Rational expectations modelling in O.R

The conventional OR approach to managing a system is, in outline, firstly to create a model of the existing system, secondly, to investigate changes in the model which improve or control the behaviour of the model and thirdly, to implement these changes in the system. It is assumed that the model incorporating these changes will be a valid representation of the system after the changes, in as far as the original model was a valid representation of the original system, and can thus be used to assess the benefits and disbenefits arising from the changes

Integrating big data into a sustainable mobility policy 2.0 planning support system

It is estimated that each of us, on a daily basis, produces a bit more than 1 GB of digital content through our mobile phone and social networks activities, bank card payments, location-based positioning information, online activities, etc. However, the implementation of these large data amounts in city assets planning systems still remains a rather abstract idea for several reasons, including the fact that practical examples are still very strongly services-oriented, and are a largely unexplored and interdisciplinary field; hence, missing the cross-cutting dimension. In this paper, we describe the Policy 2.0 concept and integrate user generated content into Policy 2.0 platform for sustainable mobility planning. By means of a real-life example, we demonstrate the applicability of such a big data integration approach to smart cities planning process. Observed benefits range from improved timeliness of the data and reduced duration of the planning cycle to more informed and agile decision making, on both the citizens and the city planners end. The integration of big data into the planning process, at this stage, does not have uniform impact across all levels of decision making and planning process, therefore it should be performed gradually and with full awareness of existing limitations

Numerical Integration and Dynamic Discretization in Heuristic Search Planning over Hybrid Domains

In this paper we look into the problem of planning over hybrid domains, where change can be both discrete and instantaneous, or continuous over time. In addition, it is required that each state on the trajectory induced by the execution of plans complies with a given set of global constraints. We approach the computation of plans for such domains as the problem of searching over a deterministic state model. In this model, some of the successor states are obtained by solving numerically the so-called initial value problem over a set of ordinary differential equations (ODE) given by the current plan prefix. These equations hold over time intervals whose duration is determined dynamically, according to whether zero crossing events take place for a set of invariant conditions. The resulting planner, FS+, incorporates these features together with effective heuristic guidance. FS+ does not impose any of the syntactic restrictions on process effects often found on the existing literature on Hybrid Planning. A key concept of our approach is that a clear separation is struck between planning and simulation time steps. The former is the time allowed to observe the evolution of a given dynamical system before committing to a future course of action, whilst the later is part of the model of the environment. FS+ is shown to be a robust planner over a diverse set of hybrid domains, taken from the existing literature on hybrid planning and systems.Comment: 17 page

A simple model to evaluate relative urban conditions

The urban development process intends to enhance urban equipment, infrastructure and services to improve urban conditions and reduce inequalities. While planning activities usually demand a sizeable amount of data to assess these conditions, it is hard to find a straightforward procedure to translate these data into a comprehensive and balanced set of projects and programs. Most urban projects have a strong sectorial character and it is difficult to compare parks with roads or sanitation with schools. Development projects are normally justified in their own terms, yet rarely are sectorial demands equably met by the proposed projects. The following model intends to facilitate this task, providing a simple yet efficient means to assess urban conditions, evaluate and compare the sectorial demands and assemble a balanced set of development goals that can be used to estimate the overall investment needed. The proposed system assesses urban conditions through a set of selected indicators, derived from a compact data base. These indicators represent the performance of the basic urban sectors at each urban zone, thus providing the necessary spatial component to the system. The indicators are normalised to allow comparison of conditions between different sectors. The values of the normalised indicators are then used to evaluate the relative quality of services at each urban zone. By displaying the normalised indicators as bar charts, it is easier to visualise sectorial demands and spatial imbalances. The model is built on a spreadsheet, making fairly easy to verify how much improvement in the sector is needed to attain the desired performance level in each zone. New values can be entered interactively until a preferred urban conditions pattern is attained. The information produced by the model can be used as the basic guideline to dimension sectorial projects and compose a comprehensive development program. Besides describing and discussing all the procedures adopted, a simple yet complete example illustrates the use of the model.planning; urban; development; model; infrastructure; condition; regional

Modelling Planner-Carrier Interactions in Road Freight Transport: Optimization of Road Maintenance Costs Via Overloading Control

