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Incremental Software Development Model for Solving Exam Scheduling Problems

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Soft Computing in Data Science (SCDS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 937))

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Abstract

Examination scheduling is a challenging and time consuming activity among academic administrators of colleges and universities. This is because it involves scheduling a set of exams within a limited number of timeslots, assigning invigilators for each exam and satisfying a set of defined constraints. Scheduling is done to avoid cases in which students sit for more than one exam at the same time or invigilators invigilate more than one exam in different examination venue at the same time or the exams set exceeded the venue capacity. To overcome these challenges, we developed an incremental software model based on greedy algorithm to structure, plan and control the process of an automated schedule construction. Incremental development model using greedy algorithm (IMGA) is used to prioritize the hard and soft constraints and optimize exam scheduling problems. IMGA assigns exams to resources (e.g.: time periods and venues) based on a number of rules. When rules defined are not applicable to the current partial solution, a backtracking is executed in order to find a solution which satisfies all constraints. These processes are done through adaptation of greedy algorithm. Our algorithm iteratively makes one choice after another in order to minimize the conflicts that may have arisen. The advantage of IMGA is that it provides clear-cut solutions to smaller instances of a problem and hence, makes the problem easier to be understood.

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Acknowledgements

The authors are grateful to the Research Management Centre (RMC) UiTM for the support under the national Fundamental Research Grant Scheme (600-IRMI/FRGS 5/3).

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Correspondence to Maryam Khanian Najafabadi .

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Najafabadi, M.K., Mohamed, A. (2019). Incremental Software Development Model for Solving Exam Scheduling Problems. In: Yap, B., Mohamed, A., Berry, M. (eds) Soft Computing in Data Science. SCDS 2018. Communications in Computer and Information Science, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-3441-2_17

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  • DOI: https://doi.org/10.1007/978-981-13-3441-2_17

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  • Print ISBN: 978-981-13-3440-5

  • Online ISBN: 978-981-13-3441-2

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