Fuzzy Multi-attribute Decision-making Approach Research Articles

A new integrated multi-attribute decision-making approach for mobile medical app evaluation under q-rung orthopair fuzzy environment

Mobile medical app evaluation can be modelled as a multi-attribute decision-making (MADM) problem with multiple assessment attributes. Due to the increasing complexity and high uncertainty of decision environments, numerical numbers and/or traditional fuzzy sets may not be appropriate to model attribute information of mobile medical apps. In addition, heterogeneous relationships are often observed among different attributes in various practical decision situations. To deal with these issues, a q-rung orthopair fuzzy (q-ROF) MADM approach, which is a very powerful tool for describing vague information occurring in real decision circumstances, is proposed to handle decision-making problems in medical app evaluation. In particular, q-rung orthopair fuzzy numbers (q-ROFNs) are first applied to better express the preference information and expert assessment information. Then, q-ROFNs are extended by combining with Zhenyuan integral, resulting in the q-ROF Zhenyuan integral (q-ROFZI). This integral can capture complementary, redundant and/or independent characteristics among the attributes and is superior to existing operators on q-ROFNs. Next, based on the best-worst method (BWM) and Shapley value, two optimization models are constructed to objectively identify optimal fuzzy measures on the attribute set. Finally, a novel integrated q-ROF MADM approach is proposed and its computation procedure is presented and illustrated with its application to the problem of mobile medical app evaluation. A comparative analysis is carried out to demonstrate the validity, rationality, robustness and superiority of the developed method.

Fuzzy multi-attribute decision-making approach for the selection of software effort estimation models

Fuzzy multi-attribute decision-making approach for the selection of software effort estimation models

An Evaluation Framework for Communication and Coordination Processes in Offshore Software Development Outsourcing Relationship: Using Fuzzy Methods

Offshore Software Development Outsourcing (OSDO) is a modern business strategy for producing high quality software at low cost. OSDO refers to the practice of contracting to an offshore (extrinsic) organization to perform some or all software development work of a product. For the benefit of OSDO vendors, the present research aims to develop a “Communication and Coordination Challenges’ Mitigation Model” (CCCMM) that provides solutions for unambiguously defined communication and coordination processes in a global software development (GSD) environment. Our proposed model is based on the fuzzy multi-attribute decision-making (FMADM) approach incorporating the capability of group decision making. FMADM approach is used both in ranking of survey and assessment of case studies. Firstly, authors undertook a systematic literature review (SLR) that identified all cited challenges from a set of 101 articles. We identified eighteen problem areas faced by GSD vendors in OSDO relationships. Of these, six were ranked as critical. For the purposes of identifying corrective interventions, a second SLR was conducted that revealed 75 remedial measures extracted from 63 chosen articles. To validate our SLR findings, we surveyed 42 outsourcing experts from six countries. We also categorized six critical challenge areas and 75 corrective practices into four mitigation levels based on CMMI, SOVRM and SOPM. In addition, two case studies were conducted to evaluate CCCMM outcomes in OSDO companies. The assessment results of first case study do not recommended Company-A for successful implementation of level-2 of CCCMM, so Company-A stands at level 1. We have observed from second case study that Company-B has implemented all the critical challenges of the level-2 only; therefore Company-B is at level-2 success of the proposed assessment model.

An Evaluation Framework for Communication and Coordination Processes in Offshore Software Development Outsourcing Relationship: Using Fuzzy Methods

