A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency – combination of fuzzy QFD and DEA

© 2018 Elsevier Ltd Resources of an organisation (people, time, money, equipment, etc) are never endless. As such, a constant and continuous challenge for decision makers is to decide which projects should be given priority in terms of receiving critical resources in a way that the organisation's productivity and profitability is best guaranteed. Previous literature has already developed a plenitude of project portfolio selection methodologies ranging from simple scoring to complex mathematical models. However, most of them too often fail to propose one integrated and seamless method that can simultaneously take into account three important elements: (1) prioritisation of selection criteria over each other, (2) uncertainty in decision-making, and (3) projects interdependencies. This paper aims to fill this gap by proposing an integrated method that can simultaneously address all these three aspects. The proposed method combines Quality Function Development (QFD), fuzzy logic, and Data Envelopment Analysis (DEA) to accounts for prioritisation, uncertainty and interdependency. We then apply this method in a numerical example from a real world case to illustrate the applicability and efficacy of the proposed methodology

Development, test and comparison of two Multiple Criteria Decision Analysis(MCDA) models: A case of healthcare infrastructure location

When planning a new development, location decisions have always been a major issue. This paper examines and compares two modelling methods used to inform a healthcare infrastructure location decision. Two Multiple Criteria Decision Analysis (MCDA) models were developed to support the optimisation of this decision-making process, within a National Health Service (NHS) organisation, in the UK. The proposed model structure is based on seven criteria (environment and safety, size, total cost, accessibility, design, risks and population profile) and 28 sub-criteria. First, Evidential Reasoning (ER) was used to solve the model, then, the processes and results were compared with the Analytical Hierarchy Process (AHP). It was established that using ER or AHP led to the same solutions. However, the scores between the alternatives were significantly different; which impacted the stakeholders‟ decision-making. As the processes differ according to the model selected, ER or AHP, it is relevant to establish the practical and managerial implications for selecting one model or the other and providing evidence of which models best fit this specific environment. To achieve an optimum operational decision it is argued, in this study, that the most transparent and robust framework is achieved by merging ER process with the pair-wise comparison, an element of AHP. This paper makes a defined contribution by developing and examining the use of MCDA models, to rationalise new healthcare infrastructure location, with the proposed model to be used for future decision. Moreover, very few studies comparing different MCDA techniques were found, this study results enable practitioners to consider even further the modelling characteristics to ensure the development of a reliable framework, even if this means applying a hybrid approach

SINVLIO: using semantics and fuzzy logic to provide individual investment portfolio recommendations

Portfolio selection addresses the problem of how to diversify investments in the most efficient and profitable way possible. Portfolio selection is a field of study that has been broached from several perspectives, including, among others, recommender systems. This paper presents SINVLIO (Semantic INVestment portfoLIO), a tool based on semantic technologies and fuzzy logic techniques that recommends investments grounded in both psychological aspects of the investor and traditional financial parameters of the investments. The results are very encouraging and reveal that SINVLIO makes good recommendations, according to the high degree of agreement between SINVLIO and expert recommendationsThis work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the projects SONAR2 (TSI-020100-2008-665) and the Spanish Ministry of Science and Innovation under the project “FINANCIAL LINKED OPEN DATA REASONING AND MANAGEMENT FOR WEB SCIENCE” (TIN2011-27405).Publicad

Application of fuzzy TOPSIS framework for selecting complex project in a case company

