Showing 77 results for Fuzzy
Huki Chandra, Ilma Mufidah, Moch. Wibisono, Dhimas Nur, Raya Fahreza,
Volume 0, Issue 0 (10-2025)
Abstract
Constructions have considerable revenue and stakeholder accountability implications. It is the aim of this research to identify and assess risks so as to apply the appropriate controls for an Indonesian construction project. Observation, interviewing, and staff meetings were employed in this research. Risk analysis was conducted by a safety practitioner and validated with 12 construction workers. Methods like Fuzzy Logic (FMEA and AHP), PLS-SEM, Kruskal-Wallis, and cluster analysis were used to provide precise scoring and classification. Fuzzy Logic accommodated the uncertainty of risks, and Fuzzy-AHP ranked the risks with criteria of injury, asset loss, reputation, and environment. It defines two medium-risk and four high-risk activities, with the highest risk being Activity 2 (wall painting) due to being at height. Job Safety Analysis provides particular mitigation in detail. The RPN score for wall painting was 526, with a Mean Squared Error of 507 and an overall coefficient of variation of 6%, showing high consensus. This integrated methodology reduces bias, maintains uncertainty, and provides tailored safety recommendations, a new approach not implemented in past research.
M.b Aryanezhad , A. Roghanian ,
Volume 19, Issue 1 (3-2008)
Abstract
Abstract: Bi-level programming, a tool for modeling decentralized decisions, consists of the objective(s) of the leader at its first level and that is of the follower at the second level. Three level programming results when second level is itself a bi-level programming. By extending this idea it is possible to define multi-level programs with any number of levels. Supply chain planning problems are concerned with synchronizing and optimizing multiple activities involved in the enterprise, from the start of the process, such as procurement of the raw materials, through a series of process operations, to the end, such as distribution of the final product to customers. Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another level to a corresponding plant-wide planning/network problem. Such a multi-level decision network structure can be mathematically represented by using “multi-level programming” principles. This paper studies a “bi-level linear multi-objective decision making” model in with “interval” parameters and presents a solution method for solving it this method uses the concepts of tolerance membership function and multi-objective multi-level optimization when all parameters are imprecise and interval .
M.b. Aryanezhad , E. M.b.aryanezhad & E.roghanian ,
Volume 19, Issue 1 (3-2008)
Abstract
A. Amid, S.h. Ghodsypour,
Volume 19, Issue 4 (12-2008)
Abstract
Supplier selection is one of the most important activities of purchasing departments. This importance is increased even more by new strategies in a supply chain, because of the key role suppliers perform in terms of quality, costs and services which affect the outcome in the buyer’s company. Supplier selection is a multiple criteria decision making problem in which the objectives are not equally important. In practice, vagueness and imprecision of the goals, constraints and parameters in this problem make the decision making complicated. Simultaneously, in this model, vagueness of input data and varying importance of criteria are considered. In real cases, where Decision- Makers (DMs) face up to uncertain data and situations, the proposed model can help DMs to find out the appropriate ordering from each supplier, and allows purchasing manager(s) to manage supply chain performance on cost, quality, on time delivery, etc. An additive weighted model is presented for fuzzy multi objective supplier selection problem with fuzzy weights. The model is explained by an illustrative example.
, , ,
Volume 20, Issue 1 (5-2009)
Abstract
Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrated via real data of a purchase system in order to simulate the system’s behavior.
Masoud Narenji, Ahmad Makui, Mehdi Fathi ,
Volume 20, Issue 4 (4-2010)
Abstract
Nowadays, interval comparison matrices (ICM) take an important role in decision making under uncertainty. So it seems that a brief review on solution methods used in ICM should be useful. In this paper, the common methods are divided into four categories that are Goal Programming Method (GPM), Linear Programming Method (LPM), Non-Linear Programming Method (NLPM) and Statistic Analysis (SA). GPM itself is divided also into three categories. This paper is a review paper and is written to introduce the mathematical methods and the most important applications of ICM in decision making techniques.
Hosein Saghaei, Hosein Didehkhani ,
Volume 20, Issue 4 (4-2010)
Abstract
This research aims at presenting a fuzzy model to evaluate and select Six-Sigma projects. For this purpose, a model of fuzzy analytic network process (ANP) was designed to consider the relation and mutual impact among the factors. In order to evaluate the projects, nine sub-criteria were considered which were classified into three categories of business, finance and procedural ones. Also to consider the ambiguity related to the pairwise comparisons being used in the research, the fuzzy logic was employed. The fuzzy algorithm being used is in the method of Mikhailov which has various advantages such as the presentation of consistency index and weight vector in a crisp form. At the end, in order to show the applicability, the proposed methodology was applied in an automobile part manufacturing firm.
