M. Ahsan Akhtar Hasin

Algorithms for fuzzy multi expert multi criteria decision making (ME-MCDM)

Inherent complexity and uncertainty in a business environment necessitate the participation of many experts in multi criteria decision making. However, participation of many experts makes the conflict aggregation process difficult. To handle this difficulty, we propose two algorithms namely possibility measure and averaging conflict aggregation. In possibility measure, we integrate the possibility theory of fuzzy logic with a maximal containment method that is designed based on the decision problem. Possibility measure algorithm for ME-MCDM involves computationally expensive multiple information processing steps. Therefore to test and compare this algorithm, averaging conflict aggregation algorithm is proposed that requires fewer mathematical information processing steps. Based on the proposed algorithms, a decision support system (DSS) is developed. We present a case study of supplier evaluation to compare both of the proposed algorithms with the help of developed DSS.

Multi-criteria inventory classification through integration of fuzzy analytic hierarchy process and artificial neural network

A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. To efficiently control the inventory items and to determine the suitable ordering policies for them, multi- criteria inventory classification is used. The objective of this research is to develop a multi-criteria inventory classification model through integration of fuzzy analytic hierarchy process (FAHP) and artificial neural network approach. FAHP is used to determine the relative weights of the attributes or criteria using Changs extent analysis and to classify inventories into different categories. Various structures of multi-layer feed-forward back-propagation neural networks have been analysed and the optimal one with the minimum mean absolute percentage of error between the measured and the predicted values have been selected. To accredit the proposed model, it is implemented for 351 raw materials of switchgear section of Energypac Engineering Limited, a large power engineering company of Bangladesh.

Supplier evaluation with GD-based multi criteria decision making

Inherent complexity and uncertainty in a business environment necessitates the consideration of conflicting multi criteria in the supplier evaluation process. In multi criteria supplier evaluation process, there are many decision situations in which the information cannot be assessed precisely in a quantitative form but may be in a qualitative one. This research aims to develop a computing tool which can evaluate the supplier by taking the opinion of expert as a linguistic value in a fuzzy form and incorporating the uncertainty measure. The use of linguistic labels makes expert judgement more reliable and informative for decision making. To address the uncertainty during decision making, Alpha-cut which is a fuzzy algebra, shall be used to get a crisp range from a fuzzy range. To enlighten the effects of uncertainty, objective of this research is to perform a sensitivity analysis using the range of truth-value using alpha-cut and see how the decision is affected.

Integrating modified Delphi method with fuzzy AHP for optimal power substation location selection

Evaluation and selection of power substation location is an important strategic decision-making problem for both public and private sector. The multi-dimensional, multicriteria nature of the power substation location problem limits the usefulness of any particular single objective model. Studies have shown that electromagnetic radiations emanating from the high voltage lines and substations, pose serious adverse health consequences to living beings. In this study, social, technological, economical, environmental and site characteristics factors and sub-criteria, have been derived to make the optimal power substation location selection decision more realistic and effectual. In this paper, an improved and more appropriate power substation location evaluation and selection model has been developed by integrating modified Delphi method with fuzzy analytic hierarchy process (FAHP). A numerical example is presented to show applicability and performance of the proposed methodology followed by a sensitivity analysis to discuss and explain the results.

Fuzzy Based Project Time-Cost Optimization Using Simulated Annealing Search Technique

The Project time-cost optimization is inherently a complex task. Because of various kinds of uncertainties, such as weather, productivity level, inflation, human factors etc. during project execution process, time and cost of each activity may vary significantly. The complexity multiplies several folds when the operational times are not deterministic, rather fuzzy in nature. Therefore, deterministic models for time-cost optimization are not yet efficient. It is very difficult to find the exact solution of savings in both time and cost. To make such problems realistic, triangular fuzzy numbers and the concept of a-cut method in fuzzy logic theory are employed to model the problem. Because of NP-hard nature of the project scheduling problem, this paper develops a simple approach with Simulated Annealing (SA) based searching technique. The proposed model leads the decision makers to choose the desired solution under different values of a-cut. Finally, taking a real project, the performance of SA has been tested.

