Automated generation of dispatching rules for the green unrelated machines scheduling problem
Abstract The concept of green scheduling, which deals with the environmental impact of the scheduling process, is becoming increasingly important due to growing environmental concerns. Most green scheduling problem variants focus on modelling the energy consumption during the execution of the schedu...
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01677-9 |
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| author | Nikolina Frid Marko Ɖurasević Francisco Javier Gil-Gala |
| author_facet | Nikolina Frid Marko Ɖurasević Francisco Javier Gil-Gala |
| author_sort | Nikolina Frid |
| collection | DOAJ |
| description | Abstract The concept of green scheduling, which deals with the environmental impact of the scheduling process, is becoming increasingly important due to growing environmental concerns. Most green scheduling problem variants focus on modelling the energy consumption during the execution of the schedule. However, the dynamic unrelated machines environment is rarely considered, mainly because it is difficult to manually design simple heuristics, called dispatching rules (DRs), which are suitable for solving dynamic, non-standard scheduling problems. Using hyperheuristics, especially genetic programming (GP), alleviates the problem since it enables the automatic design of new DRs. In this study, we apply GP to automatically design DRs for solving the green scheduling problem in the unrelated machines environment under dynamic conditions. The total energy consumed during the system execution is optimised along with two standard scheduling criteria. The three most commonly investigated green scheduling problem variants from the literature are selected, and GP is adapted to generate appropriate DRs for each. The experiments show that GP-generated DRs efficiently solve the problem under dynamic conditions, providing a trade-off between optimising standard and energy-related criteria. |
| format | Article |
| id | doaj-art-8af0e9b6ac9548aeb5d4890000dae46d |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-8af0e9b6ac9548aeb5d4890000dae46d2025-02-02T12:50:09ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111112210.1007/s40747-024-01677-9Automated generation of dispatching rules for the green unrelated machines scheduling problemNikolina Frid0Marko Ɖurasević1Francisco Javier Gil-Gala2Department of Electronics, Microelectronics, Computer and Intelligent Systems, University of Zagreb Faculty of Electrical Engineering and ComputingDepartment of Electronics, Microelectronics, Computer and Intelligent Systems, University of Zagreb Faculty of Electrical Engineering and ComputingDepartment of Computer Science, University of OviedoAbstract The concept of green scheduling, which deals with the environmental impact of the scheduling process, is becoming increasingly important due to growing environmental concerns. Most green scheduling problem variants focus on modelling the energy consumption during the execution of the schedule. However, the dynamic unrelated machines environment is rarely considered, mainly because it is difficult to manually design simple heuristics, called dispatching rules (DRs), which are suitable for solving dynamic, non-standard scheduling problems. Using hyperheuristics, especially genetic programming (GP), alleviates the problem since it enables the automatic design of new DRs. In this study, we apply GP to automatically design DRs for solving the green scheduling problem in the unrelated machines environment under dynamic conditions. The total energy consumed during the system execution is optimised along with two standard scheduling criteria. The three most commonly investigated green scheduling problem variants from the literature are selected, and GP is adapted to generate appropriate DRs for each. The experiments show that GP-generated DRs efficiently solve the problem under dynamic conditions, providing a trade-off between optimising standard and energy-related criteria.https://doi.org/10.1007/s40747-024-01677-9Genetic programmingGreen schedulingUnrelated machines environmentDispatching rulesHyperheuristics |
| spellingShingle | Nikolina Frid Marko Ɖurasević Francisco Javier Gil-Gala Automated generation of dispatching rules for the green unrelated machines scheduling problem Complex & Intelligent Systems Genetic programming Green scheduling Unrelated machines environment Dispatching rules Hyperheuristics |
| title | Automated generation of dispatching rules for the green unrelated machines scheduling problem |
| title_full | Automated generation of dispatching rules for the green unrelated machines scheduling problem |
| title_fullStr | Automated generation of dispatching rules for the green unrelated machines scheduling problem |
| title_full_unstemmed | Automated generation of dispatching rules for the green unrelated machines scheduling problem |
| title_short | Automated generation of dispatching rules for the green unrelated machines scheduling problem |
| title_sort | automated generation of dispatching rules for the green unrelated machines scheduling problem |
| topic | Genetic programming Green scheduling Unrelated machines environment Dispatching rules Hyperheuristics |
| url | https://doi.org/10.1007/s40747-024-01677-9 |
| work_keys_str_mv | AT nikolinafrid automatedgenerationofdispatchingrulesforthegreenunrelatedmachinesschedulingproblem AT markoɖurasevic automatedgenerationofdispatchingrulesforthegreenunrelatedmachinesschedulingproblem AT franciscojaviergilgala automatedgenerationofdispatchingrulesforthegreenunrelatedmachinesschedulingproblem |