Golam Kabir

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.

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.

Hazardous Waste Transportation Firm Selection Using Fuzzy Analytic Hierarchy and PROMETHEE Methods

Evaluation of the proper and appropriate hazardous industrial waste transportation firm (HIWTF) is a vital problem for hazardous waste generators as hazardous and toxic industrial wastes may cause extensive damage to both humans and the environment when improperly handled. In general, many conflicting factors affect the appropriate HIWTF selection problem which adheres to uncertain and imprecise data. In this paper, a simple, systematic and logical scientific approach is structured to evaluate HIWTF through integrating fuzzy analytical hierarchy process (FAHP) with the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). The proposed decision-making approach takes advantage of the synergy between these two well-known multi-criteria decision-making (MCDM) methods in which their strong and weak points are detected and a ranking is provided which facilitates the final selection for the decision maker. To accredit the proposed model, it is implemented in an automotive battery manufacturing company in Bangladesh.

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.

Integrating fuzzy Delphi with graph theory and matrix methods for evaluation of hazardous industrial waste transportation firm

Hazardous and toxic industrial wastes may cause or significantly contribute to extensive damage to both humans and the environment when improperly handled. Evaluation of the proper and most appropriate hazardous industrial waste transportation firm is an important problem for hazardous waste generators. In this paper, a simple, systematic and logical scientific approach is structured to evaluate hazardous industrial waste transportation firm through integrating fuzzy Delphi method (FDM) with graph theory and matrix approach (GTMA). Fuzzy Delphi method used to identify the most important and significant criteria and most probable service providers, and graph theory and matrix approach evaluate and rank those transportation service provider firms. To accredit the proposed model, it is implemented in an automotive battery manufacturing company in Bangladesh.

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.