A review of fuzzy AHP methods for decision-making with subjective judgements

Analytic Hierarchy Process (AHP) is a broadly applied multi-criteria decision-making method to determine the weights of criteria and priorities of alternatives in a structured manner based on pairwise comparison. As subjective judgments during comparison might be imprecise, fuzzy sets have been combined with AHP. This is referred to as fuzzy AHP or FAHP. An increasing amount of papers are published which describe different ways to derive the weights/priorities from a fuzzy comparison matrix, but seldomly set out the relative benefits of each approach so that the choice of the approach seems arbitrary. A review of various fuzzy AHP techniques is required to guide both academic and industrial experts to choose suitable techniques for a specific practical context. This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems. The techniques are categorised by the four aspects of developing a fuzzy AHP model: (i) representation of the relative importance for pairwise comparison, (ii) aggregation of fuzzy sets for group decisions and weights/priorities, (iii) defuzzification of a fuzzy set to a crisp value for final comparison, and (iv) consistency measurement of the judgements. These techniques are discussed in terms of their underlying principles, origins, strengths and weakness. Summary tables and specification charts are provided to guide the selection of suitable techniques. Tips for building a fuzzy AHP model are also included and six open questions are posed for future work

Water Absorption Process Parametric Selection For Natural Composites Using The PROMETHEE Method And Analytical Hierarchy Process For Objective Weights For Ship’s Hull Application

The purpose of this study is to establish the importance of parameters in a water absorption process for natural particulate composite for ship’s hull applications. To attain useful and reliable outcomes, the subjective evaluation of the assessor and weights of inputs are combined in a PROMETHEE and analytical hierarchy process (AHP) approach. The PROMETHEE serves the goal of ranking while the AHP is deployed to establish the objective weighing. It was found that time is the heading parameter for the natural particulate thermoset composite solutions, compared with thickness and length. By integrating PROMETHEE and AHP, it was proved that this approach offers a higher level of confidence to composite developers than initiative practices that currently dominate choices of parameters. It is particularly useful for natural particulate water absorption parametric selection since it is an innovative and scientific choice approach involving multicriteria analysis

Extended Topics in the Integration of Data Envelopment Analysis and the Analytic Hierarchy Process in Decision Making.

The Analytic Hierarchy Process (AHP) is a procedure, which can only consider relative priorities as estimated by decision-makers. A Data Envelopment Analysis (DEA) model is a data-oriented approach for evaluating the relative efficiency of a group of entities referred to as Decision Making Units (DMUs). This research work integrates and combines positive aspects of AHP\u27s estimated qualitative data and DEA\u27s quantitative data. This combination is accomplished by specifying two variants of the DEA methodology for selection of the best DMU. Initially the priority weights of AHP are integrated with the DEA methodology to provide results that are logic based. Next, a method is developed to work backwards through the DEA model to provide values that would be the required results from an AHP formulation to give the same result in DEA. The objective of the research is to propose variants of DEA that would possibly improve the results and also integrate subjective data. Through the application of the methods developed in this research, it is believed that the acceptability of the results obtained from DEA analysis can be improved

Selection of the best maintenance approach in the maritime industry under fuzzy multiple attributive group decision-making environment

Many maintenance approaches have been developed and applied successfully in a variety of sectors such as aviation and nuclear industries over the years. Some of those have also been employed in the maritime industry such as condition based maintenance; however, choosing the best maintenance approach has always been a big challenge due to the involvement of many attributes and alternatives which can also be associated with multiple experts and vague information. In order to accommodate these aspects, and as part of an overall novel Reliability and Criticality Based Maintenance strategy, an existing fuzzy multiple attributive group decision-making technique is employed in this study, which is further enhanced with the use of Analytical Hierarchy Process to obtain a better weighting of the maintenance attributes used. The fuzzy multiple attributive group decision-making technique has three distinctive stages, namely rating, aggregation and selection in which multiple experts’ subjective judgments are processed and aggregated to be able to arrive at a ranking for a finite number of maintenance options. To demonstrate the applicability in a real-life industrial context, the technique is exemplified by selecting the best maintenance approach for shipboard equipment such as the diesel generator system of a vessel. The results denote that preventive maintenance is the best approach closely followed by predictive maintenance, thus steering away from the ship corrective maintenance framework and increasing overall ship system reliability and availability

