Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

Optimal Participation of Power Generating Companies in a Deregulated Electricity Market

The function of an electric utility is to make stable electric power available to consumers in an efficient manner. This would include power generation, transmission, distribution and retail sales. Since the early nineties however, many utilities have had to change from the vertically integrated structure to a deregulated system where the services were unbundled due to a rapid demand growth and need for better economic benefits. With the unbundling of services came competition which pushed innovation and led to the improvement of efficiency. In a deregulated power system, power generators submit offers to sell energy and operating reserve in the electricity market. The market can be described more as oligopolistic with a System Operator in-charge of the power grid, matching the offers to supply with the bid in demands to determine the market clearing price for each interval. This price is what is paid to all generators. Energy is sold in the day-ahead market where offers are submitted hours prior to when it is needed. The spot energy market caters to unforeseen rise in load demand and thus commands a higher price for electrical energy than the day-ahead market. A generating company can improve its profit by using an appropriate bidding strategy. This improvement is affected by the nature of bids from competitors and uncertainty in demand. In a sealed bid auction, bids are submitted simultaneously within a timeframe and are confidential, thus a generator has no information on rivals’ bids. There have been studies on methods used by generators to build optimal offers considering competition. However, many of these studies base estimations of rivals’ behaviour on analysis with sufficient bidding history data from the market. Historical data on bidding behaviour may not be readily available in practical systems. The work reported in this thesis explores ways a generator can make security-constrained offers in different markets considering incomplete market information. It also incorporates possible uncertainty in load forecasts. The research methodology used in this thesis is based on forecasting and optimization. Forecasts of market clearing price for each market interval are calculated and used in the objective function of profit maximization to get maximum benefit at the interval. Making these forecasts includes competition into the bid process. Results show that with information on historical data available, a generator can make adequate short-term analysis on market behaviour and thus optimize its benefits for the period. This thesis provides new insights into power generators’ approach in making optimal bids to maximize market benefits

Gestion flexible des ressources dans les réseaux de nouvelle génération avec SDN

Abstract : 5G and beyond-5G/6G are expected to shape the future economic growth of multiple vertical industries by providing the network infrastructure required to enable innovation and new business models. They have the potential to offer a wide spectrum of services, namely higher data rates, ultra-low latency, and high reliability. To achieve their promises, 5G and beyond-5G/6G rely on software-defined networking (SDN), edge computing, and radio access network (RAN) slicing technologies. In this thesis, we aim to use SDN as a key enabler to enhance resource management in next-generation networks. SDN allows programmable management of edge computing resources and dynamic orchestration of RAN slicing. However, achieving efficient performance based on SDN capabilities is a challenging task due to the permanent fluctuations of traffic in next-generation networks and the diversified quality of service requirements of emerging applications. Toward our objective, we address the load balancing problem in distributed SDN architectures, and we optimize the RAN slicing of communication and computation resources in the edge of the network. In the first part of this thesis, we present a proactive approach to balance the load in a distributed SDN control plane using the data plane component migration mechanism. First, we propose prediction models that forecast the load of SDN controllers in the long term. By using these models, we can preemptively detect whether the load will be unbalanced in the control plane and, thus, schedule migration operations in advance. Second, we improve the migration operation performance by optimizing the tradeoff between a load balancing factor and the cost of migration operations. This proactive load balancing approach not only avoids SDN controllers from being overloaded, but also allows a judicious selection of which data plane component should be migrated and where the migration should happen. In the second part of this thesis, we propose two RAN slicing schemes that efficiently allocate the communication and the computation resources in the edge of the network. The first RAN slicing scheme performs the allocation of radio resource blocks (RBs) to end-users in two time-scales, namely in a large time-scale and in a small time-scale. In the large time-scale, an SDN controller allocates to each base station a number of RBs from a shared radio RBs pool, according to its requirements in terms of delay and data rate. In the short time-scale, each base station assigns its available resources to its end-users and requests, if needed, additional resources from adjacent base stations. The second RAN slicing scheme jointly allocates the RBs and computation resources available in edge computing servers based on an open RAN architecture. We develop, for the proposed RAN slicing schemes, reinforcement learning and deep reinforcement learning algorithms to dynamically allocate RAN resources.La 5G et au-delà de la 5G/6G sont censées dessiner la future croissance économique de multiples industries verticales en fournissant l'infrastructure réseau nécessaire pour permettre l'innovation et la création de nouveaux modèles économiques. Elles permettent d'offrir un large spectre de services, à savoir des débits de données plus élevés, une latence ultra-faible et une fiabilité élevée. Pour tenir leurs promesses, la 5G et au-delà de la-5G/6G s'appuient sur le réseau défini par logiciel (SDN), l’informatique en périphérie et le découpage du réseau d'accès (RAN). Dans cette thèse, nous visons à utiliser le SDN en tant qu'outil clé pour améliorer la gestion des ressources dans les réseaux de nouvelle génération. Le SDN permet une gestion programmable des ressources informatiques en périphérie et une orchestration dynamique de découpage du RAN. Cependant, atteindre une performance efficace en se basant sur le SDN est une tâche difficile due aux fluctuations permanentes du trafic dans les réseaux de nouvelle génération et aux exigences de qualité de service diversifiées des applications émergentes. Pour atteindre notre objectif, nous abordons le problème de l'équilibrage de charge dans les architectures SDN distribuées, et nous optimisons le découpage du RAN des ressources de communication et de calcul à la périphérie du réseau. Dans la première partie de cette thèse, nous présentons une approche proactive pour équilibrer la charge dans un plan de contrôle SDN distribué en utilisant le mécanisme de migration des composants du plan de données. Tout d'abord, nous proposons des modèles pour prédire la charge des contrôleurs SDN à long terme. En utilisant ces modèles, nous pouvons détecter de manière préemptive si la charge sera déséquilibrée dans le plan de contrôle et, ainsi, programmer des opérations de migration à l'avance. Ensuite, nous améliorons les performances des opérations de migration en optimisant le compromis entre un facteur d'équilibrage de charge et le coût des opérations de migration. Cette approche proactive d'équilibrage de charge permet non seulement d'éviter la surcharge des contrôleurs SDN, mais aussi de choisir judicieusement le composant du plan de données à migrer et l'endroit où la migration devrait avoir lieu. Dans la deuxième partie de cette thèse, nous proposons deux mécanismes de découpage du RAN qui allouent efficacement les ressources de communication et de calcul à la périphérie des réseaux. Le premier mécanisme de découpage du RAN effectue l'allocation des blocs de ressources radio (RBs) aux utilisateurs finaux en deux échelles de temps, à savoir dans une échelle de temps large et dans une échelle de temps courte. Dans l’échelle de temps large, un contrôleur SDN attribue à chaque station de base un certain nombre de RB à partir d'un pool de RB radio partagé, en fonction de ses besoins en termes de délai et de débit. Dans l’échelle de temps courte, chaque station de base attribue ses ressources disponibles à ses utilisateurs finaux et demande, si nécessaire, des ressources supplémentaires aux stations de base adjacentes. Le deuxième mécanisme de découpage du RAN alloue conjointement les RB et les ressources de calcul disponibles dans les serveurs de l’informatique en périphérie en se basant sur une architecture RAN ouverte. Nous développons, pour les mécanismes de découpage du RAN proposés, des algorithmes d'apprentissage par renforcement et d'apprentissage par renforcement profond pour allouer dynamiquement les ressources du RAN

