Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to minimize operation-cost expectation while satisfying reliability-related constraints. We propose a distributed scenario-based chance-constrained optimization formulation for this problem. To tackle tractability issues arising in large networks, we use machine learning to build a proxy for predicting outcomes of power system operation processes in this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains cheaper and more reliable plans than other candidates

"Rotterdam econometrics": publications of the econometric institute 1956-2005

This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005.

A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning

The behaviour of an optimization algorithm when attempting to solve a problem depends on the values assigned to its control parameters. For an algorithm to obtain desirable performance, its control parameter values must be chosen based on the current problem. Despite being necessary for optimal performance, selecting appropriate control parameter values is time-consuming, computationally expensive, and challenging. As the quantity of control parameters increases, so does the time complexity associated with searching for practical values, which often overshadows addressing the problem at hand, limiting the efficiency of an algorithm. As primarily recognized by the no free lunch theorem, there is no one-size-fits-all to problem-solving; hence from understanding a problem, a tailored approach can substantially help solve it. To predict the performance of control parameter configurations in unseen environments, this thesis crafts an intelligent generalizable framework leveraging machine learning classification and quantitative characteristics about the problem in question. The proposed parameter performance classifier (PPC) framework is extensively explored by training 84 high-accuracy classifiers comprised of multiple sampling methods, fitness types, and binning strategies. Furthermore, the novel framework is utilized in constructing a new parameter-free particle swarm optimization (PSO) variant called PPC-PSO that effectively eliminates the computational cost of parameter tuning, yields competitive performance amongst other leading methodologies across 99 benchmark functions, and is highly accessible to researchers and practitioners. The success of PPC-PSO shows excellent promise for the applicability of the PPC framework in making many more robust parameter-free meta-heuristic algorithms in the future with incredible generalization capabilities

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

"Rotterdam econometrics": publications of the econometric institute 1956-2005

This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown

Addressing the complexity of sustainability-driven structural design: Computational design, optimization, and decision making

Being one of the sectors with the largest environmental burden and high socio-economic impacts sets high requirements on the construction industry. At the same time, this provides the sector with great opportunities to contribute to the globally pursued sustainability transition. To cope with the increasing need for infrastructure and, at the same time, limit their sustainability impacts, changes and innovation in the construction sector are required. The greatest possibility to limit the sustainability impact of construction works is at the early design phase of construction projects, as many of the choices influencing sustainability are made at that point. Traditionally, an early choice of a preferred design is often made based on limited knowledge and past experience, considering only a handful of options. This preferred design is then taken on to the successive stages in the stepwise design process, leading to suboptimization.Alternatively, many different design choices could be considered and evaluated in a more holistic approach in order to find the most sustainable design for a particular application. However, finding design solutions that offer the best sustainability performance and fulfil all structural, performance and buildability requirements, require methods that allow considering different design options, analysing them, and assessing their sustainability. The aim of this thesis is to explore and develop methods enabling structural engineers to take sustainability objectives into account in the design of structures. Throughout this thesis, a number of methods have been explored to take sustainability aspects into account in the structural design process. As a first step, highly parameterized computer codes for sustainability-driven design have been developed. These codes interoperate with FE analysis software to automatically model and analyse design concepts over the whole design space and verify compliance with structural design standards. The codes were complemented with a harmonized method for life cycle sustainability performance assessment, in line with the state-of-the-art standards. Here, sustainability criteria were defined covering environmental, social, economic, buildability and structural performance for multi-criteria assessment of design concepts. To identify the most sustainable designs within the set, multi-objective optimization algorithms were used. Algorithms that address the high expense of constraint function evaluations of structural design problems were developed and integrated in the parameterized computer codes for sustainability-driven design. To ensure the applicability and validity of these methods, case studies based on real-world projects and common structural engineering problems were used in this thesis. Case studies for bridges and wind turbine foundations as well as a benchmark case of a reinforced concrete beam were investigated.The case studies highlight the potential of the methods explored to support the design of more sustainable structures, as well as the applicability of the methods in structural engineering practice. It is concluded that it is possible and beneficial to combine computational design, life cycle sustainability assessment, and multi-objective design optimization as a basis for decision making in the design phase of civil engineering projects. A wide adoption of such a sustainability-driven design optimization approach in structural engineering practice can directly improve the sustainability of the construction sector