A bi-level modelling approach is proposed to represent the interaction between the vehicle loading practices of road freight transport carriers, and the decisions of a road planning authority responsible both for road maintenance and for the enforcement of overloading control. At the lower (reactive) level, the overloading decisions of the carriers impact on road maintenance expenditure, while at the upper (anticipatory) level the planner decides fine and enforcement levels by anticipating the responses of the carriers. A case study using data from Mexico is used to illustrate the method

Creating planning portfolios with predictive models

Mención Internacional en el título de doctorSequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners: for each planning task there are one or more base planners that obtain the best solution. Therefore, the main challenge when building a planning portfolio is to ensure that a suitable planner be chosen and that it gets enough planning time. To solve this problem we need firstly to define three elements. The first is the settings or planning conditions: time, memory, or other constraints. The second one is the set of base planners. And finally, a benchmark that provides us with knowledge on how the base planners will behave under the given settings, following some kind of inductive process. Ideally, if the previous elements are correctly defined, when a new planning task arrives, an oracle will be able to tell which base planner to run and for how long. In practice, since no oracle exists, the challenge to choose a sub-set of base planners, is assigning them a running time and deciding the order in which they are run to optimize a planning metric under the predefined settings. Many state-of-the- art portfolios might never achieve an optimal performance because they do not select different planners for the different planning tasks. In addition, these static techniques typically assign a fixed running time to the selected set of planners, independently of the task. besides, the old-fashioned dynamic portfolios present a poor characterization of the planning task and do not have enough knowledge to predict an accurate portfolio configuration in many cases. The aforementioned drawbacks are intensified by the fact that there is an increasing number of planners available to choose from, although many of them are designed following similar approaches, so they are expected to behave similarly. This dissertation is built on two main hypotheses. Firstly that the space of the base planners can be reduced just by selecting a subset of diverse or complementary planners; e.g. that there is a minimal set of planners that ensure that the optimal portfolio can be computed. Secondly, that planning tasks can be characterized, and that the difficulty in solving them can be modelled as a function of these features. To evaluate the first hypothesis, we analyze different metrics that could be used to filter the initial set of base planners. Classical metrics such as coverage, quality or execution time have been chosen by different portfolios in the past. We demonstrate that these selection methods may reduce the diversity of the portfolios, and propose an alternative method based on the Pareto dominance. We then carry out a profound analysis on previous planning task characterizations and show how we could exploit them in current planning paradigms. A group of very informative features are proposed to improve the current feature definition of the planning tasks. These features have enough knowledge to differentiate planning tasks with similar \a priori" complexity. In this thesis we demonstrate that the implicit knowledge can be exploited in the construction of predictive models. These models estimate whether a base planner will be able to solve a given problem and, if so, how long it will take. Nevertheless, the predictive models are not perfect and sometimes provide wrong (or inaccurate) predictions. To solve this kind of problems, we propose different portfolio strategies to combine the number of selected base planners and their times. These strategies take into account the predefined settings and the knowledge learned in previous phases. In conclusion, this thesis sets out a profound analysis of three different mechanisms or steps to create planning portfolios with predictive models, including new proposals for developing: planner filtering, planning task featuring, learning predictive models and portfolio construction strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014Los portfolios de planificadores tienen un gran potencial ya que pueden aprovecharse de los diferentes planificadores automáticos, consiguiendo mejorar el rendimiento de un único planificador. Sin embargo, la creación de un portfolio no es una tarea sencilla, ya que para poder crear uno lo suficientemente bueno, hay que tratar tres problemas fundamentales. El primero de ellos es encontrar qué planificadores hay que seleccionar como componentes del mismo. La segunda es el tiempo que hay que asignar a cada planificador y, la última y no menos importante el orden en el que se tienen que ejecutar. Actualmente en el estado del arte, estas configuraciones, se realizan a partir de los resultados obtenidos por los planificadores en una fase previa de entrenamiento con un conjunto de problemas y restricciones prefijado (tiempo, memoria, etc), consiguiendo una configuración específica tratando de optimizar una métrica. Idealmente, la mejor configuración posible consiste en asignar el tiempo suficiente al mejor planificador para cada tarea de planificación. Sin embargo, esta configuración no siempre es posible, y hay que recurrir a otras aproximaciones como asignar un tiempo fijo a una selección de planificadores. Ésta no es la única simplificación utilizada, existen otras técnicas más cercanas a la óptima, en las cuales se selecciona un planificador o varios en función de la tarea a resolver. Sin embargo, estos sistemas, denominados dinámicos, incluyen una escasa caracterización de las tareas de planificación. En esta tesis se parte de dos hipótesis. La primera de ellas es que existe un conjunto reducido de planificadores que maximiza la diversidad. La segunda de ellas consiste en la posibilidad de crear un conjunto de descriptivos lo suficientemente bueno para caracterizar la tarea de planificación. La caracterización de las tareas de planificación puede estar basada en sus distintas representaciones, así como en sus paradigmas. La primera tarea es seleccionar un conjunto de planificadores; realizando un análisis basado en las métricas clásicas de planificación, como son problemas resueltos, calidad y tiempo para seleccionar un subconjunto de planificadores. Adicionalmente, proponemos como alternativa a estas métricas, una técnica multiobjetivo. Este criterio está basado en la dominancia de Pareto combinando las métricas de tiempo y calidad. Continuando con nuestras hip_otesis es necesario crear un conjunto de características bien informado para la tarea de planificación. Estas características deben ser capaces de diferenciar adecuadamente por problema y para ello sería necesario basarse en los distintos paradigmas de la planificación automática. Este grupo de características tienen que ser úutiles para crear modelos predictivos. Estos modelos podrán darnos además de una selección de planificadores, una aproximación del tiempo asignado a cada componente y el orden de los mismos. Adicionalmente se presentarán una serie de estrategias para explotar el conocimiento obtenido con los modelos predictivos. En conclusión, se plantea y desarrolla un sistema para configurar porfolios de planificadores usando modelos predictivos en tres fases distintas. Una instanciación de este sistema fue el ganador de la competición internacional de planificación en el áarea de satisfacibilidad en el año 2014.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Álvaro Torralba Arias de Reyna.- Vocal: Alessandro Saett