J. Jassbi, S.m. Seyedhosseini , N. Pilevari,
Volume 20, Issue 4 (4-2010)
Abstract
Nowadays, in turbulent and violate global markets, agility has been considered as a fundamental characteristic of a supply chain needed for survival. To achieve the competitive edge, companies must align with suppliers and customers to streamline operations, as well as agility beyond individual companies. Consequently Agile Supply Chain (ASC) is considered as a dominant competitive advantage. However, so far a little effort has been made for designing, operating and evaluating agile supply chain in recent years. Therefore, in this study a new approach has been developed based on Adaptive Neuro Fuzzy Inference System (ANFIS) for evaluating agility in supply chain considering agility capabilities such as Flexibility, Competency, Cost, Responsiveness and Quickness. This evaluation helps managers to perform gap analysis between existent agility level and the desired one and also provides more informative and reliable information for decision making. Finally the proposed model has been applied to a leading car manufacturing company in Iran to prove the applicability of the model.
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.
A. Doostparast Torshizi, S.r. Hejazi,
Volume 21, Issue 2 (5-2010)
Abstract
In highly competitive industrial market, the concept of failure analysis is an unavoidable fact in complex industrial systems. Reliability of such systems not only depends on the reliability of each element of these systems, but also depends on occurrence of sequence of failures. In this paper, a novel approach to sequential failure analysis is proposed which is based upon fuzzy logic and the concept of Petri nets which is utilized to track all the risky behaviors of the system and to determine the potential failure sequences and then prioritizing them in order to perform corrective actions. The process of prioritizing failure sequences in this paper is done by a novel similarity measure between generalized fuzzy numbers. The proposed methodology is demonstrated with an example of two automated machine tools and two input/output buffer stocks.
E. Teimoury, H. Ansari , M. Fathi ,
Volume 22, Issue 1 (3-2011)
Abstract
The importance of reliable supply is increasing with supply chain network extension and just-in-time (JIT) production. Just in time implications motivate manufacturers towards single sourcing, which often involves problems with unreliable suppliers. If a single and reliable vendor is not available, manufacturer can split the order among the vendors in order to simultaneously decrease the supply chain uncertainty and increase supply reliability. In this paper we discuss with the aim of minimizing the shortage cost how we can split orders among suppliers with different lead times. The (s,S) policy is the basis of our inventory control system and for analyzing the system performance we use the fuzzy queuing methodology. After applying the model for the case study (SAPCO), the result of the developed model will be compared in the single and multiple cases and finally we will find that order splitting in optimized condition will conclude in the least supply risk and minimized shortage cost in comparison to other cases .
Asadallah Najafi , Abbas Afrazeh,
Volume 22, Issue 1 (3-2011)
Abstract
Improving knowledge worker productivity has been one of the most important tasks of the century. However, we have few measures or management interventions to make such improvement possible, and it is difficult to identify patterns that should be followed by knowledge workers because systems and processes in an organization are often regarded as a death blow to creativity. In this paper, we seek to present a method for prediction of Knowledge worker productivity (KWP) that it must be capable of predicting the productivity of the knowledge workers in a one year period of time based on the Fuzzy cognitive maps (FCM) technique Based on Real Coded Genetic Algorithm (RCGA) , as well as presenting the best option from among different options as the knowledge workers’ productivity improving strategy (suggesting solution), based on the results gained from this and the previous section and depending on the requirements. The validity of the suggested model will be tested in an Iranian Company .
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 .
Reza Morovatdar , Abdolah Aghaie , Simak Haji Yakhchali ,
Volume 22, Issue 1 (3-2011)
Abstract
In order to have better insight of project characteristics, different kinds of fuzzy analysis for project networks have been recently proposed, most of which consider activities duration as the main and only source of imprecision and vagueness, but as it is usually experienced in real projects, the structure of the network is also subject to changes. In this paper we consider three types of imprecision namely activity duration, activity existence and precedence relation existence which make our general fuzzy project network. Subsequently, a corrected forward recursion is proposed for analysis of this network. Since the convexity and normalization of traditional fuzzy numbers are not satisfied, some corrected algebraic operations are also presented. Employing the proposed method for a real project reveals that our method results in more applicable and realistic times for activities and project makespan in comparison to
Classic fuzzy PERT.
Iman Nosoohi , Seyed Nader Shetab-Boushehri,
Volume 22, Issue 2 (6-2011)
Abstract
Selection of appropriate infrastructure transportation projects such as highways, plays an important role in promotion of transportation systems. Usually in evaluation of transportation projects, because of lack of information or due to long time and high expenditures needed for gathering information, different effective factors are ignored. Thus, in this research, regarding multi criteria nature of transportation projects selection and using fuzzy logic, an appropriate conceptual framework for ranking and selecting transportation projects is proposed. Also, unlike the previous researches, we've applied a fuzzy inference system (FIS) to account value of each project with respect to each criterion, in the proposed methodology. The FIS helps us to set rule-based systems for paying attention to expert's experience and professional knowledge in decision making. The proposed methodology is explained in detail through an applicable example. We've considered most common criteria including effect of transportation project on traffic flow, economical growth and environment beside budget constraint, in the descriptive example.
Saeed Ramezani , Azizollah Memariani,
Volume 22, Issue 2 (6-2011)
Abstract
Maintenance , as a support function, plays an important role in manufacturing companies and operational organizations. In this paper, fuzzy rules used to interpret linguistic variables for determination of priorities. Using this approach, such verbal expressions, which cannot be explicitly analyzed or statistically expressed, are herein quantified and used in decision making. In this research, it is intended to justify the importance of historic data in oil analysis for fault detection. Initial rules derived by decision trees and visualization then these fault diagnosis rules corrected by experts. With the access to decent information sources, the wear behaviors of diesel engines are studied. Also, the relation between the final status of engine and selected features in oil analysis is analyzed. The dissertation and analysis of determining effective features in condition monitoring of equipments and their contribution, is the issue that has been studied through a Data Mining model.
Behin Elahi, Seyed Mohammad Seyed-Hosseini, Ahmad Makui,
Volume 22, Issue 2 (6-2011)
Abstract
Supplier selection, Multi-objective decision making, Fuzzy Compromise programming, Supply chain management, Quantity discount . |
Supplier selection is naturally a complex multi-objective problem including both quantitative and qualitative factors. This paper deals with this issue from a new view point. A quantity discount situation, which plays a role of motivator for buyer, is considered. Moreover, in order to find a reasonable compromise solution for this problem, at first a multi-objective modeling is presented. Then a proposed fuzzy compromise programming is utilized to determine marginal utility function for each criterion. Also, group decision makers’ preferences have taken into account and the weight of each criterion has been measured by forming pair-wise comparison matrixes. Finally the proposed approach is conducted for a numerical example and its efficacy and efficiency are verified via this section. The results indicate that the proposed method expedites the generation of compromise solution .
Mostafa Shirinfar, Hassan Haleh,
Volume 22, Issue 4 (12-2011)
Abstract
In this study, an outsourcer evaluation and management system is developed for a manufacturing company by use of Fuzzy goal programming (FGP). A first phase of the methodology evaluation criteria for outsources and the objectives of the company are determined. Considering the fuzziness in the decision data, linguistic variables that can be expressed in generalized fuzzy number are used. The propose approach is utilized from fuzzy sets, Analytic Network Process (ANP), fuzzy TOPSIS and Preference Ranking Organization method for enrichment evaluations (PROMETHEE) approaches. Evaluation criteria for this problem are weighted by Fuzzy ANP approach then in the Fuzzy TOPSIS and Fuzzy PROMETHEE approaches. At the second phase the FGP model developed selects the most appropriate outsourcers suitable to be strategic partners with the company and simultaneously allocates the quantities to be ordered to them. At the end, gives the computational results .
Kouroush Jenab, Samir Khoury, Ahmad Sarfaraz,
Volume 23, Issue 1 (3-2012)
Abstract
Evaluative and comparative analysis among educational projects remains an issue for administration, program directors, instructors, and educational institutes. This study reports a fuzzy complexity model for educational projects, which has two primary aspects (technical aspects and transparency aspects). These aspects may not be measured precisely due to uncertain situations. Therefore, a fuzzy graph-based model to measure the relative complexity of educational projects is presented that uses an aggregation operator to resolve conflict among experts with respect to a complexity relation. The model maps the fuzzy graph into a scaled Cartesian diagram that depicts the relative degree of complexity among projects. An illustrative example for several educational projects is demonstrated to present the application of the model.
