University Course Timetabling with Genetic Algorithm: A Laboratory Excercises Case Study

Bratković, Zlatko; Herman, Tomislav; Omrčen, Vjera; Čupić, Marko; Jakobović, Domagoj

doi:10.1007/978-3-642-01009-5_21

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…OBTAINED FROM RUNNING PROPOSED Algorithm for presented courses has been shown in figure (8). The results of this test is better that using standard genetic algorithm, and as we mentioned above, in addition to hard Constraints, some soft Constraints have also been considered in creating primary population which has caused the initial fitness rat of chromosomes to decrease.…”

Section: The Results Of Running Proposed New Algorithm Curve Of Resultsmentioning

confidence: 88%

A Fast Genetic Algorithm for Solving University Scheduling Problem

Abbaszadeh

Saeedvand

2014

IJ-AI

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University course timetabling is a NP-hard problem which is very difficult to solve by conventional methods, we know scheduling problem is one of the Nondeterministic Polynomial (NP) problems. This means, solving NP problems through normal algorithm is a time-consuming process (it takes days or months with available equipment) which makes it impossible to be solved through a normal algorithm like this. In purposed algorithm the problem of university class scheduling is solved through a new chromosome structure and modifying the normal genetic methods which really improves the solution in this case. We include lecturer, class and course information in presented algorithm, with all their Constraints, and it creates optimized scheduling table for weekly program of university after creating primary population of chromosomes and running genetic operators. In the final part of this paper we conclude from the results of input data analysis that the results have high efficiency compared with other algorithms considering maximum Constraints.

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Section: The Results Of Running Proposed New Algorithm Curve Of Resultsmentioning

confidence: 88%

A Fast Genetic Algorithm for Solving University Scheduling Problem

Abbaszadeh

Saeedvand

2014

IJ-AI

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“…The algorithm prioritizes courses with Algorithm 2. Pseudocode of the MAPT co crossover operator, adapted from Bratković et al (2009).…”

Section: Initial Solutionsmentioning

confidence: 99%

“…PMX, CX, and OX operators are frequently used to solve scheduling problems (Chinnasri et al, 2012). We propose a new crossover operator called MAPT co , which was inspired by Bratković et al (2009) and two-point crossover. The pseudocode is displayed in Algorithm 2.…”

Section: Crossovermentioning

confidence: 99%

“…A custom operator named MAPT mo is proposed to be compared with a classical operator. MAPT mo was adapted from Bratković et al (2009) and incorporates business logic into its design. The operator randomly selects an event's session and then determines whether to generate a random valid time slot or select new resources (rooms, equipment, or trainers).…”

Section: Mutationmentioning

confidence: 99%

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Solving a multiobjective professional timetabling problem using evolutionary algorithms at Mandarine Academy

Hafsa

Wattebled²,

Jacques

et al. 2023

Int Trans Operational Res

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In this paper, we propose a method for solving a real‐world timetabling problem at Mandarine Academy. The primary motivation for this work is to provide an automated professional course scheduling tool to replace the time‐consuming task of manually creating timetables that are constantly incorrect. Following a review of both scientific literature and company requirements, a mathematical model of the problem is provided, which includes 18 constraints (hard/soft) and five objectives, two of which are competing. We test a handful of multi‐objective evolutionary algorithms (MOEA's) starting with the non‐dominated sorting genetic algorithm (NSGA II and NSGA III), the multi‐objective evolutionary algorithm based on decomposition (MOEA/D), the indicator‐based evolutionary algorithm and finally the strength Pareto evolutionary algorithm . Two custom genetic operators (mutation and crossover) are proposed and compared to conventional operators (PMX and swap mutation). To obtain elite configurations, a tuning phase involving all of the aforementioned algorithms is carried out. Experiments were divided by problem size, with three to five objectives tested. Experiments include the use of real‐world data from the company's catalog. This dataset was made available to the scientific community to serve as a testing ground for professional course scheduling, an underexploited field of scheduling. We discuss findings, including a comparison of each algorithm's performance using various metrics, as well as convergence graphs and population evolution.

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“…In general, scheduling problem in universities (course timetabling problem -CTP) includes finding the appropriate allocation of time within a limited time period for all events (such as courses, semester exams) and assigns them to a number of resources (teachers, students and classrooms) to ensure that the constraints are met [1,2,3,4,6]. However, in credit training, depending on the characteristics of each university, the scheduling problem will be deployed with certain differences.…”

Section: Problem Formulationmentioning

confidence: 99%

A Genetic Algorithm Based Method for Timetabling Problems Using Linguistics of Hedge Algebra in Constraints

Long

2017

JCC

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Timetabling problems is one of very significant problems in many fields of applications. As mentioned in [3], this problem is NP-complete with numerous factors and constraints. In general, it is treated as a multi-objective optimization problem. Currently, the work of scheduling is very difficult in universities, especially in credits training. Almost it is impossible to control all cases of the problem by human. Therefore, we cannot manually give an effective solution for this problem. There are quite many methods to resolve this problem in literature, they are mostly searching methods based on genetic algorithms and their results are proved effective in practice. In this paper, we propose a method based on genetic algorithms for university course timetabling problems with some modifications and apply it to real-world datasets in Hanoi Open University.

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University Course Timetabling with Genetic Algorithm: A Laboratory Excercises Case Study

Cited by 6 publications

References 13 publications

A Fast Genetic Algorithm for Solving University Scheduling Problem

A Fast Genetic Algorithm for Solving University Scheduling Problem

Solving a multiobjective professional timetabling problem using evolutionary algorithms at Mandarine Academy

A Genetic Algorithm Based Method for Timetabling Problems Using Linguistics of Hedge Algebra in Constraints

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