EXPERT SYSTEM BASED APPROACH FOR MATERIAL SELECTION OF AUTOMOBILE BODY-IN-WHITE STRUCTURAL PANELS USING NUMERICAL RANKING AND SUSTAINABILITY INDICES

The goal of this work is to establish a set of quantifiable measures for design for sustainability (DFS) that can be applied to automotive applications in terms of environmental, social, economic and technical aspects. In this study, a comprehensive analysis was made in order to develop a methodology that can evaluate different body-in-white designs in terms of major sustainability aspects. Besides the complete life cycle analysis, environmental impacts and cost factors will be analyzed over vehicle\u27s entire life-cycle (fuel extraction and refining, Pre-manufacturing, Manufacturing, Use, and Post-use stages). The considered material options include: conventional steel, high strength steel, aluminum, magnesium, titanium and composites that are currently used in body-in-white (BIW) structures and exterior body panels. Sustainability scoring method was developed and used to decide on how using lighter materials in auto body applications is beneficial or not. The proposed major sustainable factors are categorized into four major groups: environmental, economical, social and technical groups. Also, each group has corresponding factors which were chosen by extensive search and screening, so only important sustainability aspects for auto body design have been selected in this study. Then the dissertation proceeds to show some sustainability scoring methods in order to get better understanding as well as relative ranking for different materials from sustainability point of view. Moreover, this work discusses the role and application of some multi-criteria decision making methods in materials selection, namely quality function deployment (QFD) and analytic hierarchy process (AHP). However, multi-criteria decision making methods are efficient tools to choose alternative from large set of alternatives, especially when two or more conflicting goals are present. Besides that, knowledge based system (KBS) was established for eco-material selection for auto-body structural panels. The goal behind using KBS is to help designers in material selection process which usually needs experience, time and effort

Computer-aided decision-making in construction project development

One of the most difficult problems in construction is taking objective decisions. A decision-making process is very complicated and time consuming (due to the complex nature of construction projects). Many experts with extensive knowledge of construction industry take subjective decisions related to verbal methods of decision-making. Difficulties are related mostly to the creation of a set of relevant criteria, providing answers to the decision-maker’s questions. A set of proper criteria and mathematical tools (such as computer calculation algorithms with multi-criteria analysis) could significantly improve objective decision-making. The paper presents ESORD – an informatics tool allowing to establish a hierarchy (ranking) of different types of solutions on the basis of mathematical calculation. The authors present a comparison of different methods used for multi-criteria decision-making

Optimal design based on fabricated SiC/B4C/porcelain filled aluminium alloy matrix composite using hybrid AHP/CRITIC-COPRAS approach

In this present study, SiC, B4C and waste porcelain reinforced AA7075 alloy composites are fabricated by adopting stir casting approach. Twelve formulations based on different weight percentages of reinforcers (3 wt.%, 4.5 wt.%, 6 wt.% and 7.5 wt.%.) were manufactured and afterward analysed in terms of physical, mechanical, corrosion and tribological performances. The reinforcers of less than 53 μm size were consistently blended in molten AA7075 accompanied by stirring process. To identify the best suitable formulation the density, hardness, tensile strength, compressive strength, flexural strength, friction coefficient, wear and corrosion rate were fixed as selection criteria. The composite containing 7.5 wt.% B4C (ASBP-8) exhibited the highest mechanical strength (Hardness=162 Hv; Tensile strength= 298 MPa; Compressive strength= 221 MPa; and Flexural strength= 267 MPa), whereas wear performance (at 40 N load and 1300 m SD= 0.00261 g; and at 5.026 m/s SV and 1300 m SD= 0.0231 g) and coefficient of friction (at 40 N load and 1300 m SD= 0.536 g; and at 5.026 m/s SV and 1300 m SD= 0.47 g) remain the lowest for 6 wt.% porcelain (ASBP-11) based composites. The density and corrosion rate remains lowest for the composite containing 7.5 wt.% porcelain (ASBP-12).Since no single composite(ASBP-1 to ASBP-12) could merely satisfy all the desired characteristics; to this end, this study applied a novel hybrid AHP/CRITIC-COPRAS method for the selection of optimal alternative material for automotive components. The weight of each material evaluated was determined by establishing a criterion of importance by applying inter-criteria correlation (CRITIC) and analytic hierarchy process (AHP) methods. The alternative ranking was evaluated using the complex proportional assessment (COPRAS) method. The evaluation indicated that the AA7075 containing 7.5 wt.% porcelain (ASBP-12) composite possesses the best material solution to be used in automotive applications

