Effective Evolutionary Algorithm for Solving the Real-Resource-Constrained Scheduling Problem
This paper defines and introduces the formulation of the Real-RCPSP (Real-Resource-Constrained Project Scheduling Problem), a new variant of the MS-RCPSP (Multiskill Resource-Constrained Project Scheduling Problem). Real-RCPSP is an optimization problem that has been attracting widespread interest f...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8897710 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546987374804992 |
|---|---|
| author | Huu Dang Quoc Loc Nguyen The Cuong Nguyen Doan Naixue Xiong |
| author_facet | Huu Dang Quoc Loc Nguyen The Cuong Nguyen Doan Naixue Xiong |
| author_sort | Huu Dang Quoc |
| collection | DOAJ |
| description | This paper defines and introduces the formulation of the Real-RCPSP (Real-Resource-Constrained Project Scheduling Problem), a new variant of the MS-RCPSP (Multiskill Resource-Constrained Project Scheduling Problem). Real-RCPSP is an optimization problem that has been attracting widespread interest from the research community in recent years. Real-RCPSP has become a critical issue in many fields such as resource allocation to perform tasks in Edge Computing or arranging robots at industrial production lines at factories and IoT systems. Compared to the MS-RCPSP, the Real-RCPSP is supplemented with assumptions about the execution time of the task, so it is more realistic. The previous algorithms for solving the MS-RCPSP have only been verified on simulation data, so their results are not completely convincing. In addition, those algorithms are designed only to solve the MS-RCPSP, so they are not completely suitable for solving the new Real-RCPSP. Inspired by the Cuckoo Search approach, this literature proposes an evolutionary algorithm that uses the function Reallocate for fast convergence to the global extremum. In order to verify the proposed algorithm, the experiments were conducted on two datasets: (i) the iMOPSE simulation dataset that previous studies had used and (ii) the actual TNG dataset collected from the textile company TNG. Experimental results on the iMOPSE simulation dataset show that the proposed algorithm achieves better solution quality than the existing algorithms, while the experimental results on the TNG dataset have proved that the proposed algorithm decreases the execution time of current production lines at the TNG company. |
| format | Article |
| id | doaj-art-fe3a2a04d8b942919e22d1001558421e |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-fe3a2a04d8b942919e22d1001558421e2025-02-03T06:46:33ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88977108897710Effective Evolutionary Algorithm for Solving the Real-Resource-Constrained Scheduling ProblemHuu Dang Quoc0Loc Nguyen The1Cuong Nguyen Doan2Naixue Xiong3Thuong Mai University, Hanoi, VietnamHanoi National University of Education, Hanoi, VietnamMilitary Institute of Science and Technology, Hanoi, VietnamNortheastern State University, Tahlequah, OK, USAThis paper defines and introduces the formulation of the Real-RCPSP (Real-Resource-Constrained Project Scheduling Problem), a new variant of the MS-RCPSP (Multiskill Resource-Constrained Project Scheduling Problem). Real-RCPSP is an optimization problem that has been attracting widespread interest from the research community in recent years. Real-RCPSP has become a critical issue in many fields such as resource allocation to perform tasks in Edge Computing or arranging robots at industrial production lines at factories and IoT systems. Compared to the MS-RCPSP, the Real-RCPSP is supplemented with assumptions about the execution time of the task, so it is more realistic. The previous algorithms for solving the MS-RCPSP have only been verified on simulation data, so their results are not completely convincing. In addition, those algorithms are designed only to solve the MS-RCPSP, so they are not completely suitable for solving the new Real-RCPSP. Inspired by the Cuckoo Search approach, this literature proposes an evolutionary algorithm that uses the function Reallocate for fast convergence to the global extremum. In order to verify the proposed algorithm, the experiments were conducted on two datasets: (i) the iMOPSE simulation dataset that previous studies had used and (ii) the actual TNG dataset collected from the textile company TNG. Experimental results on the iMOPSE simulation dataset show that the proposed algorithm achieves better solution quality than the existing algorithms, while the experimental results on the TNG dataset have proved that the proposed algorithm decreases the execution time of current production lines at the TNG company.http://dx.doi.org/10.1155/2020/8897710 |
| spellingShingle | Huu Dang Quoc Loc Nguyen The Cuong Nguyen Doan Naixue Xiong Effective Evolutionary Algorithm for Solving the Real-Resource-Constrained Scheduling Problem Journal of Advanced Transportation |
| title | Effective Evolutionary Algorithm for Solving the Real-Resource-Constrained Scheduling Problem |
| title_full | Effective Evolutionary Algorithm for Solving the Real-Resource-Constrained Scheduling Problem |
| title_fullStr | Effective Evolutionary Algorithm for Solving the Real-Resource-Constrained Scheduling Problem |
| title_full_unstemmed | Effective Evolutionary Algorithm for Solving the Real-Resource-Constrained Scheduling Problem |
| title_short | Effective Evolutionary Algorithm for Solving the Real-Resource-Constrained Scheduling Problem |
| title_sort | effective evolutionary algorithm for solving the real resource constrained scheduling problem |
| url | http://dx.doi.org/10.1155/2020/8897710 |
| work_keys_str_mv | AT huudangquoc effectiveevolutionaryalgorithmforsolvingtherealresourceconstrainedschedulingproblem AT locnguyenthe effectiveevolutionaryalgorithmforsolvingtherealresourceconstrainedschedulingproblem AT cuongnguyendoan effectiveevolutionaryalgorithmforsolvingtherealresourceconstrainedschedulingproblem AT naixuexiong effectiveevolutionaryalgorithmforsolvingtherealresourceconstrainedschedulingproblem |