Automatic Examination Timetable Scheduling Using Particle Swarm Optimization and Local Search Algorithm
Examination timetable scheduling is a serious challenge in every University system with concerns on assigning examinations to venues over a period of time. Major challenges facing examination scheduling includes: having student’s population to be much mor
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1 Introduction Educational timetabling challenges include school timetabling, examination timetabling and university course. Basically, university timetable problem exists in two forms, viz course and examination timetable formats [1]. Examination scheduling or timetabling is one of the most difficult problems faced by academic community [2]. Timetabling challenge is defined as assigning examinations into a fixed number of timeslots hereby satisfying all university constraints [3]. However, for every institution the objective is to construct an effective and satisfactory examination timetable. An institutional timetable is said to be effective when it keep its users satisfied to a reasonable extent. The main challenge of examination timetable scheduling is the population of students when compared to the inadequate available resources within a short period of time; hence, the population of students will always be more than available resources [4]. The problem addressed here deals with the assignment of examinations into a limited number of timeslots with respect to certain constraints for the undergraduate O. Abayomi-Alli (B) · S. Misra Center of ICT/ICE Research, CUCRID Building, Covenant University, Ota, Nigeria e-mail: [email protected] S. Misra e-mail: [email protected] A. Abayomi-Alli Department of Computer Science, Federal University of Agriculture, Abeokuta, Nigeria e-mail: [email protected] R. Damasevicius · R. Maskeliunas Kaunas University of Technology, Kaunas, Lithuania e-mail: [email protected] R. Maskeliunas e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2019 R. K. Shukla et al. (eds.), Data, Engineering and Applications, https://doi.org/10.1007/978-981-13-6347-4_11
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students with about a population of 15,000 from the Federal University of Agriculture, Abeokuta, Nigeria. There are two major types of constraints to be satisfied when dealing with a university examination timetabling which are: Hard and Soft constraints. Hard constraints are conditions that must be conceited while soft constraints may not be conceited, but it is desirable to have a good and feasible timetable. Hard constraints considered in this study was adopted from [5]: (i) every examination should be assigned to a venue at a particular timeslot; (ii) the scheduled examinations must not exceed the venue capacity; (iii) the maximum number of time period within a day should not be exceeded. Soft constraint include: (i) student preference for having an examination a day; (ii) finalist preference for having their examinations scheduled for first to second week of the examination. Satisfying all the constraints to have a good timetable is becoming increasingly difficult. The motivation of this study is the complexity of satisfying the constraints in examination timetable which includes high time consumption and manpower, thereby being too stressful. However, the major objective of this study is to design an algorith
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