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 192

This bibliography lists 247 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1979

Designing Fuzzy Expert System to select managers based upon competency Case Study: Middle Managers of Automobile manufacturing company (Iran Khodro)

An essential and inevitable component of the efficiency of an organization is efficient managers. This study aims to design a fuzzy expert system in order to select Automobile manufacturing company middle managers based upon their competency. In contrast to conventional methods, which restrict assessment and measurement to specific criteria and supervisor's opinion, this research provides a comprehensive study to measure and compare managers' abilities. Thus, it has an applicable aspect. In terms of data collections and variables construct, this study is a descriptive research aims to modeling. The method of collecting data is field work and documentary research uses databases and experts' opinions. In order to construct a model, it used a mathematical framework (fuzzy Inference system). After designing a conceptual framework and verifying its validity, a MATLAB fuzzy toolbox has been used to design a fuzzy Inference system. In order to measure competency of a manager, a fuzzy deductive system has been designed at three levels. Sensivity analysis and limit analysis were used to measure the validity of the model. Finally, the designed model has been implemented in the area of research

Information Retrieval Performance Enhancement Using The Average Standard Estimator And The Multi-criteria Decision Weighted Set

Information retrieval is much more challenging than traditional small document collection retrieval. The main difference is the importance of correlations between related concepts in complex data structures. These structures have been studied by several information retrieval systems. This research began by performing a comprehensive review and comparison of several techniques of matrix dimensionality estimation and their respective effects on enhancing retrieval performance using singular value decomposition and latent semantic analysis. Two novel techniques have been introduced in this research to enhance intrinsic dimensionality estimation, the Multi-criteria Decision Weighted model to estimate matrix intrinsic dimensionality for large document collections and the Average Standard Estimator (ASE) for estimating data intrinsic dimensionality based on the singular value decomposition (SVD). ASE estimates the level of significance for singular values resulting from the singular value decomposition. ASE assumes that those variables with deep relations have sufficient correlation and that only those relationships with high singular values are significant and should be maintained. Experimental results over all possible dimensions indicated that ASE improved matrix intrinsic dimensionality estimation by including the effect of both singular values magnitude of decrease and random noise distracters. Analysis based on selected performance measures indicates that for each document collection there is a region of lower dimensionalities associated with improved retrieval performance. However, there was clear disagreement between the various performance measures on the model associated with best performance. The introduction of the multi-weighted model and Analytical Hierarchy Processing (AHP) analysis helped in ranking dimensionality estimation techniques and facilitates satisfying overall model goals by leveraging contradicting constrains and satisfying information retrieval priorities. ASE provided the best estimate for MEDLINE intrinsic dimensionality among all other dimensionality estimation techniques, and further, ASE improved precision and relative relevance by 10.2% and 7.4% respectively. AHP analysis indicates that ASE and the weighted model ranked the best among other methods with 30.3% and 20.3% in satisfying overall model goals in MEDLINE and 22.6% and 25.1% for CRANFIELD. The weighted model improved MEDLINE relative relevance by 4.4%, while the scree plot, weighted model, and ASE provided better estimation of data intrinsic dimensionality for CRANFIELD collection than Kaiser-Guttman and Percentage of variance. ASE dimensionality estimation technique provided a better estimation of CISI intrinsic dimensionality than all other tested methods since all methods except ASE tend to underestimate CISI document collection intrinsic dimensionality. ASE improved CISI average relative relevance and average search length by 28.4% and 22.0% respectively. This research provided evidence supporting a system using a weighted multi-criteria performance evaluation technique resulting in better overall performance than a single criteria ranking model. Thus, the weighted multi-criteria model with dimensionality reduction provides a more efficient implementation for information retrieval than using a full rank model