Applying a novel clustering technique based on FP-tree to university timetabling problem: A case study

Shatnawi, Safwan; Al-Rababah, Khaleel; Bani-Ismail, Basel

doi:10.1109/icces.2010.5674875

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…However, recently (Shatnawi, Al -Rababah, & Bani-Ismail, 2010) has used a novel clustering technique based on FP-Tree to solve UCTTP where the given technique is done to classify students based on their selective courses who submitted for the next semester. The aim of this clustering is to solve scheduling of courses where in the previous semesters the submission of students in some courses due to simultaneous scheduling has been prevented, while in this technique no conflict would happen over scheduling of exams since no two exams at the same time would be taken for courses by two identical groups of students.…”

Section: Fuzzy Approachmentioning

confidence: 99%

A survey of approaches for university course timetabling problem

Babaei

Karimpour

Hadidi

2015

Computers & Industrial Engineering

182

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Section: Fuzzy Approachmentioning

confidence: 99%

A survey of approaches for university course timetabling problem

Babaei

Karimpour

Hadidi

2015

Computers & Industrial Engineering

182

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“…Timetabling was also correlated with the general class of network flow problems [5]. Other methods include clustering of the problem to smaller sub-problems [6]. The application of case-based reasoning to timetabling also gives promising results [7].…”

Section: A Multidisciplinary Contributions To Timetablingmentioning

confidence: 99%

Course Scheduling in an Adjustable Constraint Propagation Schema

Pothitos

Stamatopoulos

Zervoudakis

2012

2012 IEEE 24th International Conference on Tools With Artificial Intelligence

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Abstract-Constraint Programming constitutes a prominent paradigm for solving time-consuming Constraint Satisfaction Problems (CSPs). In this work, at first we model a generic course scheduling problem as a CSP, that complies with the International Timetabling Competition (ITC) standards. Constraint Programming allowed us to search for a solution via several state-of-the-art methodologies and compare them. For the stochastic search methods, we propose new hybrid semi-random heuristics. Second, we chose to maintain bounds consistency during search to prune 'no-good' branches of the search tree. We theoretically define new lightweight consistency types, namely k-bounds-consistency, in order to speed up the overall search procedure. Eventually, we process real world data and show the efficiency of our proposal: While plain backtracking produces poor results, constraint propagation dramatically boosts the solutions quality, and can be 'finetuned' in our adjustable schema to make it even faster.

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“…The applied fuzzy logic within this approach is also used to evaluate the violation of soft constraints in objective function due to facing with uncertainty in real world data. However, Shatnawi, Rababah, & Bani-Ismail [22] has used a novel clustering technique based on FP-Tree to solve university course timetable problem where the given technique is done to classify students based on their selective courses who submitted for the next semester. However, a fuzzy genetic algorithm has been presented by Shahvali Kohshori, Saniee Abadeh, & Sajedi [23] accompanied with local search to solve university course timetable problem where the fuzzy genetic algorithm with a local search algorithm uses inductive search to solve the combined problem and applied local search which has the ability of improving efficiency within genetic algorithm.…”

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confidence: 99%

Preferences Based Decision Making Mathematical Model for Faculty Course Assignment using Linear and Exponential Membership Functions

Shah¹

2018

IJRASET

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Abstract-This paper gives a general model for the faculty course assignment problem that is a zero-one nonlinear multiobjective programming problem. Because of the nonconvexity of the problem, linear membership function and exponential membership function are used to find optimal solutions. The model with fuzzy methods provides a more satisfactory solution to a course assignment problem than assigning with arbitrary weights. Keywords-Preferences based decision makers, zero-one multiobjective programming, faculty course problem; scalarization. I. INTRODUCTIONThe employee assignment problem is becoming more intricate now a day. Specially schools, colleges, industries, organizations, etc are facing scheduling problem for assigning task. For example, scheduling or assigning work means matching people, places, time slots, and facilities. Further it is very difficult to solve problems having so many constraints. Generally, constraint is two types hard and soft. The problem of faculty course assignment means to satisfy all the constraints like one subject to one teacher only, teacher preference to teach course, not exceeding load, all course is distributed according to preferences of teachers as well as administrator. So many researchers have carried out research in the field of assigning courses to faculty. [3]. Two-stage optimization model to maximize faculty course preferences in assigning faculty members to courses (stage 1) and then maximize faculty time preferences by allocating courses to time blocks (stage 2). These constraints, which are computationally more complex than the others, are recovered during the second stage, and a number of sub-problems, one for each day of the week, are solved for local optima by Badri [7]. Bloomfield and McShary [1] also considered faculty preferences in their heuristic approach. Kara and Ozdemir [8] developed a minimax approach to the faculty course assignment problem by considering faculty preferences. Asratian and Werra [13] considered a theoretical model which extends the basic class teacher model of timetabling. This model corresponds to some situations which occur frequently in the basic training programs of universities and schools. It has been shown that this problem is NP complete when founded in some sufficient conditions for the existence of a timetable. Kara and Ozdemir presented a min-max approach to the faculty course assignment problem by considering faculty preferences. This study is a continuation and generalization of the faculty-course assignment problem considered earlier by Ozdemir and Gasimov [14]. They constructed a multi objective 0-1 nonlinear model for the problem, considering participants' average preferences and explained an effective way for its solution.To optimal fuzzy classification of students, Amintoosi & Haddadnia [18] has used a fuzzy function to solve university course timetable by genetic programming problem. A hybrid fuzzy evolutionary algorithm has been presented by Rachmawati & Srinivasan [20] to multi objective resource allocation pro...

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Applying a novel clustering technique based on FP-tree to university timetabling problem: A case study

Cited by 6 publications

References 10 publications

A survey of approaches for university course timetabling problem

A survey of approaches for university course timetabling problem

Course Scheduling in an Adjustable Constraint Propagation Schema

Preferences Based Decision Making Mathematical Model for Faculty Course Assignment using Linear and Exponential Membership Functions

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