The development of a general algorithmic procedure for university examination timetabling

The problem of scheduling university examinations is becoming difficult for examination officers especially when they have to construct the timetables manually. It is largely due to the increasing number of students and greater freedom in choosing the courses. Examination officers would have to spend a considerable amount of time checking for student conflicts so that no student would have to sit for more than one exam at any one time. There are also other limitations such as the number of examination rooms, the length of the examination period and others. The examination timetabling problem varies between institutions, depending on their particular needs and limited resources. Most of the existing computerised examination timetabling systems found in the literature are developed and used by particular institutions. Therefore, the aim of the research is to produce a general computerised system for timetabling examinations which can be used by most universities. The research is done in two stages; the first stage involves carrying out a survey on the university examination timetabling systems and the second stage is the construction of a university examination timetabler incorporating the common objectives and constraints found in the survey. The survey was carried out to determine the extent to which the computerised examination timetabling procedures are used, to identify the objectives and constraints which are commonly considered when constructing examination timetables and to evaluate the effectiveness of the existing examination timetabling systems in achieving the objectives and satisfying the constraints The construction of the general examination timetabling system is done in two parts. In the first part, a new algorithmic rule is developed to assign exams to the minimum number of sessions without creating conflicts for any student. The rule adopts a clique initialisation strategy as a starting point and a graph colouring approach for assigning the exams. This rule is also quite capable of scheduling exams to the sessions which are as close as to the least number of sessions possible, without having to carry out any backtracking process. The backtracking process can sometimes be time consuming if there are a lot of exams firstly to be scheduled, and secondly clashing with each other. The second part of the work involves minimising the total number of students taking two exams on the same day and scheduling large exams early in the examination period subject to a specified time limit on the overall examination period and a maximum number of students that may be examined in any session. A swapping rule was introduced where exams in one of the sessions in any day with large number of sameday exams are interchanged with exams in other sessions which will reduce the total number of same-day exams. The experimentation showed that if the swapping procedures are repeated three times, the total number of same-day exams will be reduced by 50%. The total number of same-day exams will be reduced even more if some extra sessions can be added to the initial minimum number of sessions. A simple rule was devised to schedule large exams early in the examination period

Metode Simulated Annealing untuk Optimasi Penjadwalan Ujian Perguruan Tinggi

Penjadwalan ujian (timetabling) merupakan penugasan mata kuliah yang diujikan yang diikuti oleh mahasiswa pada slot waktu dan ruang yang tersedia dengan melibatkan batasan tertentu. Simulated annealing merupakan salah satu metode heuristic yang dapat digunakan sebagai metode pencarian dan memberikan solusi yang dapat diterima (objective function) dengan hasil yang baik. Pada penelitian ini membahas tentang penjadwalan ujian pada perguruan tinggi menggunakan metode simulated annealing dengan menggunakan lima variabel data yaitu mata kuliah yang diujikan (X1), mahasiswa (X2), slot waktu yang terdiri dari hari (X3) dan waktu periode (X4) dan variabel ruang (X5). Penelitian ini memiliki dua objective function yang akan dihasilkan, pertama adalah penugasan mata kuliah yang diujikan yang diikuti oleh mahasiswa pada slot hari dan waktu yang tersedia, kedua penugasan hasil optimasi objective function 1 pada ruang yang tersedia. Objective function dihitungdengan memperhatikan batasan yang terlibat untuk menghasilkan solusi yang optimal. Penelitian ini melakukan uji coba terhadap metode simulated annealing dengan menghasilkan rata-rata varian sebesar 84,994% data dapat mencapai solusi dengan standar deviasi sebesar 1.0267. Pada penelitian ini diberikan metode solusi dalam penggunaan ruang pencarian yang tersisa untuk dapat digunakan kembali oleh data yang belum teralokasikan

Ordonnancement des trains dans une gare complexe et à forte densité de circulation

