A Memetic Algorithm for the Solution of the Resource Leveling Problem
In this paper, we present a novel memetic algorithm (MA) for the solution of the resource leveling problem (RLP). The evolutionary framework of the MA is based on integration of a genetic algorithm and simulated annealing methods along with a resource leveling heuristic. The main objective of the pr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/11/2738 |
| _version_ | 1797459902605557760 |
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| author | Mehdi Iranagh Rifat Sonmez Tankut Atan Furkan Uysal Önder Halis Bettemir |
| author_facet | Mehdi Iranagh Rifat Sonmez Tankut Atan Furkan Uysal Önder Halis Bettemir |
| author_sort | Mehdi Iranagh |
| collection | DOAJ |
| description | In this paper, we present a novel memetic algorithm (MA) for the solution of the resource leveling problem (RLP). The evolutionary framework of the MA is based on integration of a genetic algorithm and simulated annealing methods along with a resource leveling heuristic. The main objective of the proposed algorithm is to integrate complementary strengths of different optimization methods and incorporate the individual learning as a separate process for achieving a successful optimization method for the RLP. The performance of the MA is compared with the state-of-the-art leveling methods. For small instances up to 30 activities, mixed-integer linear models are presented for two leveling metrics to provide a basis for performance evaluation. The computational results indicate that the new integrated framework of the MA outperforms the state-of-the-art leveling heuristics and meta-heuristics and provides a successful method for the RLP. The limitations of popular commercial project management software are also illustrated along with the improvements achieved by the MA to reveal potential contributions of the proposed integrated framework in practice. |
| first_indexed | 2024-03-09T16:57:33Z |
| format | Article |
| id | doaj.art-4e234e88c14e4895861ef7883457fc73 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T16:57:33Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-4e234e88c14e4895861ef7883457fc732023-11-24T14:33:11ZengMDPI AGBuildings2075-53092023-10-011311273810.3390/buildings13112738A Memetic Algorithm for the Solution of the Resource Leveling ProblemMehdi Iranagh0Rifat Sonmez1Tankut Atan2Furkan Uysal3Önder Halis Bettemir4Transit & Rail Company, Toronto, ON 43964, CanadaCivil Engineering Department, Middle East Technical University, Ankara 06531, TürkiyeDepartment of Industrial Engineering, Bahçeşehir University, Istanbul 34353, TürkiyeCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitDepartment of Civil Engineering, İnönü University, Malatya 44280, TürkiyeIn this paper, we present a novel memetic algorithm (MA) for the solution of the resource leveling problem (RLP). The evolutionary framework of the MA is based on integration of a genetic algorithm and simulated annealing methods along with a resource leveling heuristic. The main objective of the proposed algorithm is to integrate complementary strengths of different optimization methods and incorporate the individual learning as a separate process for achieving a successful optimization method for the RLP. The performance of the MA is compared with the state-of-the-art leveling methods. For small instances up to 30 activities, mixed-integer linear models are presented for two leveling metrics to provide a basis for performance evaluation. The computational results indicate that the new integrated framework of the MA outperforms the state-of-the-art leveling heuristics and meta-heuristics and provides a successful method for the RLP. The limitations of popular commercial project management software are also illustrated along with the improvements achieved by the MA to reveal potential contributions of the proposed integrated framework in practice.https://www.mdpi.com/2075-5309/13/11/2738resource levelingproject schedulingoptimizationgenetic algorithmssimulated annealingmemetic algorithms |
| spellingShingle | Mehdi Iranagh Rifat Sonmez Tankut Atan Furkan Uysal Önder Halis Bettemir A Memetic Algorithm for the Solution of the Resource Leveling Problem Buildings resource leveling project scheduling optimization genetic algorithms simulated annealing memetic algorithms |
| title | A Memetic Algorithm for the Solution of the Resource Leveling Problem |
| title_full | A Memetic Algorithm for the Solution of the Resource Leveling Problem |
| title_fullStr | A Memetic Algorithm for the Solution of the Resource Leveling Problem |
| title_full_unstemmed | A Memetic Algorithm for the Solution of the Resource Leveling Problem |
| title_short | A Memetic Algorithm for the Solution of the Resource Leveling Problem |
| title_sort | memetic algorithm for the solution of the resource leveling problem |
| topic | resource leveling project scheduling optimization genetic algorithms simulated annealing memetic algorithms |
| url | https://www.mdpi.com/2075-5309/13/11/2738 |
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