The Importance of Fuzzy Preference in Course Assignment Problem

Gorka, Artur; Thipwiwatpotjana, Phantipa

doi:10.1155/2015/106727

Cited by 6 publications

(6 citation statements)

References 6 publications

(9 reference statements)

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“…The mentioned information in this section and the model study in the next section have cited from [18,17]. Firstly, we divide the teaching preference value into 6 ranks with different fuzzy values of ranks as presented in [18]. The provided ranks of instructors represent the expertise or interest in each subject.…”

Section: Descriptions and Notation Of Course Assignment Problemmentioning

confidence: 99%

“…In this context, we assume that the decisions of all instructors to clarify their rank of subjects are the same way. assignment problem, more details can be seen in [18].…”

Section: Descriptions and Notation Of Course Assignment Problemmentioning

confidence: 99%

“…Normally, the value of M 2 should be greater than M 1 because having positive residual workload may refer that an instructor cannot keep his or her promise to complete work. By literature [18], we choose (M 1 , M 2 ) = (0.0015, 10) which was experimented for a reasonable result.…”

Section: Formulation the Course Assignment Modelmentioning

confidence: 99%

“…By following the idea of Leela-apiradee [16], we propose the characterization of these two solutions. By reviewing the research studies of Thipwiwatpotjana et al [17,18],…”

Section: Introductionmentioning

confidence: 99%

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Tolerance-localized and control-localized solutions to system of interval linear equations

Burimas¹

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In this thesis, we are interested in a system of interval linear equations Ax=b whose coefficient A and right hand side b vary in some real intervals. We study two types of solutions called tolerance--localized and control--localized solutions of interval linear equations system. The characterizations of each solution are proposed in two main theorems. First, the proposed theorem is stated in terms of center and radius matrices which is directly proved by following their definitions. The other theorem is presented as magnitude sense with new notation. Based on the second theorem, the closed form of all solution sets is released. In addition, we apply the idea of tolerance--localized solution to deal with the course assignment problem. To optimize the preference and over/under workload of the instructors, we formulate the modified integer linear programming model to solve the problem. The obtained result is not significantly different from the original model. Moreover, we found that the obtained result gives higher overall preference of the instructors than actual course assignment in the 2nd semester of 2018.

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Section: Descriptions and Notation Of Course Assignment Problemmentioning

confidence: 99%

“…In this context, we assume that the decisions of all instructors to clarify their rank of subjects are the same way. assignment problem, more details can be seen in [18].…”

Section: Descriptions and Notation Of Course Assignment Problemmentioning

confidence: 99%

Section: Formulation the Course Assignment Modelmentioning

confidence: 99%

“…By following the idea of Leela-apiradee [16], we propose the characterization of these two solutions. By reviewing the research studies of Thipwiwatpotjana et al [17,18],…”

Section: Introductionmentioning

confidence: 99%

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Tolerance-localized and control-localized solutions to system of interval linear equations

Burimas¹

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“…In every academic year, the head of the department asks each instructor to provide his/her preference ranking of each course as shown in Figure 3.1 or for more details in Appendix A. A. Gorka and P. Thipwiwatpotjana [5] study about the important preference ranking value. In our work, we will use the same value of preference ranking as [5] and teaching preference description is provided in Table 3.1.…”

Section: Teaching Preferencesmentioning

confidence: 99%

A two-stage model for balancing instructor workload and teaching preference in university course timetabling

Burana¹

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This thesis focuses on balancing instructor workload and maximizing preferences by using the data in the first semester of 2019 from Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University as a case study. Since there are many instructors with over-workload in the department which directly affect their research qualities, balancing teaching workload is the main objective of this study. The proposed approach to balance workload is to split some basic courses into two parts: before midterm and after midterm and then assign each course to two instructors. Moreover, the preferences or the requests of teaching a course are important to maintain the instructor comfortable, and the preferences or the requests of teaching a course also help students to gain knowledge to their full potentials. The results show that our model is able to reduce both the number of instructors who have the high difference of requested workload and assigned workload, and the number of non-preferable courses for each instructor by comparing our results with the department timetable and the model without splitting courses.

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Special Tolerance Left Solution for Course Assignment Problem with Interval Workload Constraint

Pongsumrankul

Thipwiwatpotjana

Gorka

2022

Applications of Fuzzy Techniques

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The Importance of Fuzzy Preference in Course Assignment Problem

Cited by 6 publications

References 6 publications

Tolerance-localized and control-localized solutions to system of interval linear equations

Tolerance-localized and control-localized solutions to system of interval linear equations

A two-stage model for balancing instructor workload and teaching preference in university course timetabling

Special Tolerance Left Solution for Course Assignment Problem with Interval Workload Constraint

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