Offshore software development outsourcing (OSDO) is a modern business strategy for producing high-quality software at a low cost. The OSDO refers to the practice of contracting to an offshore (extrinsic) organization to perform some or all software development work of a product. For the benefit of the OSDO vendors, this paper aims to develop a “communication and coordination challenges mitigation model” (CCCMM) that provides solutions for unambiguously defined communication and coordination processes in global software development (GSD) environment. Our proposed model is based on the fuzzy multi-attribute decision-making (FMADM) approach incorporating the capability of group decision-making. The FMADM approach is used both in the ranking of survey and assessment of case studies. First, the authors undertook a systematic literature review (SLR) that identified all cited challenges from a set of 101 articles. We identified 18 problem areas faced by the GSD vendors in OSDO relationships. Of these, six were ranked as critical. For the purpose of identifying corrective interventions, a second SLR was conducted that revealed 75 remedial measures extracted from 63 chosen articles. To validate our SLR findings, we surveyed 42 outsourcing experts from six countries. We also categorized six critical challenges and 75 corrective practices into four mitigation levels based on CMMI, SOVRM, and SOPM. In addition, two case studies were conducted to evaluate CCCMM outcomes in OSDO companies. The assessment results of the first case study do not recommend Company-A for the successful implementation of level-2 of the CCCMM, so Company-A stands at level-1. We have observed from the second case study that Company-B has implemented all the critical challenges of the level-2 only; therefore, Company-B is at level-2 “success” of the proposed assessment model.

Prioritizing disaster-prone areas for large-scale earthquakes' preparedness: Methodology and application

This paper presents a new hybrid fuzzy multi-attribute decision-making approach to prioritize disaster-prone areas which are known as potential demand points (PDPs) regarding their vulnerability under large-scale earthquakes. Significant criteria for prioritizing PDPs are first determined. Then, the fuzzy DEMATEL is applied to specify interrelationship between the criteria, and the weights of criteria are achieved by the fuzzy ANP. Finally, the fuzzy PROMETHEE II is used to rank the PDPs. The proposed methodology is validated using a conventional relief pre-positioning network design model. Numerical results demonstrate the applicability and usefulness of the presented approach in configuring a more responsive relief network in practice.

An integrated dynamic intuitionistic fuzzy MADM approach for personnel promotion problem

Purpose – One of the key success factors for an organization is the promotion of qualified personnel for vacant positions. Especially, the promotion of middle and senior managers play an important role in terms of organization’s success. In personnel promotion problem in which the candidates are nominated within the organization and they have been working for a specific period of time and are known in their organization, the candidates should be evaluated based on their recent as well as past performances to make right selection for the vacant position. For this reason, the purpose of this paper is to propose an integrated dynamic multi-attribute decision-making (MADM) model based on intuitionistic fuzzy set for solving personnel promotion problem. Design/methodology/approach – The proposed model integrates analytic hierarchy process (AHP) technique and the dynamic evaluation by intuitionistic fuzzy operator for personnel promotion. AHP is employed to determine the weight of attributes based on decision maker’s opinions, and the dynamic operator is utilized to aggregate evaluations of candidates for different years. Atanassov’s intuitionistic fuzzy set theory is utilized to represent uncertainty and vagueness in MADM process. Findings – A numerical example is presented to show the applicability of the proposed method for personnel promotion problem and a sensitivity analysis is conducted to demonstrate efficiency of dynamic evaluation. The findings indicate that the varying weights of years employed determined the best candidate for promotion. Originality/value – The novelty of this study is defining personnel promotion as a MADM problem in the literature for the first time and proposing an integrated dynamic intuitionistic fuzzy MADM approach for the solution, in which the candidates are evaluated at different years.

Integrate inconsistent and heterogeneous data based on user feedback

Purpose – Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data. An important issue on data integration is the existence of conflicts among the different data sources. Data sources may conflict with each other at data level, which is defined as data inconsistency. The purpose of this paper is to aim at this problem and propose a solution for data inconsistency in data integration. Design/methodology/approach – A relational data model extended with data source quality criteria is first defined. Then based on the proposed data model, a data inconsistency solution strategy is provided. To accomplish the strategy, fuzzy multi-attribute decision-making (MADM) approach based on data source quality criteria is applied to obtain the results. Finally, users feedbacks strategies are proposed to optimize the result of fuzzy MADM approach as the final data inconsistent solution. Findings – To evaluate the proposed method, the data obtained from the sensors are extracted. Some experiments are designed and performed to explain the effectiveness of the proposed strategy. The results substantiate that the solution has a better performance than the other methods on correctness, time cost and stability indicators. Practical implications – Since the inconsistent data collected from the sensors are pervasive, the proposed method can solve this problem and correct the wrong choice to some extent. Originality/value – In this paper, for the first time the authors study the effect of users feedbacks on integration results aiming at the inconsistent data.

