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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Vydané v:Annals of operations research Ročník 206; číslo 1; s. 449 - 483
Hlavní autori: Tavana, Madjid, Khalili-Damghani, Kaveh, Abtahi, Amir-Reza
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Boston Springer US 01.07.2013
Springer
Springer Nature B.V
Predmet:
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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Popis
Shrnutí: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.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-013-1387-3