This thesis focuses on the trains platforming problem within busy and complex railway stations and aims to develop a computerized dispatching support tool for railway station dispatchers to generate a full-day conflict-free timetable. The management of rail traffic in stations requires careful scheduling to fit to the existing infrastructure, while avoiding conflicts between large numbers of trains and satisfying safety or business policy and objectives. Based on operations research techniques and professional railway expertise, we design a generalized mathematical model to formalize the trains platforming problem including topology of railway station, trains' activities, dispatching constraints and objectives. As a large-scale problem, full-day platforming problem is decomposed into tractable sub-problems in time order by cumulative sliding window algorithm. Each sub-problem is solved by branch-and-bound algorithm implemented in CPLEX. To accelerate calculation process of sub-problems, tri-level optimization model is designed to provide a local optimal solution in a rather short time. This local optimum is provided to branch-and bound algorithm as an initial solution.This system is able to verify the feasibility of tentative timetable given to railway station. Trains with unsolvable conflicts will return to their original activity managers with suggestions for the modification of arrival and departure times. Time deviations of commercial trains' activities are minimized to reduce the delay propagation within the whole railway networks.Cette thèse porte sur l'ordonnancement des trains dans les gares complexes en forte densité de circulation. L'objet se situe à la réalisation d'un outil pour aider les managers de la gare à générer un tableau des horaires sans-conflits dans un journée. Le management des circulations ferroviaires dans la gare demande l'ordonnancement soigneux pour adapter les ressources limités, en évitant les conflits entre les trains et satisfaisant l'objectif et les politiques économiques et de la sécurité en même temps. D'après les méthodes appliquées en recherche opérationnelle et les expériences professionnelles, une modèle mathématique applicable aux gares différentes est construit pour formaliser le problème de l'ordonnancement des trains contenant la topologie de la gare, activités des trains, contraintes de planification et objectives. Comme un problème à grande échelle, l'ordonnancement des trains dans un journée est décomposé en sous-problèmes traitables dans l'ordre du temps par sliding window algorithme accumulé. Chaque sous-problème est résolu par branch-and-bound de CPLEX. Afin d'accélérer le calcul des sous-problèmes, tri-level optimisation méthode est construit pour offrir une solution optimale locale dans un temps de calcul assez court. Cette solution est donnée à branch-and-bound comme une solution initiale.Ce système consiste à vérifier la faisabilité des horaires donnés à la gare. Les trains avec les conflits insolvables sont retournés à l'origine de ces trains avec les modifications des heures proposées. Déviations des trains commerciaux sont minimisées pour diminuer la propagation du délai dans le réseau ferroviaire

Models, solution methods and threshold behaviour for the teaching space allocation problem

Universities have to manage their teaching space, and plan future needs. Their efforts are frequently hampered by, capital and maintenance costs, on one hand, pedagogical and teaching services on the other. The efficiency of space usage, can be measured by the utilisation: the percentage of available seat-hours actually used. The observed utilisation, in many institutions, is unacceptably low, and this provides our main underlying motivation: To address and assess some of the major factors that affect teaching space usage in the hope of improving it in practise. Also, when performing space management, managers operate within a limited number and capacity of lecture theatres, tutorial rooms, etc. Hence, some teaching activities require splitting into different groups. For example, lectures being too large to fit in any one room and seminars/tutorials being taught in small groups for good teaching practise. This thesis forms the cornerstone of ongoing research to illuminate issues stemming from poorly utilised space and studies the nature of constraints that underlies those low levels of utilisation. We give quantitative evidence that constraints related to timetabling are major players in pushing down utilisation levels and also, devise "Dynamic Splitting" algorithms to illustrate the effects of splitting on utilisation levels. We showed the existence of threshold between phases where splitting and allocation is "always possible" to ones where "it's never possible", hence, introducing a practical application of Phase Transition to space planning and management. We have also worked on the long-term planning aspect of teaching space and proposed methods to improve the future expected utilisation

