An automated university course timetabling system developed in a distributed environment: A case study

Dimopoulou, Maria; Miliotis, P.

doi:10.1016/s0377-2217(03)00104-8

Cited by 36 publications

(21 citation statements)

References 10 publications

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“…The solution in this paper maintains a mixture of action-driven approach and strategy-driven approach [4] which is mainly a reasoning approach [11]. It combines using of heuristic algorithm and analytical method, and emphasizing the development of suitable time-table that meets course instructors preferences [4,11] to design the time-table .…”

Section: The Solutionmentioning

confidence: 99%

“…It combines using of heuristic algorithm and analytical method, and emphasizing the development of suitable time-table that meets course instructors preferences [4,11] to design the time-table . The design is then implemented using an automated time-table system to generate the time-table and facilitate data mining to support decision making .…”

Section: The Solutionmentioning

confidence: 99%

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Using Color Coded Pattern Clustering Model Combined with Automated System for Courses Scheduling

Aljasmi¹

2016

IJCNT

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Course scheduling is very important but complex task at universities. The complexity increases when a course includes lab hours in addition to the lecture hours. Course schedule often yields to dissatisfaction of students, instructors, and university administration. In this paper, I discuss a Color Coded Pattern Clustering Model that applies a reasoning approach to design the time table in the Computer Science Department at University of Bahrain. The model supports minimizing clashes between lectures and lab sessions and facilitate distributing the courses among the time blocks. The model, then has been implemented to automatically generate the time-table and support data mining. It supports minimizing clashes in students time table and preserves faculty preferences.

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Section: The Solutionmentioning

confidence: 99%

Section: The Solutionmentioning

confidence: 99%

Using Color Coded Pattern Clustering Model Combined with Automated System for Courses Scheduling

Aljasmi¹

2016

IJCNT

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“…Another milestone is the study of Carter (1989), who solved instances of a course timetabling problem with several soft constraints of up to 287 events using Lagrangian relaxation. More recently, modest instances have even been solved using off-the-shelf solvers (Dimopoulou and Miliotis 2004;Qualizza and Serafini 2004;Daskalaki et al 2004Daskalaki et al , 2005Mirhassani 2006). For instance, Daskalaki et al (2004Daskalaki et al ( , 2005 solved instances of up to 211 events using ILOG CPLEX, without introducing any user cuts.…”

Section: Problem Descriptionmentioning

confidence: 99%

A branch-and-cut procedure for the Udine Course Timetabling problem

et al. 2011

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A branch-and-cut procedure for the Udine Course Timetabling problem is described. Simple compact integer linear programming formulations of the problem employ only binary variables. In contrast, we give a formulation with fewer variables by using a mix of binary and general integer variables. This formulation has an exponential number of constraints, which are added only upon violation. The number of constraints is exponential. However, this is only with respect to the upper bound on the general integer variables, which is the number of periods per day in the Udine Course Timetabling problem.A number of further classes of cuts are also introduced, arising from: enumeration of event/free-period patterns; bounds on the numbers of days of instruction; the desire to exploit integrality of the objective function value; the graph colouring component; and also from various implied bounds. An implementation of the corresponding branch-and-cut procedure is evaluated on the instances from Track 3 of the International Timetabling Competition 2007.

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“…In the last few years, colleges and universities around the world are expanding enrollment in order to develop more high-quality personnel, various countries' educational reform is also thorough unceasingly [1][2][3] . The essence of course scheduling question is the curriculum, the teacher and the student should be assigned in the appropriate time section to the appropriate classroom.…”

Section: Introductionmentioning

confidence: 99%

The Application Research of Improved Bat Algorithm for Time Table Problem

Liu¹

2016

Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

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Abstract:The time table problem is a typical NP complete problem, when the bat algorithm solves this problem; it will quickly converge, and can get the local optimal solution but not the global optimal solution. Therefore, we use the advantage of active evolutionary operator to improve the bat algorithm, and to solve TTP problem. We do a lot of experiments and evaluate the performance of the improved bat algorithm. The experiment results show that improved bat algorithm is a more superior algorithm to apply to the TTP problem.

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An automated university course timetabling system developed in a distributed environment: A case study

Cited by 36 publications

References 10 publications

Using Color Coded Pattern Clustering Model Combined with Automated System for Courses Scheduling

Using Color Coded Pattern Clustering Model Combined with Automated System for Courses Scheduling

A branch-and-cut procedure for the Udine Course Timetabling problem

The Application Research of Improved Bat Algorithm for Time Table Problem

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