Adaptive Decision Support for Academic Course Scheduling Using Intelligent Software Agents

Academic course scheduling is a complex operation that requires the interaction between different users including instructors and course schedulers to satisfy conflicting constraints in an optimal manner. Traditionally, this problem has been addressed as a constraint satisfaction problem where the constraints are stationary over time. In this paper, we address academic course scheduling as a dynamic decision support problem using an agent-enabled adaptive decision support system. In this paper, we describe the Intelligent Agent Enabled Decision Support (IAEDS) system, which employs software agents to assist humans in making strategic decisions under dynamic and uncertain conditions. The IAEDS system has a layered architecture including different components such as a learning engine that uses historic data to improve decision-making and an intelligent applet base that provides graphical interface templates to users for frequently requested decision-making tasks. We illustrate an application of our IAEDS system where agents are used to make complex scheduling decisions in a dynamically changing environment

Developing novel meta-heuristic, hyper-heuristic and cooperative search for course timetabling problems

The research presented in this PhD thesis focuses on the problem of university course timetabling, and examines the various ways in which metaheuristics, hyperheuristics and cooperative heuristic search techniques might be applied to this sort of problem. The university course timetabling problem is an NP-hard and also highly constrained combinatorial problem. Various techniques have been developed in the literature to tackle this problem. The research work presented in this thesis approaches this problem in two stages. For the first stage, the construction of initial solutions or timetables, we propose four hybrid heuristics that combine graph colouring techniques with a well-known local search method, tabu search, to generate initial feasible solutions. Then, in the second stage of the solution process, we explore different methods to improve upon the initial solutions. We investigate techniques such as single-solution metaheuristics, evolutionary algorithms, hyper-heuristics with reinforcement learning, cooperative low-level heuristics and cooperative hyper-heuristics. In the experiments throughout this thesis, we mainly use a popular set of benchmark instances of the university course timetabling problem, proposed by Socha et al. [152], to assess the performance of the methods proposed in this thesis. Then, this research work proposes algorithms for each of the two stages, construction of initial solutions and solution improvement, and analyses the proposed methods in detail. For the first stage, we examine the performance of the hybrid heuristics on constructing feasible solutions. In our analysis of these algorithms we discovered that these hybrid approaches are capable of generating good quality feasible solutions in reasonable computation time for the 11 benchmark instances of Socha et al. [152]. Just for this first stage, we conducted a second set of experiments, testing the proposed hybrid heuristics on another set of benchmark instances corresponding to the international timetabling competition 2002 [91J. Our hybrid construction heuristics were also capable of producing feasible solutions for the 20 instances of the competition in reasonable computation time. It should be noted however, that most of the research presented here was focused on the 11 problem instances of Socha et al. [152]. For the second stage, we propose new metaheuristic algorithms and cooperative hyper-heuristics, namely a non-linear great deluge algorithm, an evolutionary nonlinear great deluge algorithm (with a number of new specialised evolutionary operators), a hyper-heuristic with a learning mechanism approach, an asynchronous cooperative low-level heuristic and an asynchronous cooperative hyper-heuristic. These two last algorithms were inspired by the particle swarm optimisation technique. Detailed analyses of the proposed algorithms are presented and their relative benefits discussed. Finally, we give our suggestions as to how our best performing algorithms might be modified in order to deal with a wide range of problem domains including more real-world constraints. We also discuss the drawbacks of our algorithms in the final section of this thesis

