Preferences based decision-making model (PDM) for faculty course assignment problem

Parthiban, P.; Ganesh, K.; Narayanan, Sambasivan; Dhanalakshmi, R.

doi:10.1109/iemc.2004.1408912

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Yu and Sung employed a sector-based genetic algorithm to solve the course scheduling problem in universities, wherein a sector to which an event constituted by subject, class, teacher, classroom capacity, department, and type of teaching/learning belongs is matched with a location of the class to produce a course scheduling result with better cost effectiveness [10]. Parthiban et al studied the course/professor assignment problem and thought the use of mathematical models to solve constrained matching problems has the following shortcomings: quantified data are difficult to deal with; problems are excessively abstracted; problems are hard to describe; and there are problems with combination [11]. They proposed a method for solving the teacher/course matching problem with the Analytical Hierarchy Process.…”

Section: A Course Schedulingmentioning

confidence: 99%

On the application of data mining technique and genetic algorithm to an automatic course scheduling system

Wang

Cheng

Chang

et al. 2008

2008 IEEE Conference on Cybernetics and Intelligent Systems

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In this study, a data mining technique and a genetic algorithm are applied to an automatic course scheduling system to produce course timetables that best suit students' and teachers' needs. Course scheduling in colleges and universities is a highly complicated task for satisfying multiple constraints. Conventional course scheduling is done mainly from the school's point of view, such that courses and timetables are planned according to the characteristics of individual departments and institutes, with little attention to students' interests and their needs in career development. This study develops a practical automatic course scheduling system based on students' needs, wherein the course scheduling process is divided into two stages. In the first stage, students' needs in course selection are considered and an association among courses selected by students is mined using the association mining technique; while in the second stage, the genetic algorithm is used to arrange the course timetable. More particularly, this study is based on students' willingness in course selection, analyzes the effects of course arrangement in different class periods on students' learning performance, takes into account teachers' preferred schedules, determines the cost function value of each class period, and then applies the genetic algorithm for class period exchange, so as to produce an optimal course timetable. It is shown in the experiment results that the automatic course scheduling system proposed in this study not only can efficiently replace the onerous operation of conventional manual course scheduling, but also produce course timetables that truly fulfill users' needs and increase students' and teachers' satisfaction, thereby providing a win-win-win solution for students, teachers and the school.

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Section: A Course Schedulingmentioning

confidence: 99%

On the application of data mining technique and genetic algorithm to an automatic course scheduling system

Wang

Cheng

Chang

et al. 2008

2008 IEEE Conference on Cybernetics and Intelligent Systems

View full text Add to dashboard Cite

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“…Problem" by Parthiban et al (2004) presents a decision support system using Analytic Hierarchy Process (AHP).…”

Section: Summary Of Related Literaturementioning

confidence: 99%

Preference Driven University Course Scheduling System

Bellardo¹

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Preference Driven University Course Scheduling SystemHeather Bellardo University course planning and scheduling is the process of determining what courses to offer, how many sections are needed, determining the best term to offer each section, assigning a faculty member to instruct each section, and scheduling each section to a timeslot to avoid conflicts. The result of this task has an impact on every student and faculty member in the department. The process is typically broken down into three major phases: course offering planning, faculty assignment to planned course sections, and course scheduling into timeslots.This thesis looks at each of these phases for the Industrial and Manufacturing department and brings them together into a decision support and scheduling system. A decision support tool is created to facilitate planning of course offerings. Operations research is applied to assign sections to faculty members using a faculty preference driven integer linear programming model in order to minimize dissatisfaction in the department. Next, the faculty-section pairs are scheduled into university timeslots using a complex integer linear programming model. This scheduling model takes into consideration the faculty member time availability and preferences and general student time slot preferences as it minimizes dissatisfaction while avoiding conflicts among labs, faculty members and courses offered for each class level.

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Department course selection problem: The primitive cognitive network process approach

Kam

Yuen

2012

Proceedings of IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) 2012

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Preferences based decision-making model (PDM) for faculty course assignment problem

Cited by 3 publications

References 19 publications

On the application of data mining technique and genetic algorithm to an automatic course scheduling system

On the application of data mining technique and genetic algorithm to an automatic course scheduling system

Preference Driven University Course Scheduling System

Department course selection problem: The primitive cognitive network process approach

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