A multiattribute decision making method: Using individual and collaborative attributes

Although the multiattribute decision making (MADM) problem using not only the individual attributes but also the collaborative attributes exists extensively in the real world, while it is seldom addressed in the existing literature. The aim of this paper is to present the MADM problem using individual and collaborative attributes and propose a fuzzy MADM approach for solving it. In this paper, we firstly characterize the MADM problem with individual and collaborative attributes using a structure model. Then, we propose a fuzzy MADM approach to integrate the individual performance and the collaborative performance to achieve the final ranking of alternatives. Additionally, we illustrate the current problem and the proposed approach in this paper using a cases study of partner selection of a codevelopment alliance.

Using fuzzy number for measuring quality of service in the hotel industry

This paper presents a fuzzy multi-attribute decision-making approach for evaluating dynamically the service quality of three hotels of an important corporation in Gran Canaria island via surveys. Service quality is a composite of various attributes, among them many attributes are intangible and difficult to measure. Fuzzy numbers (FN) are based on triangular fuzzy numbers and fuzzy set theory as a new method to overcome some linguistic problems. In other words, fuzzy numbers try to resolve the ambiguity of concepts that are associated with human beings’ subjective judgments vaguely measured with linguistic terms. Based on the concept of the degree of optimality, we develop through TOPSIS an overall service performance index for each pair hotel–date of survey. This index will help hotel managers to understand their relative ranking position, and provide an adequate alternative to performance evaluation of hotel services which usually involve subjective judgments of qualitative attributes.

A reference framework for the application of MADM fuzzy techniques to selecting AMTS

In the last decade, there has been growing interest in the assessment and selection of investments in advanced manufacturing technologies (AMTs). Several authors have pointed out the shortcomings of traditional financial techniques, proposing alternative approaches usually referred to as 'non-conventional' approaches. Among them, some authors have recently proposed fuzzy multi-attribute decision making (MADM) techniques, which seem to be particularly suitable for dealing with the intangible effects of AMTs. However, no attempt has so far been made to analyse and compare the various MADM techniques included in the fuzzy theory with respect to their applicability to the assessment and selection of AMTs. The objective of this article is to give a reference framework for the application of a MADM fuzzy approach to AMT selection. In particular, the paper describes three major categories of fuzzy MADM approach and compares their results using a case study.

Development of a machine troubleshooting expert system via fuzzy multiattribute decision-making approach

Most current machine fault diagnosis systems emphasize on the correctness of the hypothesized result; however, in time-constrained situations, the efficiency of the diagnostic process becomes more important and should not be overlooked. This paper presents an Integrated Machine Troubleshooting Expert System (IMTES) that enhances the efficiency of the diagnostic process, improves the completeness and consistency of the knowledge base, and assists users in developing and maintaining their diagnostic systems. IMTES consists of five modules: a diagnostic tree module establishes the hierarchical structure regarding the function or connectivity of the diagnostic system, a fuzzy multiattribute decision-making module determines the most efficient diagnostic process and creates a “meta knowledge base” to control the diagnosis process, a knowledge base module captures the human expertise and deep knowledge to diagnose the possible machine fault, an inference engine module controls the diagnosis process and deals with the uncertainty from the user input and knowledge base itself, and a learning module uses the failure-driven learning method to train the knowledge base from the past actual cases. The system has been successfully implemented on MS-Windows environment, and it is written in MS Visual BASIC. To validate the system performance, IMTES is compared to EXACT, an expert system for automobile air-compressor troubleshooting, using 50 sample cases. The result shows that IMTES can reduce the number of queries by 20.7%.

ファジィ多属性意思決定法による電線着雪防止対策の評価

ファジィ多属性意思決定法による電線着雪防止対策の評価