food and beverage company is considered as a real-world case study to demonstrate how the proposed method could work and validate the research methodology's applicability. As the main finding, the model yields appropriate strategies with minor errors to reach the targets defined by managers. According to the proposed dynamic BSC, managers can select their strategies and observe their financial variables' outcomes on the planning horizon. Moreover, they can set future targets for their financial variable to receive strategies for their achievement.
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    Optimizing the dam site selection problem considering sustainability indicators and uncertainty: An integrated decision-making approach
    (Elsevier Sci Ltd, 2023) Ekhtiari, Mostafa; Zandieh, Mostafa; Tirkolaee, Erfan Babaee
    Changes of climate, population growth, extended droughts, water scarcity, and sustainability considerations are amongst the concerns that stress more than before the significance of water resources management (WRM). One of the approaches that may assist decision-makers (DMs) in handling water resources is the proper site selection for dam construction. The dam site selection decision is a huge long-term investment and due to multiple decision-making factors, stakeholders or DMs, non-deterministic information in the decision-making process, and ripple effect on many other elements of the economy, meticulous analysis is needed. In this study, dam site selection problem (DSSP) is modeled in the form of binary programming under certainty, uncertainty, and hybrid circumstances. Hence, nadir compromise programming (NCP) model is executed in order to tackle the problem with crisp data. Further, a novel model based on NCP and stochastic programming is suggested to treat the uncertainty. The integration of the proposed models is able to address the problem with crisp and random data. The evaluation criteria are categorized based on sustainability (social, economic, and environmental) and technical indicators for dam site selection. To obtain the criteria weight, an interval group decision-making trial and evaluation laboratory (IGDEMATEL) approach is also applied where experts are able to state their opinions on linguistic terms and interval numbers. A real case study in Iran is then investigated in order to appraise the applicability of the developed methodology. A simulation method is further proposed to assess the obtained results and validate the suggested model's performance. Finally, the IGDEMATEL results are employed to survey the relationships between cause-and-effect criteria. The results obtained from the proposed and simulation models are similar in 83% of the cases, and the remaining difference of 17% is because of the uncertainty governing the problem, as well as the lack of sufficient data for precise decision-making.