Purpose This study aims to propose a method known as the fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) for complex project selection in organizations. To fulfill study objectives, the factors responsible for making a project complex are collected through literature review, which is then analyzed by fuzzy TOPSIS, based on three decision-makers’ opinions. Design/methodology/approach The selection of complex projects is a multi-criteria decision-making (MCDM) process for global organizations. Traditional procedures for selecting complex projects are not adequate due to the limitations of linguistic assessment. To crossover such limitation, this study proposes the fuzzy MCDM method to select complex projects in organizations. Findings A large-scale engine manufacturing company, engaged in the energy business, is studied to validate the suitability of the fuzzy TOPSIS method and rank eight projects of the case company based on project complexity. Out of these eight projects, the closeness coefficient of the most complex project is found to be 0.817 and that of the least complex project is found to be 0.274. Finally, study outcomes are concluded in the conclusion section, along with study limitations and future works. Research limitations/implications The outcomes from this research may not be generalized sufficiently due to the subjectivity of the interviewers. The study outcomes support project managers to optimize their project selection processes, especially to select complex projects. The presented methodology can be used extensively used by the project planners/managers to find the driving factors related to project complexity. Originality/value The presented study deliberately explained how complex projects in an organization could be select efficiently. This selection methodology supports top management to maintain their proposed projects with optimum resource allocations and maximum productivity.© 2021, Ahm Shamsuzzoha, Sujan Piya and Mohammad Shamsuzzaman. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcodefi=vertaisarvioitu|en=peerReviewed

MULTI CRITERIA DECISION MAKING MODELS: AN OVERVIEW ON ELECTRE METHODS

In portfolio analysis, there are a few models that can be used. Therefore, the aim of this paper is to make an overview on multi criteria decision making models, in particular, on ELECTRE methods. We discuss the different versions of ELECTRE, which exist and why they exist. So, when speaking about ELECTRE methods structure, we have to consider two main procedures: construction of one or several outranking relation(s) procedure, and exploitation procedure. In the exploitation procedure, recommendations are elaborated from the results obtained in the first phase. The nature of the recommendation depends on the problematic: choosing, ranking or sorting. Each method is characterized by its construction and exploitation procedure. For choice problem, we can apply ELECTRE I, ELECTRE Iv, and ELECTRE IS; for ranking problem, we can apply ELECTRE II, ELECTRE III, ELECTRE IV and ELECTRE-SS; and for sorting problem we can apply ELECTRE TRI. Finally, some failings on ELECTRE methods assumptions are discussed, for instance, rank reversals. So, when analyzing portfolio management decision problem, the literature suggests AHP method and PROMETHEE family.CAPM; decision problem; multi criteria decision making models; ELECTRE family; ELECTRE rank reversals

Project portfolio resource risk assessment considering project interdependency by the fuzzy Bayesian network

Resource risk caused by specific resource sharing or competition among projects due to resource constraints is a major issue in project portfolio management, which challenges the application of risk analysis methods effectively. This paper presents a methodology by using a fuzzy Bayesian network to assess the project portfolio resource risk, determine critical resource risk factors, and propose risk-reduction strategies. In this method, the project portfolio resource risk factors are first identified by taking project interdependency into consideration, and then the Bayesian network model is developed to analyze the risk level of the identified risk factors in which expert judgments and fuzzy set theory are integrated to determine the probabilities of all risk factors to deal with incomplete risk data and information. To reduce the subjectivity of expert judgments, the expert weights are determined by combining experts’ background and reliability degree of expert judgments. A numerical analysis is used to demonstrate the application of the proposed methodology. The results show that project portfolio resource risks can be analyzed effectively and efficiently. Furthermore, “poor communication and cooperation among projects,” “capital difficulty,” and “lack of sharing technology among projects” are considered the leading factors of the project portfolio resource risk. Risk-reduction strategic decisions based on the results of risk assessment can be made, which provide project managers with a useful method or tool to manage project risks

The Effectiveness of Fuzzy-SAW Method for the Selection of New Student Admissions in Vocational High School

New student admission activities are routine activities carried out by vocational high schools every year to get the best students. A method is needed that can be used as a means of screening to select students based on their abilities and fields that are in accordance with the characteristics of students. This study uses the Fuzzy-SAW Method which is carried out through 3 stages, namely preparation of the components of the situation, the analysis and synthesis of information. The use of this method is expected to be able to help and provide the best decisions in the acceptance of new students