An integrated project portfolio selection and resource investment problem to maximize net present value using genetic algorithm
In this paper, a mathematical model is proposed for project portfolioselection and resource availability cost problem to scheduling activities inorder to maximize net present value of the selected projects preservingprecedence and resource constraints. Since the developed model belongs toNP-hard pro...
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| Format: | Article |
| Language: | fas |
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Allameh Tabataba'i University Press
2016-09-01
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| Series: | Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī |
| Subjects: | |
| Online Access: | https://jims.atu.ac.ir/article_5708_138941f349eb14201e3220c677861c1d.pdf |
| _version_ | 1827391916710297600 |
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| author | Hamidreza Shahabifard Behrouz Afshar-nadjafi |
| author_facet | Hamidreza Shahabifard Behrouz Afshar-nadjafi |
| author_sort | Hamidreza Shahabifard |
| collection | DOAJ |
| description | In this paper, a mathematical model is proposed for project portfolioselection and resource availability cost problem to scheduling activities inorder to maximize net present value of the selected projects preservingprecedence and resource constraints. Since the developed model belongs toNP-hard problems list, so a genetic based meta-heuristic algorithm isproposed to tackle the developed model. In the proposed algorithm besidecommon operators of genetic algorithms such as crossover & mutation, someintelligent operators are utilized for local search in computed resources andshifting the activities with negative cash flows. The key parameters of thealgorithm are calibrated using Taguchi method to accelerate convergence ofthe proposed algorithm. Then, the algorithm is used to solve 90 testproblems consisting 30 small-scale, 30 middle-scale and 30 large scaleproblems to examine the algorithm’s performance. It is observed that, insmall problems, the obtained solutions from the proposed genetic algorithmhave been comparably better than the local optimum solutions stemmedfrom Lingo software. On the other hand, for the middle and large sizeproblems which there is no local optimum available within the limited CPUtime, robustness of the proposed algorithm is appropriate |
| first_indexed | 2024-03-08T17:22:19Z |
| format | Article |
| id | doaj.art-fb04ddda56714e809191dfae59a2fb07 |
| institution | Directory Open Access Journal |
| issn | 2251-8029 2476-602X |
| language | fas |
| last_indexed | 2024-03-08T17:22:19Z |
| publishDate | 2016-09-01 |
| publisher | Allameh Tabataba'i University Press |
| record_format | Article |
| series | Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī |
| spelling | doaj.art-fb04ddda56714e809191dfae59a2fb072024-01-03T04:44:34ZfasAllameh Tabataba'i University PressMuṭāli̒āt-i Mudīriyyat-i Ṣan̒atī2251-80292476-602X2016-09-0114426112110.22054/jims.2016.57085708An integrated project portfolio selection and resource investment problem to maximize net present value using genetic algorithmHamidreza Shahabifard0Behrouz Afshar-nadjafi1کارشناسی ارشد دانشگاه علوم و تحقیقات دانشگاه آزاد اسلامیدانشیار دانشکده مهندسی صنایع و مکانیک دانشگاه ازاد اسلامی قزوینIn this paper, a mathematical model is proposed for project portfolioselection and resource availability cost problem to scheduling activities inorder to maximize net present value of the selected projects preservingprecedence and resource constraints. Since the developed model belongs toNP-hard problems list, so a genetic based meta-heuristic algorithm isproposed to tackle the developed model. In the proposed algorithm besidecommon operators of genetic algorithms such as crossover & mutation, someintelligent operators are utilized for local search in computed resources andshifting the activities with negative cash flows. The key parameters of thealgorithm are calibrated using Taguchi method to accelerate convergence ofthe proposed algorithm. Then, the algorithm is used to solve 90 testproblems consisting 30 small-scale, 30 middle-scale and 30 large scaleproblems to examine the algorithm’s performance. It is observed that, insmall problems, the obtained solutions from the proposed genetic algorithmhave been comparably better than the local optimum solutions stemmedfrom Lingo software. On the other hand, for the middle and large sizeproblems which there is no local optimum available within the limited CPUtime, robustness of the proposed algorithm is appropriatehttps://jims.atu.ac.ir/article_5708_138941f349eb14201e3220c677861c1d.pdfproject portfolio selectionproject schedulinggenetic algorithmresource investmentnet present value |
| spellingShingle | Hamidreza Shahabifard Behrouz Afshar-nadjafi An integrated project portfolio selection and resource investment problem to maximize net present value using genetic algorithm Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī project portfolio selection project scheduling genetic algorithm resource investment net present value |
| title | An integrated project portfolio selection and resource
investment problem to maximize net present value using
genetic algorithm |
| title_full | An integrated project portfolio selection and resource
investment problem to maximize net present value using
genetic algorithm |
| title_fullStr | An integrated project portfolio selection and resource
investment problem to maximize net present value using
genetic algorithm |
| title_full_unstemmed | An integrated project portfolio selection and resource
investment problem to maximize net present value using
genetic algorithm |
| title_short | An integrated project portfolio selection and resource
investment problem to maximize net present value using
genetic algorithm |
| title_sort | integrated project portfolio selection and resource investment problem to maximize net present value using genetic algorithm |
| topic | project portfolio selection project scheduling genetic algorithm resource investment net present value |
| url | https://jims.atu.ac.ir/article_5708_138941f349eb14201e3220c677861c1d.pdf |
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