Effective computational models for timetabling problem

Timetabling is a table of information showing when certain events are scheduled to take place. Timetabling is in fact very essential in making sure that all events occur in the time and place required. It is critical in areas such as: education, production and manufacturing, sport competitions, transport and logistics. The difficulty in timetabling is satisfying all the restrictions and requirements. The restrictions relate to resources such as time and location as well as conflicts. The requirements relate to the preferences of customers and service providers. The problem is further complicated by the desire to optimize an objective function that usually relates to the cost or effectiveness of the schedule. The task of how to construct a high quality timetable which satisfies all requirements is definitely not an easy one. A further difficulty is the dynamic aspect of timetabling and the need to accommodate changes after the schedule has been announced. Our focus in this study is on university timetabling problems.Mathematically, the problem is to optimize an objective function that reflects the value of the schedule subject to a set of constraints that relate to various operational requirements and a range of resource constraints (lecturers, rooms, etc). The usual objective is to maximize the total preferences or to minimize the number of students affected by clashes. The problem can be conveniently expressed as an Integer Programming (IP) problem. The computational difficulty is due to the integer restrictions on the variables. Various computational models including both heuristics and exact methods have been proposed.The timetabling problem in universities courses has existed for a long time, but due to the complexity and its variation, many researchers are still trying to decipher the solution for this problem. Numerous methods have been developed over the years and most of them have been successful. However, according to McCollum (2006) based on the international review of Operational Research in the UK (Commissioned by the Engineering and Physical Sciences Research Council), a gap still exists between the theory and practice of timetabling. Additionally, Burke and Petrovic (2002) also mentioned that many methods that have succeeded in solving this problem are applicable to specific institutions where they are designed. Nevertheless, Benli and Botsali (2004) explained that there is no generalized model for this problem because of the variation present in each university. Moreover, the limited availability of facilities and growth of flexibility of the student’s choices of courses makes the problem even tighter.This thesis in whole outlines studies which gain a step in a pathway to develop a more general IP model for university course timetabling problem. We incorporate all important features of this problem which includes the hard constraints and the desirable soft constraints. AIMMS 3.11 mathematical software is employed as a tool to solve the models with CPLEX 12.1 as the solver.In the first study (Chapter 3), we aim to develop models for timetabling problems which are flexible in terms of the ability to be applied in various institutions. To achieve this, we gather the information obtained on features that are used in other studies, which is covered in the literature review (Chapter 2) of this thesis. From the information on the gathered features, we observed that some features are compulsory, being that they are always used in models to solve timetabling problems. These features therefore form a basic model of university course timetabling problem in this study. We then develop an extended model by incorporating additional features found from the literature. The extended model also contains a few more additional features which we generate that are significant to be included in a model for solving this problem.Different combinations of the features which form the extended model are extracted to produce a range of models. These models are useful to be used by any institutions which require some relevant features to solve their timetabling problem. These models are tested with a small randomly generated test problem. In the following chapter (Chapter 4), we apply the developed model into 3 case studies obtained from the literature. The objective of this is to test the efficiency of the developed models for application to larger problems. Appropriate variation models are used to solve each of the case studies. This application testing is further extended by including a number of additional features. This is to illustrate that the developed model is able to be applied in institutions even ivwhen changes of requirements occur. Results from these tests demonstrate successful outcomes from application of our developed models to the chosen case studies.In Chapter 5, we tested the application of the developed models application in a case study using a pre-assignment approach as a simplification in solving timetabling problem. In this approach, the core units are determined and prioritized to be assigned into prime time slots at the very beginning of the scheduling process. It then follows with the assignment of the remainder units subject to the university requirements. One case study which is applied in Chapter 4 is used for the purpose of testing the pre-assignment approach. From this testing, we show that the pre-assignment is a useful simplification tool in solving timetabling problem of the chosen case study using the developed model, especially in reducing the computational time. We believe that this approach can be applied in other case studies using the developed model.As an overview of the thesis, we believe that the developed models will be applicable to other problems apart from the ones tested

Developing novel meta-heuristic, hyper-heuristic and cooperative search for course timetabling problems

The research presented in this PhD thesis focuses on the problem of university course timetabling, and examines the various ways in which metaheuristics, hyperheuristics and cooperative heuristic search techniques might be applied to this sort of problem. The university course timetabling problem is an NP-hard and also highly constrained combinatorial problem. Various techniques have been developed in the literature to tackle this problem. The research work presented in this thesis approaches this problem in two stages. For the first stage, the construction of initial solutions or timetables, we propose four hybrid heuristics that combine graph colouring techniques with a well-known local search method, tabu search, to generate initial feasible solutions. Then, in the second stage of the solution process, we explore different methods to improve upon the initial solutions. We investigate techniques such as single-solution metaheuristics, evolutionary algorithms, hyper-heuristics with reinforcement learning, cooperative low-level heuristics and cooperative hyper-heuristics. In the experiments throughout this thesis, we mainly use a popular set of benchmark instances of the university course timetabling problem, proposed by Socha et al. [152], to assess the performance of the methods proposed in this thesis. Then, this research work proposes algorithms for each of the two stages, construction of initial solutions and solution improvement, and analyses the proposed methods in detail. For the first stage, we examine the performance of the hybrid heuristics on constructing feasible solutions. In our analysis of these algorithms we discovered that these hybrid approaches are capable of generating good quality feasible solutions in reasonable computation time for the 11 benchmark instances of Socha et al. [152]. Just for this first stage, we conducted a second set of experiments, testing the proposed hybrid heuristics on another set of benchmark instances corresponding to the international timetabling competition 2002 [91J. Our hybrid construction heuristics were also capable of producing feasible solutions for the 20 instances of the competition in reasonable computation time. It should be noted however, that most of the research presented here was focused on the 11 problem instances of Socha et al. [152]. For the second stage, we propose new metaheuristic algorithms and cooperative hyper-heuristics, namely a non-linear great deluge algorithm, an evolutionary nonlinear great deluge algorithm (with a number of new specialised evolutionary operators), a hyper-heuristic with a learning mechanism approach, an asynchronous cooperative low-level heuristic and an asynchronous cooperative hyper-heuristic. These two last algorithms were inspired by the particle swarm optimisation technique. Detailed analyses of the proposed algorithms are presented and their relative benefits discussed. Finally, we give our suggestions as to how our best performing algorithms might be modified in order to deal with a wide range of problem domains including more real-world constraints. We also discuss the drawbacks of our algorithms in the final section of this thesis

