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

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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. Copyright Springer Science+Business Media New York 2013

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  • Madjid Tavana & Kaveh Khalili-Damghani & Amir-Reza Abtahi, 2013. "A fuzzy multidimensional multiple-choice knapsack model for project portfolio selection using an evolutionary algorithm," Annals of Operations Research, Springer, vol. 206(1), pages 449-483, July.
    • Handle: RePEc:spr:annopr:v:206:y:2013:i:1:p:449-483:10.1007/s10479-013-1387-3
    DOI: 10.1007/s10479-013-1387-3
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    1. Mavrotas, George & Diakoulaki, Danae & Kourentzis, Athanasios, 2008. "Selection among ranked projects under segmentation, policy and logical constraints," European Journal of Operational Research, Elsevier, vol. 187(1), pages 177-192, May.
    2. Shah, Ruchit & Reed, Patrick, 2011. "Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems," European Journal of Operational Research, Elsevier, vol. 211(3), pages 466-479, June.
    3. Lin, Feng-Tse, 2008. "Solving the knapsack problem with imprecise weight coefficients using genetic algorithms," European Journal of Operational Research, Elsevier, vol. 185(1), pages 133-145, February.
    4. Hatami-Marbini, Adel & Emrouznejad, Ali & Tavana, Madjid, 2011. "A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making," European Journal of Operational Research, Elsevier, vol. 214(3), pages 457-472, November.
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    6. Lin, Feng-Tse & Yao, Jing-Shing, 2001. "Using fuzzy numbers in knapsack problems," European Journal of Operational Research, Elsevier, vol. 135(1), pages 158-176, November.
    7. Florios, Kostas & Mavrotas, George & Diakoulaki, Danae, 2010. "Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 203(1), pages 14-21, May.
    8. Cook, Wade D. & Green, Rodney H., 2000. "Project prioritization: a resource-constrained data envelopment analysis approach," Socio-Economic Planning Sciences, Elsevier, vol. 34(2), pages 85-99, June.
    9. Eilat, Harel & Golany, Boaz & Shtub, Avraham, 2008. "R&D project evaluation: An integrated DEA and balanced scorecard approach," Omega, Elsevier, vol. 36(5), pages 895-912, October.
    10. Khalili-Damghani, Kaveh & Abtahi, Amir-Reza & Tavana, Madjid, 2013. "A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 58-75.
    11. Eilat, Harel & Golany, Boaz & Shtub, Avraham, 2006. "Constructing and evaluating balanced portfolios of R&D projects with interactions: A DEA based methodology," European Journal of Operational Research, Elsevier, vol. 172(3), pages 1018-1039, August.
    12. Kao, Chiang & Liu, Shiang-Tai, 2003. "A mathematical programming approach to fuzzy efficiency ranking," International Journal of Production Economics, Elsevier, vol. 86(2), pages 145-154, November.
    13. Charnes, A. & Cooper, W. W. & Seiford, L. & Stutz, J., 1982. "A multiplicative model for efficiency analysis," Socio-Economic Planning Sciences, Elsevier, vol. 16(5), pages 223-224.
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    Cited by:

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    2. Navid Zarbakhshnia & Devika Kannan & Reza Kiani Mavi & Hamed Soleimani, 2020. "A novel sustainable multi-objective optimization model for forward and reverse logistics system under demand uncertainty," Annals of Operations Research, Springer, vol. 295(2), pages 843-880, December.
    3. Barbati, Maria & Greco, Salvatore & Kadziński, Miłosz & Słowiński, Roman, 2018. "Optimization of multiple satisfaction levels in portfolio decision analysis," Omega, Elsevier, vol. 78(C), pages 192-204.
    4. A. Mohammed, 2020. "Towards a sustainable assessment of suppliers: an integrated fuzzy TOPSIS-possibilistic multi-objective approach," Annals of Operations Research, Springer, vol. 293(2), pages 639-668, October.

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