Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

Improving “last mile” delivery performance to retailers in hub and spoke distribution systems

Purpose – The purpose of this paper is to investigate the “last mile” delivery link between a hub and spoke distribution system and its customers. The proportion of retail, as opposed to non-retail (trade) customers using this type of distribution system has been growing in the UK. The paper shows the applicability of simulation to demonstrate changes in overall delivery policy to these customers. Design/methodology/approach – A case-based research method was chosen with the aim to provide an exemplar of practice and test the proposition that simulation can be used as a tool to investigate changes in delivery policy. Findings – The results indicate the potential improvement in delivery performance, specifically in meeting timed delivery performance, that could be made by having separate retail and non-retail delivery runs from the spoke terminal to the customer. Research limitations/implications – The simulation study does not attempt to generate a vehicle routing schedule but demonstrates the effects of a change on delivery performance when comparing delivery policies. Practical implications – Scheduling and spreadsheet software are widely used and provide useful assistance in the design of delivery runs and the allocation of staff to those delivery runs. This paper demonstrates to managers the usefulness of investigating the efficacy of current design rules and presents simulation as a suitable tool for this analysis. Originality/value – A simulation model is used in a novel application to test a change in delivery policy in response to a changing delivery profile of increased retail deliveries.

A simulation analysis of spoke-terminals operating in LTL Hub-and-Spoke freight distribution systems

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT The research presented in this thesis is concerned with Discrete-Event Simulation (DES) modelling as a method to facilitate logistical policy development within the UK Less-than-Truckload (LTL) freight distribution sector which has been typified by “Pallet Networks” operating on a hub-and-spoke philosophy. Current literature relating to LTL hub-and-spoke and cross-dock freight distribution systems traditionally examines a variety of network and hub design configurations. Each is consistent with classical notions of creating process efficiency, improving productivity, reducing costs and generally creating economies of scale through notions of bulk optimisation. Whilst there is a growing abundance of papers discussing both the network design and hub operational components mentioned above, there is a shortcoming in the overall analysis when it comes to discussing the “spoke-terminal” of hub-and-spoke freight distribution systems and their capabilities for handling the diverse and discrete customer profiles of freight that multi-user LTL hub-and-spoke networks typically handle over the “last-mile” of the delivery, in particular, a mix of retail and non-retail customers. A simulation study is undertaken to investigate the impact on operational performance when the current combined spoke-terminal delivery tours are separated by ‘profile-type’ (i.e. retail or nonretail). The results indicate that a potential improvement in delivery performance can be made by separating retail and non-retail delivery runs at the spoke-terminal and that dedicated retail and non-retail delivery tours could be adopted in order to improve customer delivery requirements and adapt hub-deployed policies. The study also leverages key operator experiences to highlight the main practical implementation challenges when integrating the observed simulation results into the real-world. The study concludes that DES be harnessed as an enabling device to develop a ‘guide policy’. This policy needs to be flexible and should be applied in stages, taking into account the growing retail-exposure.

Synchronization processes in nonlinear systems and their relation to cortical oscillatory dynamics

This thesis was focused on theoretical models of synchronization to cortical dynamics as measured by magnetoencephalography (MEG). Dynamical systems theory was used in both identifying relevant variables for brain coordination and also in devising methods for their quantification. We presented a method for studying interactions of linear and chaotic neuronal sources using MEG beamforming techniques. We showed that such sources can be accurately reconstructed in terms of their location, temporal dynamics and possible interactions. Synchronization in low-dimensional nonlinear systems was studied to explore specific correlates of functional integration and segregation. In the case of interacting dissimilar systems, relevant coordination phenomena involved generalized and phase synchronization, which were often intermittent. Spatially-extended systems were then studied. For locally-coupled dissimilar systems, as in the case of cortical columns, clustering behaviour occurred. Synchronized clusters emerged at different frequencies and their boundaries were marked through oscillation death. The macroscopic mean field revealed sharp spectral peaks at the frequencies of the clusters and broader spectral drops at their boundaries. These results question existing models of Event Related Synchronization and Desynchronization. We re-examined the concept of the steady-state evoked response following an AM stimulus. We showed that very little variability in the AM following response could be accounted by system noise. We presented a methodology for detecting local and global nonlinear interactions from MEG data in order to account for residual variability. We found crosshemispheric nonlinear interactions of ongoing cortical rhythms concurrent with the stimulus and interactions of these rhythms with the following AM responses. Finally, we hypothesized that holistic spatial stimuli would be accompanied by the emergence of clusters in primary visual cortex resulting in frequency-specific MEG oscillations. Indeed, we found different frequency distributions in induced gamma oscillations for different spatial stimuli, which was suggestive of temporal coding of these spatial stimuli. Further, we addressed the bursting character of these oscillations, which was suggestive of intermittent nonlinear dynamics. However, we did not observe the characteristic-3/2 power-law scaling in the distribution of interburst intervals. Further, this distribution was only seldom significantly different to the one obtained in surrogate data, where nonlinear structure was destroyed. In conclusion, the work presented in this thesis suggests that advances in dynamical systems theory in conjunction with developments in magnetoencephalography may facilitate a mapping between levels of description int he brain. this may potentially represent a major advancement in neuroscience.

