Empirical analysis of traffic volume for the application of the options theory to highway concessions

*************************************************************************************** EL WCTR es un Congreso de reconocido prestigio internacional en el ámbito de la investigación del transporte que hasta el 2010 publicaba sus libros de abstracts con ISBN. Por ello consideramos que debería seguir teníendose en cuenta para los indicadores de calidad ******************************************************************************************* Investment projects in the field of transportation infrastructures have a high degree of uncertainty and require an important amount of resources. In highway concessions in particular, the calculation of the Net Present Value (NPV) of the project by means of the discount of cash flows, may lead to erroneous results when the project incorporates certain flexibility. In these cases, the theory of real options is an alternative tool for the valuation of concessions. When the variable that generates uncertainty (in our case, the traffic) follows a random walk (or Geometric Brownian Motion), we can calculate the value of the options embedded in the contract starting directly from the process followed by that variable. This procedure notably simplifies the calculation method. In order to test the hypothesis of the evolution of traffic as a Geometric Brownian Motion, we have used the available series of traffic in Spanish highways, and we have applied the Augmented Dickey-Fuller approach, which is the most widely used test for this kind of study. The main result of the analysis is that we cannot reject the hypothesis that traffic follows a Geometric Brownian Motion in the majority of both toll highways and free highways in Spain.

Service Capacity Reserve under Uncertainty by Hospital’s ER Analogies: A Practical Model for Car Services

We define a capacity reserve model to dimension passenger car service installations according to the demographic distribution of the area to be serviced by using hospital?s emergency room analogies. Usually, service facilities are designed applying empirical methods, but customers arrive under uncertain conditions not included in the original estimations, and there is a gap between customer?s real demand and the service?s capacity. Our research establishes a valid methodology and covers the absence of recent researches and the lack of statistical techniques implementation, integrating demand uncertainty in a unique model built in stages by implementing ARIMA forecasting, queuing theory, and Monte Carlo simulation to optimize the service capacity and occupancy, minimizing the implicit cost of the capacity that must be reserved to service unexpected customers. Our model has proved to be a useful tool for optimal decision making under uncertainty integrating the prediction of the cost implicit in the reserve capacity to serve unexpected demand and defining a set of new process indicators, such us capacity, occupancy, and cost of capacity reserve never studied before. The new indicators are intended to optimize the service operation. This set of new indicators could be implemented in the information systems used in the passenger car services.

Service capacity reserve under uncertainty by hospital's ER's analogies: a practical model for car services

El objetivo de esta investigación consiste en definir un modelo de reserva de capacidad, por analogías con emergencias hospitalarias, que pueda ser implementado en el sector de servicios. Este está específicamente enfocado a su aplicación en talleres de servicio de automóviles. Nuestra investigación incorpora la incertidumbre de la demanda en un modelo singular diseñado en etapas que agrupa técnicas ARIMA, teoría de colas y simulación Monte Carlo para definir los conceptos de capacidad y ocupación de servicio, que serán utilizados para minimizar el coste implícito de la reserva capacidad necesaria para atender a clientes que carecen de cita previa. Habitualmente, las compañías automovilísticas estiman la capacidad de sus instalaciones de servicio empíricamente, pero los clientes pueden llegar bajo condiciones de incertidumbre que no se tienen en cuenta en dichas estimaciones, por lo que existe una diferencia entre lo que el cliente realmente demanda y la capacidad que ofrece el servicio. Nuestro enfoque define una metodología válida para el sector automovilístico que cubre la ausencia genérica de investigaciones recientes y la habitual falta de aplicación de técnicas estadísticas en el sector. La equivalencia con la gestión de urgencias hospitalarias se ha validado a lo largo de la investigación en la se definen nuevos indicadores de proceso (KPIs) Tal y como hacen los hospitales, aplicamos modelos estocásticos para dimensionar las instalaciones de servicio de acuerdo con la distribución demográfica del área de influencia. El modelo final propuesto integra la predicción del coste implícito en la reserva de capacidad para atender la demanda no prevista. Asimismo, se ha desarrollado un código en Matlab que puede integrarse como un módulo adicional a los sistemas de información (DMS) que se usan actualmente en el sector, con el fin de emplear los nuevos indicadores de proceso definidos en el modelo. Los resultados principales del modelo son nuevos indicadores de servicio, tales como la capacidad, ocupación y coste de reserva de capacidad, que nunca antes han sido objeto de estudio en la industria automovilística, y que están orientados a gestionar la operativa del servicio. ABSTRACT Our aim is to define a Capacity Reserve model to be implemented in the service sector by hospital's emergency room (ER) analogies, with a practical approach to passenger car services. A stochastic model has been implemented using R and a Monte Carlo simulation code written in Matlab and has proved a very useful tool for optimal decision making under uncertainty. The research integrates demand uncertainty in a unique model which is built in stages by implementing ARIMA forecasting, Queuing Theory and a Monte Carlo simulation to define the concepts of service capacity and occupancy, minimizing the implicit cost of the capacity that must be reserved to service unexpected customers. Usually, passenger car companies estimate their service facilities capacity using empirical methods, but customers arrive under uncertain conditions not included in the estimations. Thus, there is a gap between customer’s real demand and the dealer’s capacity. This research sets a valid methodology for the passenger car industry to cover the generic absence of recent researches and the generic lack of statistical techniques implementation. The hospital’s emergency room (ER) equalization has been confirmed to be valid for the passenger car industry and new process indicators have been defined to support the study. As hospitals do, we aim to apply stochastic models to dimension installations according to the demographic distribution of the area to be serviced. The proposed model integrates the prediction of the cost implicit in the reserve capacity to serve unexpected demand. The Matlab code could be implemented as part of the existing information technology systems (ITs) to support the existing service management tools, creating a set of new process indicators. Main model outputs are new indicators, such us Capacity, Occupancy and Cost of Capacity Reserve, never studied in the passenger car service industry before, and intended to manage the service operation.

