Exploration-Exploitation Balanced Krill Herd Algorithm for Thesis Examination Timetabling

Tawakkal, M. Iqbal; Suyanto, Suyanto

doi:10.1109/icodsa50139.2020.9212837

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…However, due to their structuring of thesis defences into sessions, it is automatically guaranteed. Concerns regarding time slot preferences also arise in Huynh et al (2012) and Tawakkal and Suyanto (2020), with the latter including an objective specifically regarding students.…”

Section: Introductionmentioning

confidence: 99%

“…There are several types of defence scheduling problems, distinguished by the number of defences that a committee is assigned to: (1) Single defence assignment -Each committee is assigned to one defence, which will take place in one slot (day, hour and room). This type of scheduling is addressed in Huynh et al (2012); Pham et al (2015); Limanto et al (2019); Tawakkal and Suyanto (2020); Christopher and Wicaksana (2021) and our work; (2) Session of defences assignment -Each committee is assigned to a group of defences (session), which will take place in one slot (day, period and room). Evidently, the time slot cannot be just a particular hour in such instances.…”

Section: Introductionmentioning

confidence: 99%

“…Several different solution approaches have been applied to the thesis defence scheduling problem, specifically: Mixed-integer linear programming (Battistutta et al, 2019); Constraint programming (Battistutta et al, 2019); Greedy backtracking hybrid algorithm (Su et al, 2020); Local search (Kochaniková and Rudová, 2013;Pham et al, 2015;Battistutta et al, 2019;Tawakkal and Suyanto, 2020); Genetic algorithm (Huynh et al, 2012;Limanto et al, 2019); and Particle swarm optimisation (Christopher and Wicaksana, 2021).…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Objective Model for Thesis Defence Scheduling

Almeida¹,

Santos²,

Figueira³

2022

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In this paper, we address the thesis defence scheduling problem, a critical academic scheduling management process, which has been overshadowed in the literature by its counterparts, course timetabling and exam scheduling. Specifically, we address the single defence assignment type of thesis defence scheduling problems, where each committee is assigned to a single defence, scheduled for a specific day, hour and room. We formulate a multi-objective mixed-integer linear programming model, which aims to be a general representation of the problem mentioned above, and that can, therefore, be applied to a broader set of cases than other models present in the literature, which have a focus on the characteristics of their universities. For such a purpose, we introduce a different decision variable, propose constraint formulations that are not regulation and policy specific, and cover and offer new takes on the more common objectives seen in the literature. We also include new objective functions based on our experience with the problem at our university and by applying knowledge from other academic scheduling problems. We also propose a two-stage solution approach. The first stage is employed to find the number of schedulable defences, enabling the optimisation of instances with unschedulable defences. The second stage is an implementation of the augmented −constraint method, which allows for the search of a set of different and non-dominated solutions while skipping redundant iterations. A novel instance generator for thesis scheduling problems is presented. Its main benefit is the generation of the availability of committee members and rooms in availability and unavailability blocks, resembling their real-world counterparts. A set of 96 randomly generated instances of varying sizes is solved and analysed regarding their relative computational performance, the number of schedulable defences and the distribution of the considered types of iterations. The proposed method can find the optimal number of schedulable defences and present non-dominated solutions within the set time limits for every tested instance.

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Section: Introductionmentioning

confidence: 99%