Automated university lecture timetable using Heuristic Approach

There are different approaches used in automating course timetabling problem in tertiary institution. This paper present a combination of genetic algorithm (GA) and simulated annealing (SA) to have a heuristic approach (HA) for solving course timetabling problem in Federal University Wukari (FUW). The heuristic approach was implemented considering the soft and hard constraints and the survival for the fittest. The period and space complexity was observed. This helps in matching the number of rooms with the number of courses. Keywords: Heuristic approach (HA), Genetic algorithm (GA), Course Timetabling, Space Complexity

Decomposition, Reformulation, and Diving in University Course Timetabling

In many real-life optimisation problems, there are multiple interacting components in a solution. For example, different components might specify assignments to different kinds of resource. Often, each component is associated with different sets of soft constraints, and so with different measures of soft constraint violation. The goal is then to minimise a linear combination of such measures. This paper studies an approach to such problems, which can be thought of as multiphase exploitation of multiple objective-/value-restricted submodels. In this approach, only one computationally difficult component of a problem and the associated subset of objectives is considered at first. This produces partial solutions, which define interesting neighbourhoods in the search space of the complete problem. Often, it is possible to pick the initial component so that variable aggregation can be performed at the first stage, and the neighbourhoods to be explored next are guaranteed to contain feasible solutions. Using integer programming, it is then easy to implement heuristics producing solutions with bounds on their quality. Our study is performed on a university course timetabling problem used in the 2007 International Timetabling Competition, also known as the Udine Course Timetabling Problem. In the proposed heuristic, an objective-restricted neighbourhood generator produces assignments of periods to events, with decreasing numbers of violations of two period-related soft constraints. Those are relaxed into assignments of events to days, which define neighbourhoods that are easier to search with respect to all four soft constraints. Integer programming formulations for all subproblems are given and evaluated using ILOG CPLEX 11. The wider applicability of this approach is analysed and discussed.Comment: 45 pages, 7 figures. Improved typesetting of figures and table

Hybrid harmony search with great deluge for UUM CAS curriculum based course timetabling

Producing university course timetabling is a tough and complicated task due to higher number of courses and constraints.The process usually consisted of satisfying a set of hard constraints so as a feasible solution can be obtained.It then continues with the process of optimizing (minimizing) the soft constraints in order to produce a good quality timetable. In this paper, a hybridization of harmony search with a great deluge is proposed to optimize the soft constraints.Harmony search comprised of two main operators such as memory consideration and random consideration operator.The great deluge was applied on the random consideration operator. The proposed approach was also adapted on curriculum-based course timetabling problems of College of Arts and Sciences, Universiti Utara Malaysia (UUM CAS).The result shows that the quality of timetable of UUM CAS produced by the proposed approach is superior than the quality of timetable produced using the current software package

An Optimization of University Course Timetabling using Case-Based Reasoning and Graph Coloring

ABSTRAKSI: University Course Timetabling (UCT) has a very challenging task to satisfy a set of stated objectives for students, lecturers, courses, rooms, and times to the highest possible extent. These objectives including the constraints must be assigned into the timeslots. This study attempts to solve the UCT problem by combining a Case-Based Reasoning (CBR) method and a Graph Coloring result. These two methods will solve satisfy the corresponding constraints, CBR to satisfy the soft-constraints and Graph Coloring to satisfy the hard-constraint. Combining these two methods has been implemented in this UCT system, which is an automated timetabling system to provide the timetable with an optimal solution.Kata Kunci : University Course Timetabling (UCT), hard-constraint, soft-constraint, Graph Coloring, Case-Based Reasoning (CBR), Optimal, timetabling.ABSTRACT: Permasalahan Penjadwalan Kuliah merupakan sebuah permasalahan yang kompleks dan menarik untuk diselesaikan karena harus dapat memenuhi sejumlah kebutuhan dari objek perkuliahan seperti mahasiswa, dosen, mata kuliah, ruang dan waktu dengan tingkat kepuasan setinggi mungkin. Adapun kebutuhan-kebutuhan tersebut memiliki sejumlah batasan untuk dapat dialokasikan ke dalam timeslot. Penelitian ini berusaha memecahkan permasalahan penjadwalan kuliah dengan mengkombinasikan metode Penalaran Berbasis Kasus atau Case-Based Reasoning (CBR) dan Pewarnaan Graf. Kedua metode ini digunakan sesuai dengan batasan atau constraint yang akan dipenuhi, CBR dipakai untuk memenuhi soft-constraints dan Pewarnaan Graf dipakai untuk memenuhi hard-constraint. Kombinasi kedua metode ini diimplementasikan dalam sistem penjadwalan kuliah, menjadi sebuah sistem penjadwalan kuliah otomatis dengan hasil berupa sebuah jadwal kuliah yang optimal.Keyword: Permasalahan Penjadwalan kuliah, hard-constraint, soft-constraint, Pewarnaan Graf, Case-Based Reasoning (CBR), Optimal, Penjadwalan Kuliah

