Solving Examination Timetabling Problem using Partial Exam Assignment with Great Deluge Algorithm

Constructing a quality solution for the examination timetable problem is a difficult task. This paper presents a partial exam assignment approach with great deluge algorithm as the improvement mechanism in order to generate good quality timetable. In this approach, exams are ordered based on graph heuristics and only selected exams (partial exams) are scheduled first and then improved using great deluge algorithm. The entire process continues until all of the exams have been scheduled. We implement the proposed technique on the Toronto benchmark datasets. Experimental results indicate that in all problem instances, this proposed method outperforms traditional great deluge algorithm and when comparing with the state-of-the-art approaches, our approach produces competitive solution for all instances, with some cases outperform other reported result

Global Optimization Using Local Search Approach for Course Scheduling Problem

Course scheduling problem is a combinatorial optimization problem which is defined over a finite discrete problem whose candidate solution structure is expressed as a finite sequence of course events scheduled in available time and space resources. This problem is considered as non-deterministic polynomial complete problem which is hard to solve. Many solution methods have been studied in the past for solving the course scheduling problem, namely from the most traditional approach such as graph coloring technique; the local search family such as hill-climbing search, taboo search, and simulated annealing technique; and various population-based metaheuristic methods such as evolutionary algorithm, genetic algorithm, and swarm optimization. This article will discuss these various probabilistic optimization methods in order to gain the global optimal solution. Furthermore, inclusion of a local search in the population-based algorithm to improve the global solution will be explained rigorously

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

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

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

Examination timetabling automation using hybrid meta-heuristics

Trabalho de projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de ComputadoresNos últimos anos, o tema da geração automática de horários tem sido alvo de muito estudo. Em muitas instituições, a elaboração de horários ainda é feita manualmente, constituindo-se uma tarefa demorada e penosa para instâncias de grande dimensão. Outro problema recorrente na abordagem manual é a existência de falhas dada a dificuldade do processo de verificação, e também a qualidade final do horário produzido. Se este fosse criado por computador, o horário seria válido e seriam de esperar horários com qualidade superior dada a capacidade do computador para pesquisar o espaço de soluções. A elaboração de horários não é uma tarefa fácil, mesmo para uma máquina. Por exemplo, horários escolares necessitam de seguir certas regras para que seja possível a criação de um horário válido. Mas como o espaço de estados (soluções) válidas é tão vasto, é impraticável criar um algoritmo que faça a enumeração completa de soluções a fim de escolher a melhor solução possível. Por outro lado, a utilização de algoritmos que realizam a enumeração implícita de soluções (por exemplo, branch and bound), não é viável para problemas de grande dimensão. A utilização de heurísticas que percorrem de uma forma guiada o espaço de estados, conseguindo assim uma solução razoável em tempo útil, constituem uma abordagem adequada para este tipo de problemas. Um dos objetivos do projeto consiste na criação duma abordagem que siga as regras do International Timetabling Competition (ITC) 2007 incidindo na criação de horários de exames em universidades (Examination timetabling track). Este projeto utiliza uma abordagem de heurísticas híbridas. Isto significa que utiliza múltiplas heurísticas para obter a melhor solução possível. Utiliza uma variação da heurística de Graph Coloring para obter uma solução válida e as meta-heurísticas Simulated Annealing e Hill Climbing para melhorar a solução obtida. Os resultados finais são satisfatórios, pois em algumas instâncias os resultados são melhores do que alguns dos cinco finalistas do concurso ITC 2007.Abstract: In the last few years the automatic creation of timetables is being a well-studied subject. In many institutions, the elaboration of timetables is still manual, thus being a time-consuming and difficulty task for large instances. Another current problem in the manual approach is the existence of failures given the difficulty in the process verification, and so the quality of the produced timetable. If this timetable had been created by a computer, the timetable would be valid and timetables with better quality should be obtained, given the computer’s capacity to search the solution space. It is not easy to elaborate timetables, even for a machine. For example, scholar/university timetables need to follow certain type of constraints or rules for them to be considered valid. But since the solution space is so vast, it is highly unlikely to create an algorithm that completely enumerates the solutions in order to choose the best solution possible, considering the problem structure. The use of algorithms that perform implicit enumeration solutions (for example, an branch bound), is not feasible for large problems. Hence the use of heuristics which navigate through the solution space in a guided way, obtaining then a reasonable solution in acceptable time. One main objective of this project consists in creating an approach that follows the International Timetabling Competition (ITC) 2007 rules, focusing on creating examination timetables. This project will use a hybrid approach. This means it will use an approach that includes multiple heuristics in order to find the best possible solution. This approach uses a variant of the Graph Coloring heuristic to find an initial valid solution, and the metaheuristics Simulated Annealing and Hill Climbing to improve that solution. The final results are satisfactory, as in some instances the obtained results beat the results of some of the five finalists from ITC 2007

Companion PASS Program

At the end of each quarter the students of Cal Poly prepare to sign up for their classes for next quarter. This process is more difficult for seniors who must meet very specific requirements in order to graduate on time. Students can spend hours trying to find the “perfect schedule”, that one schedule that fits their personal schedule and their graduation requirements, while not over taxing themselves during that quarter by having too much class work. This Cal Poly explores the creation and implementation of an extra scheduling tool to address the weaknesses of the current scheduling system, PASS, and help students reduce the amount of time spent looking for that one “perfect schedule”. The companion program makes use of MS Access and MS Excel, both common Office Programs, to create a system that offers more optimization options in choosing what classes a student would like to take for the next quarter. Working with several queries in Access and then changing those queries into constraints for Excel solver this Senior Project was able to create an ‘alpha’ level companion program that is function, but not fully tested or edited for ease of use by senior level industrial engineering students at Cal Poly

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

Scheduling Problems

Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems