A survey of the state-of-the-art of optimisation methodologies in school timetabling problems

Tan, Joo Siang; Goh, Say Leng; Kendall, Graham; Sabar, Nasser R.

doi:10.1016/j.eswa.2020.113943

Cited by 59 publications

(31 citation statements)

References 50 publications

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“…The timetabling problem is a complex problem experienced by a university or other education institutions. According to [5] in the timetabling issue, it consists of at least four parameters, i.e., limited time, limited resources, number of meetings, and limited number of constraints. Many combinations of these parameters result in timetabling, including university timetabling, an NP-hard that will take a long time to solve manually [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Bibliometric Analysis of University Timetabling Using Publish and Perish

Trenggonowati¹,

Herlina²,

Febianti³

et al. 2022

Proceedings of the Conference on Broad Exposure to Science and Technology 2021 (BEST 2021)

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The University Course Timetabling Problem (UCTP) is rated as the main enemy by the University. UCTP aims to allocate academic courses to lecturers, the division of time, and classrooms that must carry out regularly. So that learning activities run well and smoothly until achieving an increase in the quality of education. This study recommends bibliometric analysis for this problem. Doing this is necessary to see trends or research gaps that are still possible. Search data using Publish and perish software, while data analysis using VOSviewer software. The search and data analysis results show that trend for the university timetabling problem is more likely to be solved by metaheuristic models such as genetic algorithms and simulated annealing.

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Section: Literature Reviewmentioning

confidence: 99%

Bibliometric Analysis of University Timetabling Using Publish and Perish

Trenggonowati¹,

Herlina²,

Febianti³

et al. 2022

Proceedings of the Conference on Broad Exposure to Science and Technology 2021 (BEST 2021)

View full text Add to dashboard Cite

show abstract

“…In 2021, Tan, Goh, Kendall, and Sabar updated the Schaerf survey. They managed to identify 22 scheduling constraints and 14 methods to solve scheduling problems [13]. The methods used to automate timetabling are presented in Table 1.…”

Section: Schedulingmentioning

confidence: 99%

Business Process Models Complexity Growth Analysis based on Scheduling Generated by Timetabling of Courses

2021

IJIES

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“…In contrast with the most commonly studied academic scheduling problems, specifically exam scheduling and course timetabling (Chaudhuri and De, 2010); for a state-of-the-art review of those, we refer the reader to Babaei et al (2015); Tan et al (2021); and for how inefficient and time-consuming assigning and scheduling committees is for the unfortunate people to whom such tasks are delegated, thesis defence scheduling is remarkably underrepresented in the literature, which still holds several noticeable gaps, with this work being an attempt to fill some of them. In particular, we propose a multi-objective mixed-integer linear programming model, which, to the best of our knowledge, is the first formulation of its type for the single defence assignment type.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Model for Thesis Defence Scheduling

Almeida¹,

Santos²,

Figueira³

2022

Preprint

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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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A survey of the state-of-the-art of optimisation methodologies in school timetabling problems

Cited by 59 publications

References 50 publications

Bibliometric Analysis of University Timetabling Using Publish and Perish

Bibliometric Analysis of University Timetabling Using Publish and Perish

Business Process Models Complexity Growth Analysis based on Scheduling Generated by Timetabling of Courses

A Multi-Objective Model for Thesis Defence Scheduling

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