A fuzzy multidimensional multiple-choice knapsack model for project portfolio selection using an evolutionary algorithm

Project portfolio selection problems are inherently complex problems with multiple and often conflicting objectives. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems with precise data. Howeve...

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Published in:	Annals of operations research Vol. 206; no. 1; pp. 449 - 483
Main Authors:	Tavana, Madjid, Khalili-Damghani, Kaveh, Abtahi, Amir-Reza
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.07.2013 Springer Springer Nature B.V
Subjects:	Algorithms Analysis Business and Management Capital budgeting Case studies Combinatorics Data envelopment analysis Decision making Decision support systems Evolutionary algorithms Fuzzy Fuzzy logic Fuzzy set theory Genetic algorithms Heuristic Integer programming Knapsack problem Mathematical analysis Mathematical models Mathematical programming Methods Multidimensional scaling Operations research Operations Research/Decision Theory Optimization Preferences Production planning Scoring Studies Theory of Computation Iran Multidimensional multiple-choice knapsack Efficient epsilon-constraint Multi-objective evolutionary algorithm Data envelopment analysis
ISSN:	0254-5330, 1572-9338
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Abstract	Project portfolio selection problems are inherently complex problems with multiple and often conflicting objectives. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems with precise data. However, the project data in real-world problems are often imprecise or ambiguous. We propose a fuzzy Multidimensional Multiple-choice Knapsack Problem (MMKP) formulation for project portfolio selection. The proposed model is composed of an Efficient Epsilon-Constraint (EEC) method and a customized multi-objective evolutionary algorithm. A Data Envelopment Analysis (DEA) model is used to prune the generated solutions into a limited and manageable set of implementable alternatives. Statistical analysis is performed to investigate the effectiveness of the proposed approach in comparison with the competing methods in the literature. A case study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.
AbstractList	Project portfolio selection problems are inherently complex problems with multiple and often conflicting objectives. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems with precise data. However, the project data in real-world problems are often imprecise or ambiguous. We propose a fuzzy Multidimensional Multiple- choice Knapsack Problem (MMKP) formulation for project portfolio selection. The proposed model is composed of an Efficient Epsilon-Constraint (EEC) method and a customized multi-objective evolutionary algorithm. A Data Envelopment Analysis (DEA) model is used to prune the generated solutions into a limited and manageable set of implementable alternatives. Statistical analysis is performed to investigate the effectiveness of the proposed approach in comparison with the competing methods in the literature. A case study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms. Project portfolio selection problems are inherently complex problems with multiple and often conflicting objectives. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems with precise data. However, the project data in real-world problems are often imprecise or ambiguous. We propose a fuzzy Multidimensional Multiple- choice Knapsack Problem (MMKP) formulation for project portfolio selection. The proposed model is composed of an Efficient Epsilon-Constraint (EEC) method and a customized multi-objective evolutionary algorithm. A Data Envelopment Analysis (DEA) model is used to prune the generated solutions into a limited and manageable set of implementable alternatives. Statistical analysis is performed to investigate the effectiveness of the proposed approach in comparison with the competing methods in the literature. A case study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms. Keywords Multidimensional multiple-choice knapsack * Multi-objective evolutionary algorithm * Efficient epsilon-constraint * Data envelopment analysis Issue Title: Including Special Section: Applications of Operations Research in Educational Measurement in Memory of Ronald D. Armstrong (1945-2011) Project portfolio selection problems are inherently complex problems with multiple and often conflicting objectives. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems with precise data. However, the project data in real-world problems are often imprecise or ambiguous. We propose a fuzzy Multidimensional Multiple-choice Knapsack Problem (MMKP) formulation for project portfolio selection. The proposed model is composed of an Efficient Epsilon-Constraint (EEC) method and a customized multi-objective evolutionary algorithm. A Data Envelopment Analysis (DEA) model is used to prune the generated solutions into a limited and manageable set of implementable alternatives. Statistical analysis is performed to investigate the effectiveness of the proposed approach in comparison with the competing methods in the literature. A case study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.[PUBLICATION ABSTRACT]
Audience	Academic
Author	Khalili-Damghani, Kaveh Tavana, Madjid Abtahi, Amir-Reza
Author_xml	– sequence: 1 givenname: Madjid surname: Tavana fullname: Tavana, Madjid email: tavana@lasalle.edu organization: Business Systems and Analytics, Lindback Distinguished Chair of Information Systems and Decision Sciences, La Salle University – sequence: 2 givenname: Kaveh surname: Khalili-Damghani fullname: Khalili-Damghani, Kaveh organization: Department of Industrial Engineering, Islamic Azad University South-Tehran Branch – sequence: 3 givenname: Amir-Reza surname: Abtahi fullname: Abtahi, Amir-Reza organization: Department of Knowledge Engineering and Decision Sciences, Faculty of Economic Institutions Management, University of Economic Sciences
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Snippet	Project portfolio selection problems are inherently complex problems with multiple and often conflicting objectives. Numerous analytical techniques ranging... Issue Title: Including Special Section: Applications of Operations Research in Educational Measurement in Memory of Ronald D. Armstrong (1945-2011) Project...
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SubjectTerms	Algorithms Analysis Business and Management Capital budgeting Case studies Combinatorics Data envelopment analysis Decision making Decision support systems Evolutionary algorithms Fuzzy Fuzzy logic Fuzzy set theory Genetic algorithms Heuristic Integer programming Knapsack problem Mathematical analysis Mathematical models Mathematical programming Methods Multidimensional scaling Operations research Operations Research/Decision Theory Optimization Preferences Production planning Scoring Studies Theory of Computation
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Title	A fuzzy multidimensional multiple-choice knapsack model for project portfolio selection using an evolutionary algorithm
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