An Innovative Approach to Evaluating Organisational Change

This paper addresses the difficult problem of how to improve the process of evaluating organisational change. Given that the data emergent from an evaluative exercise will strongly influence the subsequent strategic and operational decisions taken by organisational managers, it is critical that the evaluation approach itself is capable of delivering high quality, accurate and timely data. The aim of this paper is to examine the role of the IT-based Optionfinder Technology used in conjunction with focus groups, in generating management decision-making data, and reflecting the changes in key performance indicators in a utility organisation. The case study research evaluates the innovative integrative approach adopted by the utility organisation and concludes that the proposed approach contributes to improvements in the decision-making capability of managers.

Towards Improving the Utilisation of University Teaching Space

There is a perception that teaching space in universities is a rather scarce resource. However, some studies have revealed that in many institutions it is actually chronically under-used. Often, rooms are occupied only half the time, and even when in use they are often only half full. This is usually measured by the ‘utilization’ which is defined as the percentage of available ‘seat-hours’ that are employed. Within real institutions, studies have shown that this utilization can often take values as low as 20–40%. One consequence of such a low level of utilization is that space managers are under pressure to make more efficient use of the available teaching space. However, better management is hampered because there does not appear to be a good understanding within space management (near-term planning) of why this happens. This is accompanied, within space planning (long-term planning) by a lack of experise on how best to accommodate the expected low utilizations. This motivates our two main goals: (i) To understand the factors that drive down utilizations, (ii) To set up methods to provide better space planning. Here, we provide quantitative evidence that constraints arising from timetabling and location requirements easily have the potential to explain the low utilizations seen in reality. Furthermore, on considering the decision question ‘Can this given set of courses all be allocated in the available teaching space?’ we find that the answer depends on the associated utilization in a way that exhibits threshold behaviour: There is a sharp division between regions in which the answer is ‘almost always yes’ and those of ‘almost always no’. Through analysis and understanding of the space of potential solutions, our work suggests that better use of space within universities will come about through an understanding of the effects of timetabling constraints and when it is statistically likely that it will be possible for a set of courses to be allocated to a particular space. The results presented here provide a firm foundation for university managers to take decisions on how space should be managed and planned for more effectively. Our multi-criteria approach and new methodology together provide new insight into the interaction between the course timetabling problem and the crucial issue of space planning.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

A taxonomy for wavelet neural networks applied to nonlinear modelling

This article presents a novel classification of wavelet neural networks based on the orthogonality/non-orthogonality of neurons and the type of nonlinearity employed. On the basis of this classification different network types are studied and their characteristics illustrated by means of simple one-dimensional nonlinear examples. For multidimensional problems, which are affected by the curse of dimensionality, the idea of spherical wavelet functions is considered. The behaviour of these networks is also studied for modelling of a low-dimension map.

Identification of dynamic systems under closed-loop control

This paper introduces a novel modelling framework for identifying dynamic models of systems that are under feedback control. These models are identified under closed-loop conditions and produce a joint representation that includes both the plant and controller models in state space form. The joint plant/controller model is identified using subspace model identification (SMI), which is followed by the separation of the plant model from the identified one. Compared to previous research, this work (i) proposes a new modelling framework for identifying closed-loop systems, (ii) introduces a generic structure to represent the controller and (iii) explains how that the new framework gives rise to a simplified determination of the plant models. In contrast, the use of the conventional modelling approach renders the separation of the plant model a difficult task. The benefits of using the new model method are demonstrated using a number of application studies.

Comparison of two construction algorithms for local model networks

The divide-and-conquer approach of local model (LM) networks is a common engineering approach to the identification of a complex nonlinear dynamical system. The global representation is obtained from the weighted sum of locally valid, simpler sub-models defined over small regions of the operating space. Constructing such networks requires the determination of appropriate partitioning and the parameters of the LMs. This paper focuses on the structural aspect of LM networks. It compares the computational requirements and performances of the Johansen and Foss (J&F) and LOLIMOT tree-construction algorithms. Several useful and important modifications to each algorithm are proposed. The modelling performances are evaluated using real data from a pilot plant of a pH neutralization process. Results show that while J&F achieves a more accurate nonlinear representation of the pH process, LOLIMOT requires significantly less computational effort.

