Evaluating R&amp;amp;D Projects as Investments by Using an Overall Ranking From Four New Fuzzy Similarity Measure-Based TOPSIS Variants

Collan, Mikael; Luukka, Pasi

doi:10.1109/tfuzz.2013.2260758

Cited by 53 publications

(33 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Following Luukka [26] and Collan and Luukka [27], we used fuzzy similarity both to calculate the distance between fuzzy ratings and to make the final evaluation and ranking. In our study, the fuzzy ratings and the weights of criteria were assigned as the values of linguistic variables.…”

Section: Methodsmentioning

confidence: 99%

Assessing the Effect of Knowledge Management Initiatives on Stakeholder Objectives Using Fuzzy TOPSIS

Hájek

Mansfeldová

2015

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

Abstract. Comprehensive assessment tools to prioritize KM initiatives are critical for KM adoption. This paper aims to develop a hierarchical fuzzy TOPSIS model to rank KM initiatives in terms of their effect on often contradictory stakeholder objectives. The empirical case study of a medical supplier company demonstrates that this group multi-criteria decision making tool can effectively address both the uncertainty in KM assessment and the different effects of KM processes on individual stakeholder objectives. Thus, it may contribute to the successful KM implementation process.

show abstract

Section: Methodsmentioning

confidence: 99%

Assessing the Effect of Knowledge Management Initiatives on Stakeholder Objectives Using Fuzzy TOPSIS

Hájek

Mansfeldová

2015

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

show abstract

“…Moreover, it deals effectively with uncertain and imprecise information to solve real-world problems in different domains including cloud computing. Furthermore, the success of fuzzy and TOPSIS methods is measured by solving several MCDA problems, such as management of research and development projects as a portfolio of investments [57]. Accordingly, in this paper, we focus on using the benefits of these MCDA methods to solve the selection of services in the fog-to-cloud environment.…”

Section: Ranking Fog/cloud Servicesmentioning

confidence: 99%

Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services

et al. 2018

View full text Add to dashboard Cite

Smart cities aim at integrating various IoT (Internet of Things) technologies by providing many opportunities for the development, governance, and management of user services. One of the ways to support this idea is to use cloud and edge computing techniques to reduce costs, manage resource consumption, enhance performance, and connect the IoT devices more effectively. However, the selection of services remains a significant research question since there are currently different strategies towards cloud computing, including services for central remote computing (traditional cloud model) as well as distributed local computing (edge computing). In this paper, we offer an integrated view of these two directions and the selection among the edge technologies based on MCDA (Multiple Criteria Decision Analysis) algorithms. To this end, we propose a foglet as a middleware that aims at achieving satisfactory levels of customer services by using fuzzy similarity and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to facilitate the rating and selection of services in the fog-to-cloud environment. Then, we describe the selection process with a numerical example, and conclude our work with an outline of future perspectives.

show abstract

“…The works of [16][17][18] are similar to Scoring and Ranking methods [10] or additive functions [12,19] in that they prioritize projects according to a certain utility function to measure their importance. Alternatively, the use of proxy variables [9,11,14] has offered versatile and satisfactory results that extend the information derived from a ranking of projects.…”

Section: A Brief Outline Of Previous Approachesmentioning

confidence: 99%

Design and Solution of a Surrogate Model for Portfolio Optimization Based on Project Ranking

Fernández

Gómez-Santillán

Cruz-Reyes

et al. 2017

Scientific Programming

View full text Add to dashboard Cite

Characterizing the preferences of a decision maker in a multicriteria decision is a complex task that becomes even harder if the information available is limited. This paper addresses a particular case of project portfolio selection; in this case, the measures of project impacts are not assumed, and the available information is only projects' ranking and costs. Usually, resource allocation follows the ranking priorities until they are depleted. This action leads to a feasible solution, but not necessarily to a good portfolio. In this paper, a good portfolio is found by solving a multiobjective problem. To effectively address such dimensionality, the decision maker's preferences in the form of a fuzzy relational system are incorporated in an ant-colony algorithm. The Region of Interest is approached by solving a surrogate triobjective problem. The results show that the reduction of the dimensionality supports the decision maker in choosing the best portfolio.

show abstract

Evaluating R&D Projects as Investments by Using an Overall Ranking From Four New Fuzzy Similarity Measure-Based TOPSIS Variants

Cited by 53 publications

References 31 publications

Assessing the Effect of Knowledge Management Initiatives on Stakeholder Objectives Using Fuzzy TOPSIS

Assessing the Effect of Knowledge Management Initiatives on Stakeholder Objectives Using Fuzzy TOPSIS

Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services

Design and Solution of a Surrogate Model for Portfolio Optimization Based on Project Ranking

Contact Info

Product

Resources

About