Energy-aware Occupancy Scheduling

Buildings are the largest consumers of energy worldwide. Within a building, heating, ventilation and air-conditioning (HVAC) systems consume the most energy, leading to trillion dollars of electrical expenditure worldwide each year. With rising energy costs and increasingly stringent regulatory environments, improving the energy efficiency of HVAC operations in buildings has become a global concern. From a short-term economic point-of-view, with over 100 billion dollars in annual electricity expenditures, even a small percentage improvement in the operation of HVAC systems can lead to significant savings. From a long-term point-of-view, the need of fostering a smart and sustainable built environment calls for the development of innovative HVAC control strategies in buildings. In this thesis, we look at the potential for integrating building operations with room booking and occupancy scheduling. More specifically, we explore novel approaches to reduce HVAC consumption in commercial buildings, by jointly optimising the occupancy scheduling decisions (e.g. the scheduling of meetings, lectures, exams) and the building’s occupancy-based HVAC control. Our vision is to integrate occupancy scheduling with HVAC control, in such a way that the energy consumption is reduced, while the occupancy thermal comfort and scheduling requirements are addressed. We identify four unique research challenges which we simultaneously tackle in order to achieve this vision, and which form the major contributions of this thesis. Our first contribution is an integrated model that achieves high efficiency in energy reduction by fully exploiting the capability to coordinate HVAC control and occupancy scheduling. The core component of our approach is a mixed-integer linear programming (MILP) model which optimally solves the joint occupancy scheduling and occupancy-based HVAC control problem. Existing approaches typically solve these subproblems in isolation: either scheduling occupancy given conventional control policies, or optimising HVAC control using a given occupancy schedule. From a computation standpoint, our joint problem is much more challenging than either, as HVAC models are traditionally non-linear and non-convex, and scheduling models additionally introduce discrete variables capturing the time slot and location at which each activity is scheduled. We find that substantial reduction in energy consumption can be achieved by solving the joint problem, compared to the state of the art approaches using heuristic scheduling solutions and to more naïve integrations of occupancy scheduling and occupancy-based HVAC control. Our second contribution is an approach that scales to large occupancy scheduling and HVAC control problems, featuring hundreds of activity requests across a large number of offices and rooms. This approach embeds the integrated MILP model into Large Neighbourhood Search (LNS). LNS is used to destroy part of the schedule and MILP is used to repair the schedule so as to minimise energy consumption. Given sets of occupancy schedules with different constrainedness and sets of buildings with varying thermal response, our model is sufficiently scalable to provide instantaneous and near-optimal solutions to problems of realistic size, such as those found in university timetabling. The third contribution is an online optimisation approach that models and solves the online joint HVAC control and occupancy scheduling problem, in which activity requests arrive dynamically. This online algorithm greedily commits to the best schedule for the latest activity requests, but revises the entire future HVAC control strategy each time it considers new requests and weather updates. We ensure that whilst occupants are instantly notified of the scheduled time and location for their requested activity, the HVAC control is constantly re-optimised and adjusted to the full schedule and weather updates. We demonstrate that, even without prior knowledge of future requests, our model is able to produce energy-efficient schedules which are close to the clairvoyant solution. Our final contribution is a robust optimisation approach that incorporates adaptive comfort temperature control into our integrated model. We devise a robust model that enables flexible comfort setpoints, encouraging energy saving behaviors by allowing the occupants to indicate their thermal comfort flexibility, and providing a probabilistic guarantee for the level of comfort tolerance indicated by the occupants. We find that dynamically adjusting temperature setpoints based on occupants’ thermal acceptance level can lead to significant energy reduction over the conventional fixed temperature setpoints approach. Together, these components deliver a complete optimisation solution that is efficient, scalable, responsive and robust for online HVAC-aware occupancy scheduling in commercial buildings

Enhancing Decision Support Systems for Airport Slot Allocation

Due to the growing imbalance between air traffic demand and airport capacity at congested airports, airlines must secure slots to operate flights at capacity-constrained airports. In practice, slot allocation is performed by independent slot coordinators at each airport according to a set of principles and regulations. As a result, the current decision-making system is considered inefficient and does not take adequate account of the complexity of real-world problems. Therefore, optimisation techniques are needed to improve airport capacity management and slot allocation. This thesis aims to contribute to single airport slot allocation research by providing an in-depth analysis of the slot request data and developing new models and solution algorithms to deal with large-scale slot allocation problems. First, we propose a new model considering slot rejections (SASA-R) based on the maximum acceptable displacement of slots to support the decision-making of rejecting slots. In addition, we analyse the impact of changing the current slot allocation rules on slot allocation results. Second, we propose a two-stage approach that aims to solve large-scale slot allocation problems. A greedy constructive heuristic is developed to generate feasible solutions in a short time. This initial feasible solution is then improved by an adaptive large neighbourhood search heuristic (ALNS). A novel related destroy operator is designed specifically for this problem. The results show high-quality solutions can be obtained within a few hours for the problem instance tested, while a commercial optimisation solver does not return a feasible solution after several days of computation. Third, we propose a flexible slot allocation model to allocate slots individually on different days of the week. This model enhances existing models by enabling coordinators to explore the trade-off between schedule regularity and flexibility. The results show that the flexible scheduler can simultaneously reduce the number of rejected slots and schedule displacement

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Intelligent real-time train rescheduling management for railway system

The issue of managing a large and complex railway system with continuous traffic flows and mixed train services in a safe and punctual manner is very important, especially after disruptive events. In the first part of this thesis an analysis method is introduced which allows the visualisation and measurement of the propagation of delays in the railway network. The BRaVE simulator and the University of Birmingham Single Train Simulator (STS) are also introduced and a train running estimation using STS is described. A practical single junction rescheduling problem is then defined and it investigates how different levels of delays and numbers of constraints may affect the performance of algorithms for network-wide rescheduling in terms of quality of solution and computation time. In order to deal with operational dynamics, a methodology using performance-based supervisory control is proposed to provide rescheduling decisions over a wider area through the application of different rescheduling strategies in appropriate sequences. Finally, an architecture for a real-time train rescheduling framework, based on the distributed artificial intelligence system, is designed in order to handle railway traffic in a large-scale network intelligently. A case study based on part of the East Coast Main Line is followed up to demonstrate the effectiveness of adopting supervisory control to provide the rescheduling options in the dynamic situation

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order