Development and implementation of a computer-aided method for planning resident shifts in a hospital

Ce mémoire propose une formulation pour le problème de confection d'horaire pour résidents, un problème peu étudiée dans la litérature. Les services hospitaliers mentionnés dans ce mémoire sont le service de pédiatrie du CHUL (Centre Hospitalier de l'Université Laval) et le service des urgences de l'Hôpital Enfant-Jésus à Québec. La contribution principale de ce mémoîre est la proposition d'un cadre d'analyse pour l’analyse de techniques manuelles utilisées dans des problèmes de confection d'horaires, souvent décrits comme des problèmes d'optimisation très complexes. Nous montrons qu'il est possible d'utiliser des techniques manuelles pour établir un ensemble réduit de contraintes sur lequel la recherche d’optimisation va se focaliser. Les techniques utilisées peuvent varier d’un horaire à l’autre et vont déterminer la qualité finale de l’horaire. La qualité d’un horaire est influencée par les choix qu’un planificateur fait dans l’utilisation de techniques spécifiques; cette technique reflète alors la perception du planificateur de la notion qualité de l’horaire. Le cadre d’analyse montre qu'un planificateur est capable de sélectionner un ensemble réduit de contraintes, lui permettant d’obtenir des horaires de très bonne qualité. Le fait que l'approche du planificateur est efficace devient clair lorsque ses horaires sont comparés aux solutions heuristiques. Pour ce faire, nous avons transposées les techniques manuelles en un algorithme afin de comparer les résultats avec les solutions manuelles. Mots clés: Confection d’horaires, Confection d’horaires pour résidents, Creation manuelle d’horaires, Heuristiques de confection d’horaires, Méthodes de recherche localeThis thesis provides a problem formulation for the resident scheduling problem, a problem on which very little research has been done. The hospital departments mentioned in this thesis are the paediatrics department of the CHUL (Centre Hospitalier de l’Université Laval) and the emergency department of the Hôpital Enfant-Jésus in Québec City. The main contribution of this thesis is the proposal of a framework for the analysis of manual techniques used in scheduling problems, often described as highly constrained optimisation problems. We show that it is possible to use manual scheduling techniques to establish a reduced set of constraints to focus the search on. The techniques used can differ from one schedule type to another and will determine the quality of the final solution. Since a scheduler manually makes the schedule, the techniques used reflect the scheduler’s notion of schedule quality. The framework shows that a scheduler is capable of selecting a reduced set of constraints, producing manual schedules that often are of very high quality. The fact that a scheduler’s approach is efficient becomes clear when his schedules are compared to heuristics solutions. We therefore translated the manual techniques into an algorithm so that the scheduler’s notion of schedule quality was used for the local search and show the results that were obtained. Key words: Timetable scheduling, Resident scheduling, Manual scheduling, Heuristic schedule generation, Local search method

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

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis

An investigation of novel approaches for optimising retail shelf space allocation

This thesis is concerned with real-world shelf space allocation problems that arise due to the conflict of limited shelf space availability and the large number of products that need to be displayed. Several important issues in the shelf space allocation problem are identified and two mathematical models are developed and studied. The first model deals with a general shelf space allocation problem while the second model specifically concerns shelf space allocation for fresh produce. Both models are closely related to the knapsack and bin packing problem. The thesis firstly studies a recently proposed generic search technique, hyper-heuristics, and introduces a simulated annealing acceptance criterion in order to improve its performance. The proposed algorithm, called simulated annealing hyper-heuristics, is initially tested on the one-dimensional bin packing problem, with very promising and competitive results being produced. The algorithm is then applied to the general shelf space allocation problem. The computational results show that the proposed algorithm is superior to a general simulated annealing algorithm and other types of hyper-heuristics. For the test data sets used in the thesis, the new approach solves every instance to over 98% of the upper bound which was obtained via a two-stage relaxation method. The thesis also studies and formulates a deterministic shelf space allocation and inventory model specifically for fresh produce. The model, for the first time, considers the freshness condition as an important factor in influencing a product's demand. Further analysis of the model shows that the search space of the problem can be reduced by decomposing the problem into a nonlinear knapsack problem and a single-item inventory problem that can be solved optimally by a binary search. Several heuristic and meta-heuristic approaches are utilised to optimise the model, including four efficient gradient based constructive heuristics, a multi-start generalised reduced gradient (GRG) algorithm, simulated annealing, a greedy randomised adaptive search procedure (GRASP) and three different types of hyper-heuristics. Experimental results show that the gradient based constructive heuristics are very efficient and all meta-heuristics can only marginally improve on them. Among these meta-heuristics, two simulated annealing based hyper-heuristic performs slightly better than the other meta-heuristic methods. Across all test instances of the three problems, it is shown that the introduction of simulated annealing in the current hyper-heuristics can indeed improve the performance of the algorithms. However, the simulated annealing hyper-heuristic with random heuristic selection generally performs best among all the other meta-heuristics implemented in this thesis. This research is funded by the Engineering and Physical Sciences Research Council (EPSRC) grant reference GR/R60577. Our industrial collaborators include Tesco Retail Vision and SpaceIT Solutions Ltd

Modelling energy efficiency for computation

In the last decade, efficient use of energy has become a topic of global significance, touching almost every area of modern life, including computing. From mobile to desktop to server, energy efficiency concerns are now ubiquitous. However, approaches to the energy problem are often piecemeal and focus on only one area for improvement. I argue that the strands of the energy problem are inextricably entangled and cannot be solved in isolation. I offer a high-level view of the problem and, building from it, explore a selection of subproblems within the field. I approach these with various levels of formality, and demonstrate techniques to make improvements on all levels.Clare College Domestic Research Scholarshi

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

Physical layer security for IoT applications

The increasing demands for Internet of things (IoT) applications and the tremendous increase in the volume of IoT generated data bring novel challenges for the fifth generation (5G) network. Verticals such as e-Health, vehicle to everything (V2X) and unmanned aerial vehicles (UAVs) require solutions that can guarantee low latency, energy efficiency,massive connectivity, and high reliability. In particular, finding strong security mechanisms that satisfy the above is of central importance for bringing the IoT to life. In this regards, employing physical layer security (PLS) methods could be greatly beneficial for IoT networks. While current security solutions rely on computational complexity, PLS is based on information theoretic proofs. By removing the need for computational power, PLS is ideally suited for resource constrained devices. In detail, PLS can ensure security using the inherit randomness already present in the physical channel. Promising schemes from the physical layer include physical unclonable functions (PUFs), which are seen as the hardware fingerprint of a device, and secret key generation (SKG) from wireless fading coefficients, which provide the wireless fingerprint of the communication channel between devices. The present thesis develops several PLS-based techniques that pave the way for a new breed of latency-aware, lightweight, security protocols. In particular, the work proposes: i) a fast multi-factor authentication solution with verified security properties based on PUFs, proximity detection and SKG; ii) an authenticated encryption SKG approach that interweaves data transmission and key generation; and, iii) a set of countermeasures to man-in-the-middle and jamming attacks. Overall, PLS solutions show promising performance, especially in the context of IoT applications, therefore, the advances in this thesis should be considered for beyond-5G networks