Multi-objective binary cuckoo search for constrained project portfolio selection under uncertainty
| Autor: | Mohammed M. S. El-Kholany, Hisham M. Abdelsalam |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Operations research
Computer science 05 social sciences Monte Carlo method Binary number Industrial and Manufacturing Engineering Set (abstract data type) Microeconomics Order (exchange) 0502 economics and business Portfolio 050211 marketing Project portfolio management Cuckoo search 050203 business & management Selection (genetic algorithm) |
| Zdroj: | European J. of Industrial Engineering. 11:818 |
| ISSN: | 1751-5262 1751-5254 |
| DOI: | 10.1504/ejie.2017.089107 |
| Popis: | One of the recurrent complex decisions faced by organisations is project portfolio selection (PPS) in which a group of the most beneficial projects must be selected from a set of candidate projects. Accordingly, effective means for selecting projects must be employed in order for the organisation to survive in today's extremely competitive business environments. This paper proposes a framework that integrates a multi-objective binary cuckoo search (MOCS) algorithm with Monte Carlo simulation to help decision makers select the best portfolio based on a given set of parameters (criteria and performance values) and several characteristics (uncertainty, constraints and multi-objective). Performance of the proposed framework was measured against a published research on two cases: 1) when transforming all objectives into one, the proposed algorithm outperformed recent algorithms used to solve the problem on a large-scale where these algorithms fail to reach an optimal solution; 2) when dealing with a multi-objective case, the proposed algorithm outperformed reported earlier results with 90% of the non-dominated solutions were obtained by it. [Received 30 September 2016; Revised 29 January 2017; Revised 24 May 2017; Revised 11 August 2017; Accepted 25 August 2017] |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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