A Micro-Genetic Algorithm Approach for Soft Constraint Satisfaction Problem in University Course Scheduling

A university course timetabling problem is a combination of optimization problems. The problems are more challenging when a set of events need to be scheduled in the time slot, to be located to the suitable rooms, which is subjected to several sets of hard and soft constraints. All these constraints that exist as regulations within each resource for the event need to be fulfilled in order to achieve the optimum tasks. In addition, the design of course timetables for universities is a very difficult task because it is a non-deterministic polynomial, (NP) hard problem. This problem can be minimized by using a Micro Genetic Algorithm approach. This approach, encodes a chromosome representation as one of the key elements to ensure the infeasible individual chromosome produced is minimized. Thus, this study proposes an encoding chromosome representation using one-dimensional arrays to improve the Micro Genetic algorithm approach to soft constraint problems in the university course schedule. The research contribution of this study is in developing effective and feasible timetabling software using Micro Genetic Algorithm approach in order to minimize the production of an infeasible individual chromosome compared to the existing optimization algorithm for university course timetabling where UNITAR International University have been used as a data sample. The Micro Genetic Algorithm proposed has been tested in a test comparison with the Standard Genetic algorithm and the Guided Search Genetic algorithm as a benchmark. The results showed that the proposed algorithm is able to generate a minimum number of an infeasible individual chromosome. The result from the experiment also demonstrated that the Micro Genetic Algorithm is capable to produce the best course schedule to the UNITAR International University

ASYNCHRONOUS ISLAND MODEL GENETIC ALGORITHM FOR UNIVERSITY COURSE TIMETABLING

ABSTRAKSI: Penjadwalan merupakan masalah kombinatorial kompleks yang diklasifikasikan sebagai NP - Hard . Masalah penjadwalan kuliah universitas (MPKU) mirip dengan masalah penjadwalan pada umumnya dengan beberapa bagian yang unik. MPKU adalah permasalahan penjadwalan dimana kita harus melakukan penjadwalan untuk pertemuan perkuliahan ke dalam slot waktu dan ruang tertentu dengan mempertimbangkan batasan keras (hard constrain) dan lunak (soft constraint) . Telkom University memiliki masala h yang hampir mirip dengan penjadwalan tersebut. Solusi saat ini dengan informed genetic algorithm untuk Telkom Universit y MPKU masih memiliki masalah waktu eksekusi .sland Model Genetic Algorithm digunakan dalam tesis ini untuk memecahkan masalah terseb ut . Ide tesis ini adalah membuat model pertukaran Individu untuk men distribusikan i ndividu lokal terbaik sebuah pulau dengan pulau lain. Island Model GA dapat membuat jadwal kuliah universitas dalam waktu yang masih dapat dipertimbangkan. Model terdistribusi ini dapat berjalan lebih cepat daripada model tunggal menurunkan pelanggaran batasan untuk mencapai nilai fitness yang optim um . Hal ini dapat terjadi karena model ini dapat keluar dari optimum lokal dengan lebih mudah. Island Model GA bahkan dapat menghasilkan akurasi yang baik untuk dataset Universitas Telkom (99,74%) dan akurasi yang cukup untuk dataset Purdue (96,80%) pada penjadwalan level mahasiswa.Kata Kunci : penjadwalan , penjadwalan kuliah universitas , informed genetic algorithm, island model genetic algorithmABSTRACT: Timetabling is a complex combinatorial problem classified as NP - Hard. University course timetabling problem (UCTP) is similar to other timetabling problems with some additional unique parts. UCTP involves assigning lecture events to timeslots and rooms subject to a variety of hard and soft constraints. Telkom University has almost similar problem with it s course timetabling. The current solution with Informed Genetic Algorithm for Telkom University UCTP still has the time consuming problem.sland Model informed Genetic Algorithm was used in this thesis to solve this problem. The idea of this thesis is m aking distributed model exchanges an island‟s local best Individu with another island. Island model GA could create university course timetabling in reasonable time. This distributed model could run faster rather than single machine model decreasing constr aint violations to reach optimum fitness. It could have less constraint violations because it could escape from stagnant local optimum easier. Island model GA could even produced great accuracy for Telkom University dataset ( 99.74% ) and acceptable accuracy at 96.80% for Purdue dataset for student level timetabling .Keyword: timetabling, university course timetabling problem, informed genetic algorithm, island model genetic algorith