An examination of the development of LTL hub-and-spoke freight distribution systems

Over recent years, hub-and-spoke distribution techniques have attracted widespread research attention. Despite there being a growing body of literature in this area there is less focus on the spoke-terminal element of the hub-and-spoke system as being a key component in the overall service received by the end-user. Current literature is highly geared towards discussing bulk optimization of freight units rather than to the more discrete and individualistic profile characteristics of shared-user Less-than-truckload (LTL) freight. In this paper, a literature review is presented to review the role hub-and-spoke systems play in meeting multi-profile customer demands, particularly in developing sectors with more sophisticated needs, such as retail. The paper also looks at the use of simulation technology as a suitable tool for analyzing spoke-terminal operations within developing hub-and spoke systems.

An incentivized auction based group-selling approach for demand response management in V2G systems

Vehicle-to-Grid (V2G) system with efficient Demand Response Management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at EVs. An incentivilized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling based auction. In the proposed approach, a number of aggregators sit on the first level auction responsible to communicate with a group of EVs. EVs as bidders consider Quality of Energy (QoE) requirements and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.

Creating and sustaining competitive advantage in collaborative systems:the what and the how

Initially this paper asks two questions: In order to create and sustain competitive advantage through collaborative systems WHAT should be managed? and HOW should it be managed? It introduces the competitive business structure and reviews some of the global trends in manufacturing and business, which leads to focus on manage processes, value propositions and extended business processes. It then goes on to develop a model of the collaborative architecture for extended enterprises and demonstrates the validity of this architecture through a case study. It concludes that, in order to create and sustain competitive advantage, collaborative systems should facilitate the management of: the collaborative architecture of the extended enterprise; the extended business processes and the value proposition for each extended enterprise through a meta level management process. It also identifies areas for further research, such as better understanding of: the exact nature and interaction of multiple strategies within an enterprise; how to manage people/teams working along extended business processes; and the nature and prerequisites of the manage processes.

Two sides to attitudinal commitment:the effect of calculative and loyalty commitment on enforcement mechanism in distribution channels

Previous conceptualizations of attitudinal commitment are extended by considering two very different components of a manufacturer’s attachment to an independent channel intermediary. Relying on commitment theory, a model is developed that describes how attitudinal commitment may reside in either the instrumental or the social strain of a manufacturer’s relationship with its distributor. For each strain, the developmental role played by key facets of the channel setting—relative dependence, pledges, and trust—are shown. Furthermore, the nature of the attachment bond is posited to motivate very different governance mechanisms as the distribution agreement is enforced by either social or contractual means. Empirical support for the model demonstrates that an expanded view of attitudinal commitment is important in understanding the complex nature of attachment in channel relationships.

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

Reliability:its implications in production systems design

Research on production systems design has in recent years tended to concentrate on ‘software’ factors such as organisational aspects, work design, and the planning of the production operations. In contrast, relatively little attention has been paid to maximising the contributions made by fixed assets, particularly machines and equipment. However, as the cost of unproductive machine time has increased, reliability, particularly of machine tools, has become ever more important. Reliability theory and research has traditionally been based in the main on electrical and electronic equipment whereas mechanical devices, especially machine tools, have not received sufficiently objective treatment. A recently completed research project has considered the reliability of machine tools by taking sample surveys of purchasers, maintainers and manufacturers. Breakdown data were also collected from a number of engineering companies and analysed using both manual and computer techniques. Results obtained have provided an indication of those factors most likely to influence reliability and which in turn could lead to improved design and selection of machine tool systems. Statistical analysis of long-term field data has revealed patterns of trends of failure which could help in the design of more meaningful maintenance schemes.

Inference by belief propagation in composite systems

We devise a message passing algorithm for probabilistic inference in composite systems, consisting of a large number of variables, that exhibit weak random interactions among all variables and strong interactions with a small subset of randomly chosen variables; the relative strength of the two interactions is controlled by a free parameter. We examine the performance of the algorithm numerically on a number of systems of this type for varying mixing parameter values.

Estimating parameters in stochastic systems:a variational Bayesian approach

This work is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variation of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here a new extended framework is derived that is based on a local polynomial approximation of a recently proposed variational Bayesian algorithm. The paper begins by showing that the new extension of this variational algorithm can be used for state estimation (smoothing) and converges to the original algorithm. However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new approach is validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein–Uhlenbeck process, the exact likelihood of which can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz ’63 (3D model). As a special case the algorithm is also applied to the 40 dimensional stochastic Lorenz ’96 system. In our investigation we compare this new approach with a variety of other well known methods, such as the hybrid Monte Carlo, dual unscented Kalman filter, full weak-constraint 4D-Var algorithm and analyse empirically their asymptotic behaviour as a function of observation density or length of time window increases. In particular we show that we are able to estimate parameters in both the drift (deterministic) and the diffusion (stochastic) part of the model evolution equations using our new methods.