Acuity-based Performance Evaluation and Tactical Capacity Planning in Primary Care

Effective primary care requires timely and equitable access to care for patients as well as efficient and balanced utilization of physician time. Motivated by a family health clinic in Ontario, Canada, this research proposes ways to improve both of these aspects of primary care through tactical capacity planning based on acuity-based performance targets. First, we propose a new metric based on acuity levels to evaluate timely access to primary care. In Canada, as well as other participant countries in the Organization for Economic Co-operation and Development (OECD), the main metric currently used to evaluate access is the proportion of patients who are able to obtain a same- or next-day appointment. However, not all patients in primary care are urgent and require a same- or next-day appointment. Therefore, accurate evaluation of timely access to primary care should consider the urgency of the patient request. To address this need, we define multiple acuity levels and relative access targets in primary care, akin to the CTAS system in emergency care. Furthermore, current access time evaluation in the province is mostly survey-based, while our evaluation is based on appointment data and hence more objective. Thus, we propose a novel, acuity-based, data-driven approach for evaluation of timely access to primary care. Second, we develop a deterministic tactical capacity planning (TCP) model to balance workload between weeks for each family physician in the specific primary care clinic in this study. Unbalanced workload among weeks may lead to provider overtime for the weeks with high workload and provider idle time for weeks with low workload. In the proposed TCP model, we incorporate the results from access time evaluation in the first study as constraints for access time. The proposed TCP model considers 11 appointment types with multiple access targets for each appointment type. The TCP model takes as input a forecast of demand coming from an ARIMA model. We compare the results of the TCP model based on current access time targets as well as targets resulting from our acuity-based metrics. The use of our proposed acuity-based targets leads to allocation of time slots which is more equitable for patients and also improves physician workload balance. Third, we also propose a robust TCP model based on the cardinality-constrained method to minimize the highest potential physician peak load between weeks. Therefore, the developed robust TCP model enables protection against uncertainty through providing a feasible allocation of capacity for all realizations of demand. The proposed robust TCP model considers two interdependent appointment types (e.g., new patients and follow ups), multiple access time targets for each appointment type and uncertainty in demand for appointments. We conduct a set of experiments to determine how to set the level of robustness based on extra cost and infeasibility probability of a robust solution. In summary, this dissertation advocates for the definition and subsequent use of acuity-based access time targets for both performance evaluation and capacity allocation in primary care. The resulting performance metrics provide a more detailed view of primary care and lead to not only more equitable access policies but also have the potential to improve physician workload balance when used as input to capacity planning models