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 Enhanced Population Selection Algorithm for Timetabling System

The timetable preparation for systems that are based on credit hours is a challenge for heads of departments. Departments have to prepare their timetables, where the problem is increased when a department serves other departments with some courses. Also, university’s management cannot assure that the offered courses are more than the needs or not. Many algorithms have been tested without an optimal solution. A new proposed algorithm which is called the Enhanced Population Selection (EPS) Algorithm has been implemented and tested with a suitable number of students, courses, lecturers, and venues that is based on the harmony search algorithm and genetic algorithm. The new proposed EPS algorithm has scheduled the timetables for two semesters with academic advisors satisfaction without conflicts. Furthermore, all specified constraints are tested and satisfied

New Swarm-Based Metaheuristics for Resource Allocation and Schwduling Problems

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 10-07-2017Esta tesis tiene embargado el acceso al texto completo hasta el 10-01-201

Performance Analyses of Graph Heuristics and Selected Trajectory Metaheuristics on Examination Timetable Problem

Examination timetabling problem is hard to solve due to its NP-hard nature, with a large number of constraints having to be accommodated. To deal with the problem effectually, frequently heuristics are used for constructing feasible examination timetable while meta-heuristics are applied for improving the solution quality. This paper presents the performances of graph heuristics and major trajectory metaheuristics or S-metaheuristics for addressing both capacitated and un-capacitated examination timetabling problem. For constructing the feasible solution, six graph heuristics are used. They are largest degree (LD), largest weighted degree (LWD), largest enrolment degree (LE), and three hybrid heuristic with saturation degree (SD) such as SD-LD, SD-LE, and SD-LWD. Five trajectory algorithms comprising of tabu search (TS), simulated annealing (SA), late acceptance hill climbing (LAHC), great deluge algorithm (GDA), and variable neighborhood search (VNS) are employed for improving the solution quality. Experiments have been tested on several instances of un-capacitated and capacitated benchmark datasets, which are Toronto and ITC2007 dataset respectively. Experimental results indicate that, in terms of construction of solution of datasets, hybridizing of SD produces the best initial solutions. The study also reveals that, during improvement, GDA, SA, and LAHC can produce better quality solutions compared to TS and VNS for solving both benchmark examination timetabling datasets

Adapting And Hybrid Ising Harmony Search With Metaheuristic Components For University Course Timetabling

Masalah Penjadualan Waktu Kursus Universiti (MPWKU) merupakan suatu masalah penjadualan kombinatorik yang rumit. Algoritma Gelintaran Harmoni (AGH) ialah suatu kaedah metaheuristik berdasarkan populasi. Kelebihan utama algoritma ini terletak pada keupayaannya dalam mengintegrasikan komponen-komponen utama bagi kaedah berdasarkan populasi dan kaedah berdasarkan gelintaran setempat dalam satu model pengoptimuman yang sama. Disertasi ini mencadangkan suatu AGH yang telah disesuaikan untuk MPWKU. Penyesuaian ini melibatkan pengubahsuaian terhadap operator AGH. Hasil yang diperoleh adalah dalam lingkungan keputusan terdahulu. Tetapi beberapa kelemahan dalam kadar penumpuan dan eksploitasi setempat telah dikesan dan telah diberikan tumpuan menerusi penghibridan dengan komponen metaheuristik yang diketahui. Tiga versi terhibrid dicadangkan, di mana, setiap hibrid merupakan peningkatan daripada yang sebelumnya: (i) Algoritma Gelintaran Harmoni yang Diubah suai; (ii) Algoritma Gelintaran Harmoni dengan Kadar Penyesuaian Berbagai Nada, dan (iii) Algoritma Gelintaran Harmoni Hibrid. Semua hasil yang diperoleh dibandingkan dengan 21 kaedah lain menggunakan sebelas dataset piawai de facto yang mempunyai saiz dan kekompleksan yang berbeza-beza. University Course Timetabling Problem (UCTP) is a hard combinatorial scheduling prob- !em. Harmony Search Algorithm (HSA) is a recent metaheuristic population-based method. The major thrust of this algorithm I ies in its abiiity to integrate the key components of populationbased methods and local search-based methods in the same optimisation model. This dissertation presents a HSA adapted for UCTP. The adaptation involved modifying the HSA operators. The results were within the range of state of the art. However, some shortcomings in the convergence rate and local exploitation were identified and addressed through hybridisation with known metaheuristic components. Three hybridized versions are proposed which are incremental improvements over the preceding version: (i) Modified Harmony Search Algorithm (MHSA); (ii) Harmony Search Algorithm with Multi-Pitch Adjusting Rate (HSA-MPAR), and (iii) Hybrid Harmony Search Algorithm (HHSA). The results werecompared against 21 other methods using eleven de facto standard dataset of different sizes and complexity

Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

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

Genetic algorithms with guided and local search strategies for university course timetabling

This article is posted here with permission from the IEEE - Copyright @ 2011 IEEEThe university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

A hybrid algorithm for university course timetabling problem

A hybrid algorithm combining the genetic algorithm with the iterated local search algorithm is developed for solving university course timetabling problem. This hybrid algorithm combines the merits of genetic algorithm and iterated local search algorithm for its convergence to global optima at the same time avoiding being get trapped into local optima. This leads to intensification of the involved search space for solutions. It is applied on a number of benchmark university course timetabling problem instances of various complexities. Keywords: timetabling, optimization, metaheuristics, genetic algorithm, iterative local searc

Projecting input-output table for Malaysia / Norhayati Shuja’

Input-output tables provide detailed accounts of the flow of production and consumption of goods and services from producers to consumers. It serves as a dataset for input-output analysis which provide the tools to perform economic modelling. The construction of the input-output tables based on detailed census or surveys is a complex procedure that requires substantial financial expenditures, large human capital and time. This is the main reason why Malaysia Input-Output Table (MIOT) is produced and published on average every five years. However, for policy makers, the time lag that reflects data from much earlier years is not appropriate to be used for planning and formulating economic policies. Hence, the availability of timely and updated input-output tables is critical for effective assessment of the contribution of industries to the economy. Therefore, projecting inputoutput table for Malaysia is important as it can provide the latest information for policy makers in national development and budget allocation. The aim of this study is to compare two projection methods for projecting inputoutput tables for Malaysia