How to incorporate the spatial dimension within destination choice models? The case of Antwerpen

This paper considers different alternatives for including spatial aspects within the activity-based approach for modeling destination choices. The study area is the urban agglomeration of Antwerpen (Belgium); the city and its suburbs are considered. Individual travel surveys are used. The paper pays particular attention to the inclusion of space within the decision context by including specific land-use explanatory variables generated by Geographical Information Systems. A preliminary geographical analysis is performed in order to represent the city by a limited set of destinations (n = 33) and to characterize those zones in terms of land use. Discrete choice modelling is used: each individual faces the total set of spatial destination alternatives. Several modelling approaches are explored and compared in terms of utility function (for instance Box-Cox; random coefficients) and in terms of global formulation (multinomial logit versus nested logit). The mixed nested logit formulation is selected as best and the parameter estimations are interpreted; it shows the importance of space within destination choices. This paper provides a useful background for decision-makers and planners of transportation policy related to individual mobility patterns. Keywords Discrete choice model, activity-based approach, GIS, land use, urban mobility, Antwerpen

Optimizing the location of weather monitoring stations using estimation uncertainty

In this article, we address the problem of planning a network of weather monitoring stations observing average air temperature (AAT). Assuming the network planning scenario as a location problem, an optimization model and an operative methodology are proposed. The model uses the geostatistical uncertainty of estimation and the indicator formalism to consider in the location process a variable demand surface, depending on the spatial arrangement of the stations. This surface is also used to express a spatial representativeness value for each element in the network. It is then possible to locate such a network using optimization techniques, such as the used methods of simulated annealing (SA) and construction heuristics. This new approach was applied in the optimization of the Portuguese network of weather stations monitoring the AAT variable. In this case study, scenarios of reduction in the number of stations were generated and analysed: the uncertainty of estimation was computed, interpreted and applied to model the varying demand surface that is used in the optimization process. Along with the determination of spatial representativeness value of individual stations, SA was used to detect redundancies on the existing network and establish the base for its expansion. Using a greedy algorithm, a new network for monitoring average temperature in the selected study area is proposed and its effectiveness is compared with the current distribution of stations. For this proposed network distribution maps of the uncertainty of estimation and the temperature distribution were created. Copyright (c) 2011 Royal Meteorological Societyinfo:eu-repo/semantics/publishedVersio