The Application of Late Acceptance Heuristic Method for the Tanzanian High School Timetabling Problem

High School timetabling is the problem of scheduling lessons of different subjects and teachers to timeslots within a week, while satisfying a set of constraints which are classified into hard and soft constraints. This problem is different from university course timetabling problem because of the differences in structures including classroom allocations and grouping of subject combinations. Given the scarce education resources in developing countries, high school timetabling problem plays a very important role in optimizing the use of meager resources and therefore contribute to improvement of quality of education. The problem has attracted attention of many researchers around the world; however, very little has been done in Tanzania. This paper presents a solution algorithm known as Late Acceptance heuristic for the problem and compares results with previous work on Simulated Annealing and Great Deluge Algorithm for three schools in Dar es Salaam Tanzania. It is concluded that Late Acceptance heuristic gives results which are similar to the previous two algorithms but performs better in terms of time saving. Keywords: Late Acceptance; High School Timetabling; Combinatorial Optimization; Heuristics; NP-Har

Genetic Algorithm For University Course Timetabling Problem

Creating timetables for institutes which deal with transport, sport, workforce, courses, examination schedules, and healthcare scheduling is a complex problem. It is difficult and time consuming to solve due to many constraints. Depending on whether the constraints are essential or desirable they are categorized as ‘hard’ and ‘soft’, respectively. Two types of timetables, namely, course and examination are designed for academic institutes. A feasible course timetable could be described as a plan for the movement of students and staff from one classroom to another, without conflicts. Being an NP-complete problem, many attempts have been made using varying computational methods to obtain optimal solutions to the timetabling problem. Genetic algorithms, based on Darwin\u27s theory of evolution is one such method. The aim of this study is to optimize a general university course scheduling process based on genetic algorithms using some defined constraints

Selecting quality initial random seed for metaheuristic pproaches: a case of timetabling problem

The Timetabling Problem is a combinatorial optimization problem. The University Course Timetabling Problems (UCTP) deals with the scheduling of the teaching program. Metaheuristic techniques have been very successful in a wide range of timetabling problem including UCTP. The performance of metaheuristic over UCTP is measured by quality timetable that is no violation of hard constraints and the lowest number of soft constraint violated. The stochastic natures of the metaheuristic approaches make it difficult to predict the quality of end result produced. Therefore the initial quality solutions are one of the important factors contributed to success of metaheuristic approaches in solving optimization problem particularly UCTP. This paper analyzes the effect of different random seed over metaheuristic performance. Techniques for selecting quality random seeding as an input for metaheuristic algorithm to solve university course timetabling are presented. The main objective is to obtain quality initial solution without much effort to construct difficult heuristic. The result obtained gives us opportunity to choose quality initial solution with less effort