Robust linear semi-infinite programming duality under uncertainty

In this paper, we propose a duality theory for semi-infinite linear programming problems under uncertainty in the constraint functions, the objective function, or both, within the framework of robust optimization. We present robust duality by establishing strong duality between the robust counterpart of an uncertain semi-infinite linear program and the optimistic counterpart of its uncertain Lagrangian dual. We show that robust duality holds whenever a robust moment cone is closed and convex. We then establish that the closed-convex robust moment cone condition in the case of constraint-wise uncertainty is in fact necessary and sufficient for robust duality. In other words, the robust moment cone is closed and convex if and only if robust duality holds for every linear objective function of the program. In the case of uncertain problems with affinely parameterized data uncertainty, we establish that robust duality is easily satisfied under a Slater type constraint qualification. Consequently, we derive robust forms of the Farkas lemma for systems of uncertain semi-infinite linear inequalities.

The chemical composition of α cen A: Strong lines and the ABO theory of collisional line broadening

The mean abundances of Mg, Si, Ca, Ti, Cr, and Fe based on both strong and weak lines of alpha CenAare determined by matching the observed line profiles with those synthesised from stellar atmospheric models and comparing these results with a similar analysis for the Sun. There is good agreement between the abundances from strong and weak lines. Strong lines should generally be an excellent indicator of abundance and far easier to measure than the weak lines normally used. Until the development of the Anstee, Barklem, and O'Mara ( ABO) theory for collisional line broadening, the uncertainty in the value of the damping constant prevented strong lines being used for abundance determinations other than in close differential analyses. We found that alpha Cen A has a mean overabundance of 0.12 +/- 0.06 dex compared to solar mean abundances. This result agrees remarkably well with previous studies that did not use strong lines or the ABO theory for collisional line broadening. Our result supports the conclusion that reliable abundances can be derived from strong lines provided this new theory for line broadening is used to calculate the van derWaals damping.

Designs for generalized linear models with several variables and model uncertainty

Standard factorial designs sometimes may be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding exact designs for such experiments that uses a criterion allowing for uncertainty in the link function, the linear predictor, or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Exact designs are investigated for two applications, and their advantages over factorial and central composite designs are demonstrated.

Science-policy in environmental and health risk assessment: if we cannot do without, can we do better?

How can empirical evidence of adverse effects from exposure to noxious agents, which is often incomplete and uncertain, be used most appropriately to protect human health? We examine several important questions on the best uses of empirical evidence in regulatory risk management decision-making raised by the US Environmental Protection Agency (EPA)'s science-policy concerning uncertainty and variability in human health risk assessment. In our view, the US EPA (and other agencies that have adopted similar views of risk management) can often improve decision-making by decreasing reliance on default values and assumptions, particularly when causation is uncertain. This can be achieved by more fully exploiting decision-theoretic methods and criteria that explicitly account for uncertain, possibly conflicting scientific beliefs and that can be fully studied by advocates and adversaries of a policy choice, in administrative decision-making involving risk assessment. The substitution of decision-theoretic frameworks for default assumption-driven policies also allows stakeholder attitudes toward risk to be incorporated into policy debates, so that the public and risk managers can more explicitly identify the roles of risk-aversion or other attitudes toward risk and uncertainty in policy recommendations. Decision theory provides a sound scientific way explicitly to account for new knowledge and its effects on eventual policy choices. Although these improvements can complicate regulatory analyses, simplifying default assumptions can create substantial costs to society and can prematurely cut off consideration of new scientific insights (e.g., possible beneficial health effects from exposure to sufficiently low 'hormetic' doses of some agents). In many cases, the administrative burden of applying decision-analytic methods is likely to be more than offset by improved effectiveness of regulations in achieving desired goals. Because many foreign jurisdictions adopt US EPA reasoning and methods of risk analysis, it may be especially valuable to incorporate decision-theoretic principles that transcend local differences among jurisdictions.

Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry

Introducing uncertainty into pattern discovery in temporal event sequences

Pattern discovery in temporal event sequences is of great importance in many application domains, such as telecommunication network fault analysis. In reality, not every type of event has an accurate timestamp. Some of them, defined as inaccurate events may only have an interval as possible time of occurrence. The existence of inaccurate events may cause uncertainty in event ordering. The traditional support model cannot deal with this uncertainty, which would cause some interesting patterns to be missing. A new concept, precise support, is introduced to evaluate the probability of a pattern contained in a sequence. Based on this new metric, we define the uncertainty model and present an algorithm to discover interesting patterns in the sequence database that has one type of inaccurate event. In our model, the number of types of inaccurate events can be extended to k readily, however, at a cost of increasing computational complexity.

Uncertainty and the influence of group norms in the attitude-behaviour relationship

Two studies were conducted to examine the impact of subjective uncertainty on conformity to group norms in the attitude-behaviour context. In both studies, subjective uncertainty was manipulated using a deliberative mindset manipulation (McGregor, Zanna, Holmes, & Spencer, 2001). In Study 1 (N = 106), participants were exposed to either an attitude-congruent or an attitude-incongruent in-group norm. In Study 2(N = 83), participants were exposed to either a congruent, incongruent, or an ambiguous in-group norm. Ranges of attitude-behaviour outcomes, including attitude-intention consistency and change in attitude-certainty, were assessed. In both studies, levels of group-normative behaviour varied as a function of uncertainty condition. In Study 1, conformity to group norms, as evidenced by variations in the level of attitude-intention consistency, was observed only in the high uncertainty condition. In Study 2, exposure to an ambiguous norm had different effects for those in the low and die high uncertainty conditions. In the low uncertainty condition, greatest conformity was observed in the attitude-congruent norm condition compared with an attitude-congruent or ambiguous norm. In contrast, individuals in the high uncertainty condition displayed greatest conformity when exposed to either an attitude-congruent or an ambiguous in-group norm. The implications of these results for the role of subjective uncertainty in social influence processes are discussed. © 2007 The British Psychological Society.

Using grounded theory for theory building in operations management research:a study on inter-firm relationship governance

Purpose – Qualitative theory building approaches, such as grounded theory method (GTM), are still not very widespread and rigorously applied in operations management (OM) research. Yet it is agreed that more systematic observation of current industrial phenomena is necessary to help managers deal with their problems. The purpose of this paper is to provide an example to help guide other researchers on using GTM for theory building in OM research. Design/methodology/approach – A GTM study in the German automotive industry consisting of 31 interviews is followed by a validation stage comprising a survey (110 responses) and a focus group. Findings – The result is an example of conducting GTM research in OM, illustrated by the development of the novel collaborative enterprise governance framework for inter-firm relationship governance in the German automotive industry. Research limitations/implications – GTM is appropriate for qualitative theory building research, but the resultant theories need further testing. Research is necessary to identify the transferability of the collaborative enterprise governance concept to other industries than automotive, to other organisational areas than R&D and to product and service settings that are less complex and innovative. Practical implications – The paper helps researchers make more informed use of GTM when engaging in qualitative theory building research in OM. Originality/value – There is a lack of explicit and well-informed use of GTM in OM research because of poor understanding. This paper addresses this deficiency. The collaborative enterprise governance framework is a significant contribution in an area of growing importance within OM.

