Showing 4 results for Fuzzy Set Theory
F. Bagheri , M. J. Tarokh,
Volume 21, Issue 1 (6-2010)
Abstract
Assessment and selection of suppliers are two most important tasks in the purchasing part in supply chain management. Supplier selection can be considered to be a single or multi-objective problem. From another point of view, it can be a single or multi-sourcing problem. In this paper, an integrated AHP and Fuzzy TOPSIS model is proposed to solve the supplier selection problem. This model makes the decision-maker to be able to solve this problem with different criteria and different weight for each criterion with respect to the purchasing strategy. Finally, the proposed model is illustrated by an example.
Mohammad Najafi Nobar, Mostafa Setak,
Volume 21, Issue 1 (6-2010)
Abstract
In nowadays world competitive market, on account of the development of electronic media and its influence on shortening distances, companies require some core competencies in order to be able to compete with numerous competitors in industry and sustain their situation in such a market. In addition companies achieve this target are those which their processes perform great and exploit from competitive price, quality, guarantee, etc. Since some parameters such as price and quality are so dependent on the performance of company supply chain management, so the results can highly impress the final price and quality of products. One of the main processes of supply chain management is supplier selection process which its accurate implementation can dramatically increase company competitiveness. In presented article two layers of suppliers have been considered as a chain of suppliers. First layer suppliers are evaluated by two groups of criteria which the first one encompasses criteria belongs to first layer suppliers features and the second group contains criteria belong to the characteristics of second layer suppliers. One of the criteria is the performance of second layer suppliers against environmental issues. Then the proposed approach is solved by a method combined of concepts of fuzzy set theory (FST) and linear programming (LP) which has been nourished by real data extracted from an engineering design and supplying parts company. At the end results reveal the high importance of considering second layer suppliers features as a criteria for selecting the best supplier.
Ahmad Makui, Pooria Moeinzadeh, Morteza Bagherpour,
Volume 27, Issue 3 (9-2016)
Abstract
Due to the particular importance of projects in human life and in organizations, proper project management has been always regarded highly by researchers and practitioners. Recent advances in technology and fundamental changes in most scientific areas have affected projects and made their nature and environmental circumstances much more complex than in the past. Fortunately, in recent years, many scholars have recognized the importance of complexity in modern project management and tried to identify its various aspects. Furthermore, one of the main factors for a project’s success is the assignment of an appropriate project manager. Many studies have been done about project managers' competencies and the selection methods of a suitable project manager. In most of these researches, the amount and type of project complexity have been explained as influential factors for determining the competent project manager. However, a specific approach for project manager selection considering the complexity of projects is not provided yet. Hence, in this paper we try to design and implement a fuzzy group decision making approach to allocate the best project manager taking into account the project complexity. Also, owing to the importance of construction projects in the development of countries' basic infrastructures, we exclusively studied this kind of projects. Finally, it should be noted that from the viewpoint of complexity theory, system complexity can exist in two forms: static and dynamic. Therefore, considering the breadth of issues related to each of these two complexity areas, just the static complexity of construction projects has been studied here.
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.
