Design, Engineering, and Experimental Analysis of a Simulated Annealing Approach to the Post-Enrolment Course Timetabling Problem

The post-enrolment course timetabling (PE-CTT) is one of the most studied timetabling problems, for which many instances and results are available. In this work we design a metaheuristic approach based on Simulated Annealing to solve the PE-CTT. We consider all the different variants of the problem that have been proposed in the literature and we perform a comprehensive experimental analysis on all the public instances available. The outcome is that our solver, properly engineered and tuned, performs very well on all cases, providing the new best known results on many instances and state-of-the-art values for the others

Reinforced Island Model Genetic Algorithm to Solve University Course Timetabling

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, departments, etc. This problem becomes complicated for universities which have immense number of students and lecturers. Therefore, a scalable and reliable timetabling solver is needed. However, current solvers and generic solution failed to meet several specific UCTP. Moreover, some universities implement student sectioning problem with individual student specific constraints. This research introduces the Reinforced Asynchronous Island Model Genetic Algorithm (RIMGA) to optimize the resource usage of the computer. RIMGA will configure the slave that has completed its process to helping other machines that have yet to complete theirs. This research shows that RIMGA not only improves time performance in the computational execution process, it also oers greater opportunity to escape the local optimum trap than previous model

Decision support tool for Operations Management course and instructor scheduling

The goal of this project is develop a decision support tool that will assist the Operations Management Department at the University of Arkansas with scheduling courses and instructors for the upcoming academic year. The staff of the department dreads this time each year because it takes countless hours to complete the daunting task. Creating an abstract mathematical model will assist the department in scheduling the courses. The model will have the ability to optimize the schedule of the courses and instructors from a large number of variables and constraints that the department requires. An optimization software package can solve the problem based on the data for the upcoming year. The staff will be able to use a decision support tool to input the relevant data with ease, and run the optimization software package with little knowledge of how mathematical models work. The focus of this project will be creating an abstract class-scheduling mathematical model that will be easily solved through the creation of a decision support tool. The tool will optimize the schedule, and save the staff precious time that could be spent elsewhere

Course Time Table Scheduling for a Local College

This study dive into the field of course time table scheduling for a local institution. The subject of the study will be a local college in Malaysia, in particular on the SEGi College branch in Penang. This covers the development of the prototype software which will enable the simulation of the course time table for both the students and lecturers. The prototype software will be on a local search approach with reference to Hill Climbing with Random Walk algorithm and Best First Search algorithm. This research enables users to increase efficiency and performance in developing a course time table. Later,this research will be proposed for implementation to the management of SEGi College branch in Penang

A general framework of multi-population methods with clustering in undetectable dynamic environments

Copyright @ 2011 IEEETo solve dynamic optimization problems, multiple population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different sub-areas in the fitness landscape. Many experimental studies have shown that locating and tracking multiple relatively good optima rather than a single global optimum is an effective idea in dynamic environments. However, several challenges need to be addressed when multi-population methods are applied, e.g., how to create multiple populations, how to maintain them in different sub-areas, and how to deal with the situation where changes can not be detected or predicted. To address these issues, this paper investigates a hierarchical clustering method to locate and track multiple optima for dynamic optimization problems. To deal with undetectable dynamic environments, this paper applies the random immigrants method without change detection based on a mechanism that can automatically reduce redundant individuals in the search space throughout the run. These methods are implemented into several research areas, including particle swarm optimization, genetic algorithm, and differential evolution. An experimental study is conducted based on the moving peaks benchmark to test the performance with several other algorithms from the literature. The experimental results show the efficiency of the clustering method for locating and tracking multiple optima in comparison with other algorithms based on multi-population methods on the moving peaks benchmark

Knowing when to target students with timely academic learning support: not a minefield with data mining

The strategic scheduling of timely engagement opportunities with academic learning support, targeting specific student cohorts requires intentional, informed and coordinated planning. Currently these timing decisions appear to be made with a limited student focus, which considers individual course units only as opposed to having an awareness of the schedule constraints imposed by the students’ full course workload. Hence, in order to respect the full student academic workload, and maximise the quantity and quality of opportunities for students to engage with learning advisors, a means to capture and work with the composition and distribution of student full workload is needed. A data mining approach is proposed in this concise paper, where public domain information accessed from the back end HTML language of course unit information webpages is collected and consolidated in graphical form. The resulting visualisation of the students’ academic learning activities provides a quick and convenient means for academics to make informed scheduling decisions. The case study presented describes the implementation of the data mining in the context of discipline specific academic learning advisors at the University of Southern Queensland servicing three campuses under the ‘One-University’ model

Cobi: A Community-Informed Conference Scheduling Tool

Effectively planning a large multi-track conference requires an understanding of the preferences and constraints of organizers, authors, and attendees. Traditionally, the onus of scheduling the program falls on a few dedicated organizers. Resolving conflicts becomes difficult due to the size and complexity of the schedule and the lack of insight into community members ’ needs and desires. Cobi presents an alternative approach to conference scheduling that engages the entire community in the planning process. Cobi comprises (a) communitysourcing applications that collect preferences, constraints, and affinity data from community members, and (b) a visual scheduling interface that combines communitysourced data and constraint-solving to enable organizers to make informed improvements to the schedule. This paper describes Cobi’s scheduling tool and reports on a live deployment for planning CHI 2013, where organizers considered input from 645 authors and resolved 168 scheduling conflicts. Results show the value of integrating community input with an intelligent user interface to solve complex planning tasks. Author Keywords Cobi; conference scheduling; mixed-initiative; constrain