Selection of optimum methods which have appropriate speed and precision for planning and de-cision-making has always been a challenge for investors and managers. One the most important concerns for them is investment planning and optimization for acquisition of desirable wealth under controlled risk with the best return. This paper proposes a model based on Markowitz the-orem by considering the aforementioned limitations in order to help effective decisions-making for portfolio selection. Then, the model is investigated by fuzzy logic and genetic algorithms, for the optimization of the portfolio in selected active companies listed in Tehran Stock Exchange over the period 2012-2016 and the results of the above models are discussed. The results show that the two studied models had functional differences in portfolio optimization, its tools and the possibility of supplementing each other and their selection.
It is very important to manage and control projects with the consideration of the triple constraints; namely time, cost and scope. It is also extremely important to manage the scope and all the procurements needed to complete any project. During the project’s lifecycle many changes take place, either positively or negatively, which should be controlled. If the changes are not controlled we may have scope creep that has negative effect on the project. It is commonly considered a negative incident, and thus, should be kept away from the project. By considering this concept, in this paper, we discuss scope change and managing scope and fuzzy analytical hierarchy process is used in selecting the best strategy to manage scope change in projects.
Six Sigma is considered as a logical business strategy that attempts to identify and eliminate the defects or failures for improving the quality of product and processes. A decision on project selection in Six Sigma is always very critical; it plays a key role in successful implementation of Six Sigma. Selection of a right Six Sigma project is essentially important for an automotive company because it greatly influences the manufacturing costs. This paper discusses an approach for right Six Sigma project selection at an automotive industry using fuzzy logic based TOPSIS method. The fuzzy TOPSIS is a well recognized tool to undertake the fuzziness of the data involved in choosing the right preferences. In this context, evaluation criteria have been designed for selection of best alternative. The weights of evaluation criteria are calculated by using the MDL (modified digital logic) method and final ranking is calculated through priority index obtained by using fuzzy TOPSIS method. In the selected case study, this approach has rightly helped to identify the right project for implementing Six Sigma for achieving improvement in productivity.
Six Sigma is a strategic approach of significant value in achieving overall excellence. It helps to accomplish the organizations strategic aim through the effectual use of project controlled methodology. As Six Sigma is a project controlled approach, it is necessary to prioritize projects which give utmost economic benefits to the firm. In real practice, Six Sigma projects selection is very tough assignment because poor project selection also happens even in the well-managed organizations and this can weaken the success and trustworthiness of the Six Sigma practice. The present study aims to develop a project selection approach based on a combination of fuzzy and MADM technique to help organizations determine proper Six Sigma projects and identify the priority of these projects mainly in automotive companies. VIKOR and TOPSIS methods have been used to select the proper Six Sigma project composed with fuzzy logic. In this context, seven critical parameters have been considered for selection of finest alternative. The weights of evaluation criteria are obtained using the MDL (modified digital logic) method and final ranking is calculated through primacy index obtained by using fuzzy based VIKOR and TOPSIS methodology. A factual case study from automotive industry is used to investigate the efficacy of the planned approach.
This research aims to offer a fuzzy approach for calculating Tehran & apos; s air pollution index. The method is based on fuzzy analysis model, and uses the information about air quality index (AQI), included on the website of Tehran’s Air Quality Monitoring And Supervision Bureau. The contrived fuzzy logic is considered a powerful tool for demonstrating the information associated with uncertainty. In the end, several graphs visualize this inferential system in various levels of pollution.
Capacity waste management is highly essential because under utilization of capacity is often referred to as a major reason for lower productivity among industries around the world. For better estimation of capacity and its utilization and then for its improved management; newer techniques are being devised in industrial sector. The current case of capacity waste problem has been taken up as a Six Sigma project, where we try to analyze critical factors responsible for the capacity waste. Decisions on critical factor selection in analysis phase of Six Sigma are always very crucial. The paper discusses an approach for selection of capacity waste factors at an automotive industry using fuzzy logic based AHP method. The fuzzy AHP is a well recognized tool to undertake the fuzziness of the data involved in choosing the preferences of the different decision variables engaged in the process of capacity waste factors selection. In this context, we have explored six crucial parameters for selection of capacity waste factors. Final ranking is calculated through priority vector thus obtained and it is seen that conveyor malfunction is found to be the key factor for capacity waste among all alternatives at the selected site.
Six Sigma is a philosophy of unremitting improvement and excellence in all aspects. The concept is a satisfactory modification process tool through customers, continuous improvement and stakeholder participation. Six Sigma is considered as statistical analysis, assessment scales and customer-oriented production accomplishments and it leads to defect production reduction. This paper recommends an approach to select Six Sigma projects using fuzzy multiple attribute decision making techniques composed with another concoction tool. Through insightful quarrying of literature, rudimentary criteria for selecting Six Sigma projects were revealed. The fundamental criteria were identified consuming the fuzzy hypothesis test. Having identified the most indispensable criteria, the weight of criteria were determined. Appling FANP techniques. Having calculated the weights pertinent to criteria through three methods, SAW, TOPSIS, and Fuzzy VIKOR, Six Sigma projects were introduced and prioritized. Applying the three methods engendered various results, which required the application of an amalgamation technique, entitled as Borda and it helped to clarify the final project rate.
In this paper, a comparison is presented between two prime methods of producing prosthetic sockets by using the fuzzy linguistic hedges approach on the qualitative feedback of Indian prosthetic users. Recent trends indicate that the Indian manufacturers have tried to adopt the newer technologies like reverse engineering (RE) approach to achieve the desired goals. However, the satisfaction of the user is of utmost importance for the unique and customized products for rehabilitation. In order to analyze the effectiveness of the manufacturing approaches, user case studies are taken, based on the linguistic feedbacks, and a comparative study is conducted. Thirteen users from four different manufacturing units are taken for study and sockets made by conventional as well as RE are experimented. Fuzzy membership functions are constructed using the linguistic hedges based on the user feedbacks. An analytical hierarchy process (AHP) is applied to arrive at a decision to select the manufacturing process for user satisfaction and manufacturing excellence.
Electricity distribution systems are considered as the most critical sectors in countries because of the essentiality of power supplement security, socioeconomic security, and way of life. According to the central role of electricity distribution systems, risk analysis helps decision maker determine the most serious risk items to allocate the optimal amount of resources and time. Probability-impact (PI) matrix is one of the most popular methods for assessment of the risks involved in the system. However, the traditional PI matrix is criticized for its inability to take into account the inherent uncertainty imposed by real-world systems. On the other hand, fuzzy sets are capable of handling the uncertainty. Thus, in this paper, fuzzy risk assessment model is developed in order to assess risk and management for electricity distribution system asset protection. Finally, a comparison analysis is conducted to show the effectiveness and the capability of the new risk assessment model.
The purpose of this article is to consider system safety and reliability analysts to evaluate the risk associated with item failure modes. The factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence (O), severity (S) and detectability (D) of an item failure mode. Because of the subjective, qualitative and dynamic nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic and system dynamics methodology is proposed. In the proposed approach, severity is replaced by dependency parameter then, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and they are evaluated in fuzzy inference engine, which makes use of well-defined rule base and fuzzy logic operations to determine the value of parameters related to system’s transfer functions. The fuzzy conclusion is then defuzzified to get transfer function for risk and failure rate. The applicability of the proposed approach is investigated with the help of an illustrative case study from the automotive industry.