A future workplace: headquarters of China Light and Power.

Fung Chi Ho Michael."Architecture Department, Chinese University of Hong Kong, Master of Architecture Programme 1996-97, design report."Includes bibliographical references.PrefaceAcknowledgeChapter 1.0 --- INTRODUCTIONChapter 1.1 --- Design MissionChapter 1.2 --- Design ObjectivesChapter 1.3 --- Client/usersChapter 1.4 --- NeedChapter 1.5 --- BriefChapter 1.6 --- Site IssuesChapter 1.7 --- Planning(Statutory)ConstraintsChapter 2.0 --- DESIGN/PLANNING/ZONING STRATEGY:Chapter 2.1 --- Design ConceptChapter 2.2 --- Zoning StrategyChapter 2.3 --- Transport: Pedestrian / Vehicular AccessChapter 2.4 --- Office Space OrganizationChapter 2.5 --- Design DevelopmentChapter 2.5 --- Final Design SolutionChapter 3.0 --- ENVIRONMENTAL CONCERN:Chapter 3.1 --- "Microclimate Concept (acoustics, ventilation, lighting, energy strategy)"Chapter 3.2 --- Services LayoutChapter 3.3 --- Computation Fluent Analysis (CFD)Chapter 4.0 --- LIFE SAFETY:Chapter 4.1 --- Means of EscapeChapter 4.2 --- Fire Fighting StrategyChapter 5.0 --- STRUCTURE:Chapter 5.1 --- Structural ConceptChapter 5.2 --- Computation Structural AnalysisChapter 5.3 --- Option StudiesChapter 6.0 --- CONSTRUCTION:Chapter 6.1 --- Construction SequenceChapter 6.2 --- Approach to External SkinChapter 6.3 --- Approach to MaintenanceChapter 7.0 --- MATERIAL SELECTION:Chapter 7.1 --- Insulating MechanismChapter 7.2 --- Material PropertiesChapter 8.0 --- COST:Chapter 8.1 --- Source of financialChapter 8.2 --- Cost AnalysisChapter 9.0 --- SPECIAL STUDY: DAYLIGHTING:Chapter 9.1 --- Day Lighting / Artificial Lighting ConceptChapter 9.2 --- Design CriteriaChapter 9.3 --- Computation Day Lighting Analysi

Information needs along the journey chain: users’ perspective about bus system

Buses constitute the main public transport mode in most cities of the world. Accessible Bus Systems are defined as systems that are easy to use. However accessible the infrastructure may be, it is unlikely to provide access if people cannot know about it. Therefore it is essential to have comprehensive and accessible information systems which describe the bus systems during all the stages of the journey. There is a widespread understanding amongst researchers that Information Systems can increase the efficiency of the system and that they should be oriented to meet bus users’ needs. However, existing information systems largely ignore the user’s point of view, in special the requirement of the disabled users. This thesis describes a methodology developed to investigate the problem of using information during a journey by bus in real conditions taking into account the (un)familiarity of the area in study and the individual’s previous knowledge of information system. Two main aspects are identified — the “Required Environment Capability” (the physical, social and psychological environment conditions) and the “Individual Capability Provided” (the individual ability in physical, sensorial and cognitive terms) to plan and execute a journey by bus in an unfamiliar environment. Because of the multidisciplinary aspect of the theme this study uses approaches from different fields of research to construct a methodology to understand individual information use. Based on the principles of Single Case Analysis adapted by adding the concept of the Capabilities Model (CM) (which explores interactions between individual and environment), the combined SCA/CM approach was employed to construct the INFOChain experiment. A set of information pieces were developed for the experiment, delivering Accessibility- Issues (AI-type) information in order to help older people to plan and execute different bus journeys in two different cities: London/UK and Brasilia/BR. General results have shown that although the AI-Type of information is considered important by older people, it needs more than simple expositions to actually take advantages of the information and be able to help disabled users

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques