Showing 6 results for McDm
Mir. B. Aryanezhad, M.j. Tarokh, M.n. Mokhtarian, F. Zaheri,
Volume 22, Issue 1 (3-2011)
Abstract
Multiple criteria decision making (MCDM) problem is one of the famous different kinds of decision making problems. In more cases in real situations, determining the exact values for MCDM problems is difficult or impossible. So, the values of alternatives with respect to the criteria or / and the values of criteria weights, are considered as fuzzy values (fuzzy numbers). In such conditions, the conventional crisp approaches for solving MCDM problems tend to be less effective for dealing with the imprecise or vagueness nature of the linguistic assessments. In this situation, the fuzzy MCDM methods are applied for solving MCDM problems. In this paper, we propose a fuzzy TOPSIS (for Order Preference by Similarity to Ideal Solution) method based on left and right scores for fuzzy MCDM problems. To show the applicability of the proposed method, two numerical examples are presented. As a result, our proposed method is precise, easy use and practical for solving MCDM problem with fuzzy data. Moreover, the proposed method considers the decision makers (DMs) preference in the decision making process. It seems that the proposed fuzzy TOPSIS method is flexible and easy to use and has a low computational volume .
Mahdi Karbasian, Saeed Abedi,
Volume 23, Issue 1 (3-2012)
Abstract
One of the main principles of the passive defense is the principle of site selection. In this paper, we propose a multiple objective nonlinear programming model that considers the principle of the site selection in terms of two qualitative and quantitative aspects. The purpose of the proposed model is selection of the place of key production facilities of a system in which not only it observes the dispersion principle but also reduces the system transportation costs. Moreover, the proposed model tries to select the sites that can fulfill other elements of site selection as well as dispersion in a way that it increases the trustworthiness of the selected network. For solving the proposed model we used the Genetic Algorithm integrated with TOPSIS method.
Smiljka Miškić, Željko Stević, Ilija Tanackov,
Volume 32, Issue 4 (12-2021)
Abstract
In the field of logistics, there is a daily need for decision making, i.e. the need to solve business problems by selecting an appropriate solution. During the implementation of decision-making processes, it is necessary to find an optimal solution that will best meet the needs of companies. The selection of an optimal solution is crucial for the profitability, cost-effectiveness and long-term development of companies. The decision-making process in logistics is facilitated by applying various tools such as multi-criteria decision-making methods. In this paper, an integrated SWARA (Step-wise Weight Assessment Ratio Analysis) – MARCOS (Measurement Alternatives and Ranking according to Compromise Solution) model was developed and applied in order to classify products. Fifty alternatives, i.e. products were evaluated based on three criteria. The first criterion is the quantity of purchased products, the second criterion is the unit price of products and the third criterion is the annual value of purchase. The SWARA method was applied to determine the significance of the criteria, while the classification of products was performed using the MARCOS method. According to the results of the originally created MCDM model, the products were grouped into three categories A, B, and C. Then, a sensitivity analysis was performed using a model involving the integration of SWARA method and ABC analysis. Using this model, the classification of products into three groups was performed on the basis of the aforementioned criteria, and then a comparative analysis was conducted.
Mehdi Abdollahi Kamran, Samira Afsharfar, Fatma Al Mawali, Reza Babazadeh, Marya Al Balushi,
Volume 36, Issue 2 (6-2025)
Abstract
One of the most critical concerns in supply chain management (SCM) is supplier selection, which significantly impacts an organization's efficiency and market agility. Balancing ordinal and basic criteria in supplier selection has become increasingly crucial in recent years within SCM. This research presents three multi-criteria decision-making (MCDM) methods including Fuzzy analytic hierarchy process (AHP) and Fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methods to assess and select suppliers in oil and gas (O&G) industry. The critical criteria for supplier selection in the O&G sector have been reviewed in the literature and validated by experts actively working in the field. Initially, the Fuzzy AHP technique determines criterion weights and ranks suppliers. Subsequently, the Fuzzy TOPSIS approach is applied to rank prospective suppliers identified through objective evaluation. The findings show the capability of the utilized approaches in supplier selection procedure in O&G industry.
Muhammad Faisal Ibrahim, Imam Santoso, Siti Asmaul Mustaniroh, Retno Astuti,
Volume 37, Issue 1 (3-2026)
Abstract
This study systematically reviews the application of Multi-Criteria Decision-Making (MCDM) methods in risk management, aiming to map their use to the ISO 31000:2018 framework and consolidate fragmented literature into a structured synthesis. More than 3,000 studies were screened using a PRISMA-based methodology, and 104 were analyzed in depth to examine how MCDM methods support different stages of the risk management process. The findings reveal hybrid MCDM approaches significantly enhance decision-making effectiveness across multiple stages. The most frequently applied methods are the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), often combined for risk prioritization and mitigation strategy selection. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) also effectively analyzes interdependencies between risk factors and mitigation strategies. Nonetheless, challenges such as expert judgment subjectivity and the complexity of integrating multiple techniques remain critical issues. Building on these insights, the study proposes a six-stage conceptual framework that integrates MCDM techniques across risk identification, analysis, evaluation, and treatment. The key contribution lies in providing a unified, adaptive, and data-driven framework that enhances comparative understanding and strengthens structured risk management practices across industries.
Assia Bilad, Mounia Zaim, Faical Zaim,
Volume 37, Issue 2 (6-2026)
Abstract
The increasing adoption of artificial intelligence (AI) tools in manufacturing supply chains has intensified competition and highlighted the need for effective approaches to improve production quality. However, selecting the most appropriate AI tools remains challenging due to multiple evaluation criteria and uncertainty in expert judgments. This study proposes a hybrid fuzzy multi-criteria decision-making framework combining Fuzzy Delphi, Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to assess the impact of AI tools on production quality. The Fuzzy Delphi method is used to achieve expert consensus on relevant quality criteria, FAHP determines their relative importance, and Fuzzy TOPSIS ranks AI tools according to their performance. The results reveal that quality control and process performance criteria are the most influential in evaluating production quality. Predictive maintenance is identified as the most effective AI tool for enhancing production quality, followed by computer vision and machine learning applications. A case study conducted on Moroccan manufacturing firms further confirms the positive role of AI adoption in improving production quality across the supply chain. This research provides a practical decision-support framework for managers and contributes to the literature by offering a structured and robust approach for evaluating AI tools under uncertainty.