The magnetic exchange interactions in chromium chalcogenide spinels

The chromium chalcogenide spinels, MCr2X4 (M = Zn, Cd, Hg; X = O, S, Se), have been the subject of considerable interest in recent years. In each case the crystal structure is that of a normal spinel with the chromium ions exclusively occupying the octahedral (B) sites, so that when diamagnetic ions are located at the tetrahedral (A) sites the only magnetic interactions present are those between B-site ions. Despite such apparently simple circumstances a rich variety of magnetic behaviour is exhibited. For the oxides the ground state spin configurations are antiferromagnetic whilst for the selenides ferromagnetic interactions dominate and several authors have drawn attention to the fact that the nature of the dominant interaction is a function of the nearest neighbour chromium - chromium separation. However, at least two of the compounds exhibit spiral structures and it has been proved difficult to account for the various spin configurations within a unified theory of the magnetic interactions involved. More recently, the possibility of formulating a simplified interpretation of the magnetic interactions has been provided by the discovery that the crystal struture of spinels does not always conform to the centrosymmetrical symmetry Fd3m that has been conventionally assumed. The deviation from this symmetry is associated with small < 111> displacements of the octahedrally coordinated metal ions and the structures so obtained are more correctly referred to the non-centrosymmetrical space group F43m. In the present study, therefore, extensive X-ray diffraction data have been collected from four chromium chalcogenide specimens and used to refine the corresponding structural parameters assuming F43m symmetry and also with conventional symmetry. The diffracted intensities from three of the compounds concerned cannot be satisfactorily accounted for on the basis of conventional symmetry and new locations have been found for the chromium ions in these cases. It is shown, however, that these displacements in chromium positions only partially resolve the difficulties in interpreting the magnetic behaviour. A re-examination of the magnetic data from different authors indicates much greater uncertainty in their measurements than they had claimed. By taking this into consideration it is shown that a unified theory of magnetic behaviour for the chromium chalcogenide spinels is a real possibility.

The role of industrial design in technological innovation

This is an exploratory study in a field which previously was virtually unexplored. The aim is to identify, for the benefit of innovators, the influence of industrial design on the commercial success of new science-based products used for professional and industrial purposes. The study is a contribution to the theory of success and failure in industrial innovation. The study begins by defining the terminology. To place the investigation in context, there is then a review of past attempts by official policy-making bodies to improve the competitiveness of British products of manufacture through good design. To elucidate the meaning of good design, attempts to establish a coherent philosophy of style in British products of manufacture during the same period are also reviewed. Following these reviews, empirical evidence is presented to identify what actually takes place in successful firms when industrial design is allocated a role in the process of technological innovation. The evidence comprises seven case studies of new science-based products used for professional or industrial purposes which have received Design Council Awards. To facilitate an objective appraisal, evidence was obtained by conducting separate semi-structured interviews, the detail of which is described, with senior personnel in innovating firms, with industrial design consultants, and with professional users. The study suggests that the likelihood of commercial success in technological innovation is greater when the form, configuration, and the overall appearance of a new product, together with the detail which delineates them, are consciously and expertly controlled. Moreover, uncertainty in innovation is likely to be reduced if the appearance of a new product is consciously designed to facilitate recognition and comprehension. Industrial design is an especially significant factor when a firm innovates against a background of international competition and comparable levels of technological competence in rival firms. The likelihood of success in innovation is enhanced if design is allocated a role closely identified with the total needs of the user and discrete from the engineering function in company organisation. Recent government measures, initiated since this study began, are corroborative of the findings.

Dynamics of effectuation and causation in technology-based new ventures (INTERACTIVE PAPER)

The creation of new ventures is a process characterized by the need to decide and take action in the face of uncertainty, and this is particularly so in the case of technology-based ventures. Effectuation theory (Sarasvathy, 2001) has advanced two possible approaches for making decisions while facing uncertainty in the entrepreneurial process. Causation logic is based on prediction and aims at lowering uncertainty, whereas effectuation logic is based on non-predictive action and aims at working with uncertainty. This study aims to generate more fine-grained insight in the dynamics of effectuation and causation over time. We address the following questions: (1) What patterns can be found in effectual and causal behaviour of technology-based new ventures over time? And (2) How may patterns in the dynamics of effectuation and causation be explained?

Uncertainty analysis in the Model Web

This thesis provides a set of tools for managing uncertainty in Web-based models and workflows.To support the use of these tools, this thesis firstly provides a framework for exposing models through Web services. An introduction to uncertainty management, Web service interfaces,and workflow standards and technologies is given, with a particular focus on the geospatial domain.An existing specification for exposing geospatial models and processes, theWeb Processing Service (WPS), is critically reviewed. A processing service framework is presented as a solutionto usability issues with the WPS standard. The framework implements support for Simple ObjectAccess Protocol (SOAP), Web Service Description Language (WSDL) and JavaScript Object Notation (JSON), allowing models to be consumed by a variety of tools and software. Strategies for communicating with models from Web service interfaces are discussed, demonstrating the difficultly of exposing existing models on the Web. This thesis then reviews existing mechanisms for uncertainty management, with an emphasis on emulator methods for building efficient statistical surrogate models. A tool is developed to solve accessibility issues with such methods, by providing a Web-based user interface and backend to ease the process of building and integrating emulators. These tools, plus the processing service framework, are applied to a real case study as part of the UncertWeb project. The usability of the framework is proved with the implementation of aWeb-based workflow for predicting future crop yields in the UK, also demonstrating the abilities of the tools for emulator building and integration. Future directions for the development of the tools are discussed.