Course time scheduling problem for distance education considering server load balancing: a case of an engineering faculty.
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| Title: | Course time scheduling problem for distance education considering server load balancing: a case of an engineering faculty. |
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| Authors: | Alakaş, Hacı Mehmet1 (AUTHOR) hmalagas@kku.edu.tr, Pınarbaşı, Mehmet1 (AUTHOR), Sarımehmet, Bedirhan1 (AUTHOR), Eren, Tamer1 (AUTHOR) |
| Source: | Neural Computing & Applications. Mar2025, Vol. 37 Issue 7, p5635-5653. 19p. |
| Subjects: | COVID-19 pandemic, Internet servers, Scheduling, Polynomial time algorithms, Distance education |
| Abstract: | During the Covid-19 pandemic and mass disaster, universities have had to continue their courses with distance education. Internet servers in educational institutions slowed down for some periods, and courses could not be processed. The interruptions are due to the intensity of users on the servers and the internet congestion in the country during some time periods. For these reasons, the course time scheduling problem for distance education is discussed in this study. This study aims to balance the number of users in the system to prevent internet congestion. To solve the problem, two different mathematical models are developed. The first model obtains the balanced course scheduling for any time period. The second model is provided by the course assignment, considering internet congestion that occurred during any period. The proposed models were tested in a real case study dealing with the charting of courses offered in all departments of the engineering faculty of a university in Turkey. A problem-specific heuristic model is developed to solve the problem since a solution could not be obtained from the mathematical models in polynomial time due to the large size of the actual data set. The comparative results obtained from mathematical models and problem-specific heuristics are reported, and their performance is discussed. According to the comparative results, problem-specific heuristic outperforms mathematical models in obtaining a balanced schedule and solution time. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 183372674 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Course time scheduling problem for distance education considering server load balancing: a case of an engineering faculty. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alakaş%2C+Hacı+Mehmet%22">Alakaş, Hacı Mehmet</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hmalagas@kku.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Pınarbaşı%2C+Mehmet%22">Pınarbaşı, Mehmet</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sarımehmet%2C+Bedirhan%22">Sarımehmet, Bedirhan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Eren%2C+Tamer%22">Eren, Tamer</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Mar2025, Vol. 37 Issue 7, p5635-5653. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22COVID-19+pandemic%22">COVID-19 pandemic</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+servers%22">Internet servers</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Polynomial+time+algorithms%22">Polynomial time algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Distance+education%22">Distance education</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: During the Covid-19 pandemic and mass disaster, universities have had to continue their courses with distance education. Internet servers in educational institutions slowed down for some periods, and courses could not be processed. The interruptions are due to the intensity of users on the servers and the internet congestion in the country during some time periods. For these reasons, the course time scheduling problem for distance education is discussed in this study. This study aims to balance the number of users in the system to prevent internet congestion. To solve the problem, two different mathematical models are developed. The first model obtains the balanced course scheduling for any time period. The second model is provided by the course assignment, considering internet congestion that occurred during any period. The proposed models were tested in a real case study dealing with the charting of courses offered in all departments of the engineering faculty of a university in Turkey. A problem-specific heuristic model is developed to solve the problem since a solution could not be obtained from the mathematical models in polynomial time due to the large size of the actual data set. The comparative results obtained from mathematical models and problem-specific heuristics are reported, and their performance is discussed. According to the comparative results, problem-specific heuristic outperforms mathematical models in obtaining a balanced schedule and solution time. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-024-10941-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 5635 Subjects: – SubjectFull: COVID-19 pandemic Type: general – SubjectFull: Internet servers Type: general – SubjectFull: Scheduling Type: general – SubjectFull: Polynomial time algorithms Type: general – SubjectFull: Distance education Type: general Titles: – TitleFull: Course time scheduling problem for distance education considering server load balancing: a case of an engineering faculty. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alakaş, Hacı Mehmet – PersonEntity: Name: NameFull: Pınarbaşı, Mehmet – PersonEntity: Name: NameFull: Sarımehmet, Bedirhan – PersonEntity: Name: NameFull: Eren, Tamer IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 37 – Type: issue Value: 7 Titles: – TitleFull: Neural Computing & Applications Type: main |
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