Credit Risk Assessment and Evaluation System for Industrial Project

Most of the banks are interested to provide high quality financial aid, known as credit, to their customers to contribute to the growth of gross domestic product (G.D.P.) of the country. The traditional credit risk management technique is dominated by the lending risk analysis (LRA) manual introduced by the Bangladesh Bank and recently the credit risk grading (CRG) system has been introduced which is not appropriately structured for the case of funding an industrial project. So, to overcome the lacking of existing LRA and CRG system regarding risk assessment for industrial project, an improved risk assessment model for is developed which gives a complete solution for risk evaluation for any industrial project. The purpose of this paper is to provide a standardized risk assessment and evaluation system especially for funding industrial projects and also assist in the ongoing improvement of the banking sector in Bangladesh by adopting a standardized approach in the form of credit risk grading (CRG) system. The whole model is divided into six risk components and each type of risk is again divided into some criteria which are crucial risk determinants and further the criteria are scored against specific parameters in order to assess the final grading score. Beside this a case study of a reputed cement industry project is also conducted and the risk of the proposed project is successfully evaluated using the proposed risk assessment model.

Application of adaptive neuro fuzzy inference system in demand forecasting for power engineering company

To enhance the commercial competitive advantage in a constantly fluctuating business environment, an organisation has to make the right decisions in time depending on demand information. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. Forecasting becomes a crucial process for manufacturing companies to effectively guiding several activities, and research has devoted particular attention to this issue. The objective of the paper is to propose a new forecasting mechanism which is modelled by adaptive neuro-fuzzy inference system (ANFIS) techniques to manage the fuzzy demand with incomplete information. ANFIS is utilised to harness the power of the fuzzy logic and artificial neural networks (ANN) through utilising the mathematical properties of ANNs in tuning rule-based fuzzy systems that approximate the way human’s process information. To accredit the proposed model, it is implemented to forecast the demand of distribution transformer of a power engineering c...

Integrating fuzzy AHP with TOPSIS method for optimal power substation location selection

Electromagnetic radiations emanating from the high voltage lines and substations pose serious adverse health consequences to living beings. As a result, evaluation and selection of power substation location is an important strategic decision making problem for both public and private sector. In this paper, fuzzy analytic hierarchy process (FAHP) has been integrated with technique for order preference by similarity to an ideal solution (TOPSIS) method to develop an improved and more appropriate power substation location evaluation and selection model considering social, technological, economical, environmental and site characteristics factors and sub criteria. The proposed decision-making approach takes advantage of the synergy between these two well-known multi-criteria analysis (MCA) methods. A numerical example is presented to show applicability and performance of the proposed methodology followed by a sensitivity analysis to discuss and explain the results.

Comparative Analysis of Artificial Neural Networks and Neuro-Fuzzy Models for Multicriteria Demand Forecasting

ABSTRACT An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks (ANN) and adaptive network-based fuzzy inference system (FIS) techniques to manage the fuzzy demand with incomplete information. Artificial neural networks has been applied as it is capable to model complex, nonlinear processes without having to assume the form of the relationship between input and output variables. Neuro-fuzzy systems also utilized to harness the power of the fuzzy logic and ANNs through utilizing the mathematical properties of ANNs in tuning rule-based fuzzy systems that approximate the way human’s process information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated for a 20/25 MVA Distribution Transformer from Energypac Engineering Limited (EEL), a leading power engineering company of Bangladesh.DOI: 10.4018/ijfsa.2013010101

Fuzzy analysis for higher effectiveness of control chart

This paper addresses the problems occurring in the control chart and develops a new way to improve the evaluation process of this widely used tool. Classical limitation of control chart is its inability to accurately diagnose the out of control situation. In the Shewharts control chart, the effects of the variation of the process points plotted on the control chart are not taken into account for evaluation, which can be misleading for finding the out of control situations. For a more perfect evaluation, the variation of the positions of these points is needed to be incorporated. The research deals with this classical limitation of the control chart. Through a real illustrative example, this paper introduces the concept of fuzzy control chart as a tool for verifying level of variation, and trend in quality characteristics incorporating uncertainty by introducing linguistic variables to identify the position of the points in the control chart. The use of linguistic labels provides better neural view to inspectors for acceptance or rejection of process, offering different strategic options for company to choose etc.