A Tabu search algorithm with controlled randomization for constructing feasible university course timetables

Mao, Chen; Tang, Xiangyang; Song, Ting; Wu, Chao; Liu, Sanya; Peng, Xicheng

doi:10.1016/j.cor.2020.105007

Cited by 22 publications

(10 citation statements)

References 25 publications

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“…No lecturer should teach more than one lecture in any timeslot (Conflict of lecturer) (8) Each lecturer cannot teach more than the limited number of their workload per day (Workload of lecturers per day) (9) Each student group cannot attend more than the limited number of their workload per day (Workload of student groups per day) (10) Each lecturer cannot have more than the maximum number of consecutive lectures per day (11) Each student group cannot have more than the maximum number of consecutive lectures per day (12) Some lectures of the same course are to be scheduled on the same day (Same day) (13) Some lectures of the same course must not be scheduled on the same day (Not same day) (14) Some lectures of the same course are to be scheduled consecutively (Consecutive courses) (15) Some lectures cannot be scheduled consecutively (Non-consecutive lectures) (16) Interval between two lectures (morning and afternoon sessions) (17) Lectures with a large number of students are to be scheduled simultaneously (Simultaneously)…”

Section: Mathematical Model Formulationmentioning

confidence: 99%

“…Other studies that have also employed IP with other strategies include Oladokun and Badmus [5], MirHassani [6], Colajanni and Daniele [7] and Lemos et al, [8]. Besides integer programming, graph coloring (Samarasekara [9]), simulated annealing (Gunawan and Ng [10]), genetic algorithm (Modibbo et al [11]), tabu search (Chen et al [12]), ant colony optimization (Mahmud [13]) and constraint programming (Junn et al [14]) are among the other well-known approaches that have been presented in the literature. There are various other techniques which involve hybrid techniques.…”

mentioning

confidence: 99%

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General University Course Timetabling Mathematical Model to a Malaysian Public University Problem

Zaulir

Aziz

Aizam

2022

Mal. J. Fund. Appl. Sci.

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University course timetabling is a well-known management problem amongst researchers, thus the rich body of literature. However, published articles are mainly on improved solution approaches leads to presentation on different constraints used and ignoring human preferences. This however, limits the model application to other universities. The research aims to bridge the gap by acknowledging these varieties of demands. In the process of generating our mathematical model, we have gone through meticulously researches that have been carried out in the past years to determine the demands of individuals involved directly with the timetable. The varieties of demands were clarified from a survey conducted. An improvised UCTP model was developed, which involves a superset of constraints that also includes the users’ preferences. However, we will extensively discuss on the list of requirements obtained from the survey and demonstrate the requirements that were found acceptable to be considered in a general mathematical model. To verify the compatibility of our mathematical model, we illustrate with real data from a university in Malaysia. The experimental result confirms the applicability of our mathematical model towards real problem. We expect that this model could be in favor of solving other university course timetabling problem with slight modifications.

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Section: Mathematical Model Formulationmentioning

confidence: 99%

mentioning

confidence: 99%

General University Course Timetabling Mathematical Model to a Malaysian Public University Problem

Zaulir

Aziz

Aizam

2022

Mal. J. Fund. Appl. Sci.

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“…The problem of course timetabling is a combinatorial optimization problem [21]. Based on constructive heuristics, the heuristic approaches give better results than the other approaches [22].…”

Section: Background and Related Workmentioning

confidence: 99%

A Hybrid Heuristic Algorithm for Solving COVID-19’s Social Distancing at Universities Campus

Al_Tarawneh¹,

Al-Kaabneh²,

Alhroob³

et al. 2022

Computer Systems Science and Engineering

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“…There are two types of constraints pertaining to the challenge, namely hard constraints and soft constraints [2]. Hard constraint is a limit that must be met in scheduling a class [3]. Examples of this limitation include the maximum capacity of a classroom, the number of schedule slots available in a class and two classes that cannot be scheduled simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Complex University Timetabling Using Iterative Forward Search Algorithm and Great Deluge Algorithm

Premananda

Muklason

2021

khif

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University timetabling is an issue that has received more attention in the field of operations research. Course scheduling is the process of arranging time slots and room for a class by paying attention to existing limitations. This problem is an NP-Hard problem, which means the computation time to find a solution increases exponentially with the size of the problem. Solutions to problems of this kind generally use a heuristic approach, which tries to find a sufficiently good (not necessarily optimal) solution in a reasonable time. We go through two stages in solving the timetabling problem. The first stage is to schedule all classes without breaking any predefined rules. The second stage optimizes the timetable generated in the first stage. This study attempts to solve the class timetabling problem issued in a competition called the 2019 International Timetabling Competition (ITC 2019). In the first stage, we use the Iterative Forward Search (IFS) algorithm to eliminate timetable candidates and to generate a schedule. In the second stage, we employ the Great Deluge algorithm with a hyper-heuristic approach to optimize the solution produced in the first stage. We have tested the method using 30 datasets by taking 1,000,000 iterations on each dataset. The result is an application that does schedule elimination and uses the IFS algorithm to produce a schedule that does not violate any of the hard constraints on 30 ITC 2019 datasets. The implementation of the Great Deluge algorithm optimizes existing schedules with an average penalty reduction of 42%.

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A Tabu search algorithm with controlled randomization for constructing feasible university course timetables

Cited by 22 publications

References 25 publications

General University Course Timetabling Mathematical Model to a Malaysian Public University Problem

General University Course Timetabling Mathematical Model to a Malaysian Public University Problem

A Hybrid Heuristic Algorithm for Solving COVID-19’s Social Distancing at Universities Campus

Complex University Timetabling Using Iterative Forward Search Algorithm and Great Deluge Algorithm

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