Robust fuzzy Gustafson-Kessel clustering for nonlinear system identification

This paper deals with Takagi-Sugeno (TS) fuzzy model identification of nonlinear systems using fuzzy clustering. In particular, an extended fuzzy Gustafson-Kessel (EGK) clustering algorithm, using robust competitive agglomeration (RCA), is developed for automatically constructing a TS fuzzy model from system input-output data. The EGK algorithm can automatically determine the 'optimal' number of clusters from the training data set. It is shown that the EGK approach is relatively insensitive to initialization and is less susceptible to local minima, a benefit derived from its agglomerate property. This issue is often overlooked in the current literature on nonlinear identification using conventional fuzzy clustering. Furthermore, the robust statistical concepts underlying the EGK algorithm help to alleviate the difficulty of cluster identification in the construction of a TS fuzzy model from noisy training data. A new hybrid identification strategy is then formulated, which combines the EGK algorithm with a locally weighted, least-squares method for the estimation of local sub-model parameters. The efficacy of this new approach is demonstrated through function approximation examples and also by application to the identification of an automatic voltage regulation (AVR) loop for a simulated 3 kVA laboratory micro-machine system.

An Investigation of Fuzzy Multiple Heuristic Orderings in the Construction of University Examination Timetables

In this paper, we present an investigation into using fuzzy methodologies to guide the construction of high quality feasible examination timetabling solutions. The provision of automated solutions to the examination timetabling problem is achieved through a combination of construction and improvement. The enhancement of solutions through the use of techniques such as metaheuristics is, in some cases, dependent on the quality of the solution obtained during the construction process. With a few notable exceptions, recent research has concentrated on the improvement of solutions as opposed to focusing on investigating the ‘best’ approaches to the construction phase. Addressing this issue, our approach is based on combining multiple criteria in deciding on how the construction phase should proceed. Fuzzy methods were used to combine three single construction heuristics into three different pair wise combinations of heuristics in order to guide the order in which exams were selected to be inserted into the timetable solution. In order to investigate the approach, we compared the performance of the various heuristic approaches with respect to a number of important criteria (overall cost penalty, number of skipped exams, number of iterations of a rescheduling procedure required and computational time) on twelve well-known benchmark problems. We demonstrate that the fuzzy combination of heuristics allows high quality solutions to be constructed. On one of the twelve problems we obtained lower penalty than any previously published constructive method and for all twelve we obtained lower penalty than when any of the single heuristics were used alone. Furthermore, we demonstrate that the fuzzy approach used less backtracking when constructing solutions than any of the single heuristics. We conclude that this novel fuzzy approach is a highly effective method for heuristically constructing solutions and, as such, has particular relevance to real-world situations in which the construction of feasible solutions is often a difficult task in its own right.

The outflow ranking method for weighted directed graphs

A ranking method assigns to every weighted directed graph a (weak) ordering of the nodes. In this paper we axiomatize the ranking method that ranks the nodes according to their outflow using four independent axioms. Besides the well-known axioms of anonymity and positive responsiveness we introduce outflow monotonicity – meaning that in pairwise comparison between two nodes, a node is not doing worse in case its own outflow does not decrease and the other node’s outflow does not increase – and order preservation – meaning that adding two weighted digraphs such that the pairwise ranking between two nodes is the same in both weighted digraphs, then this is also their pairwise ranking in the ‘sum’ weighted digraph. The outflow ranking method generalizes the ranking by outdegree for directed graphs, and therefore also generalizes the ranking by Copeland score for tournaments.

Maturity Model for Supply Chain Relationships in Construction

This study explores the special characteristics of the construction industry and develops a maturity model for measuring and improving the relationships between the key players of a construction supply chain. The model adopts the capability maturity methodology and defines four maturity levels of construction supply chain relationships. It is in a matrix format, consisting of 24 assessment criteria in eight categories at each maturity level. It also provides three different ways of using the model. The model is evaluated through a series of expert interviews. A case study is also presented to demonstrate the application of this model in practice.

Incorporating Higher Moments into Value at Risk Forecasting

Value-at-risk (VaR) forecasting generally relies on a parametric density function of portfolio returns that ignores higher moments or assumes them constant. In this paper, we propose a simple approach to forecasting of a portfolio VaR. We employ the Gram-Charlier expansion (GCE) augmenting the standard normal distribution with the first four moments, which are allowed to vary over time. In an extensive empirical study, we compare the GCE approach to other models of VaR forecasting and conclude that it provides accurate and robust estimates of the realized VaR. In spite of its simplicity, on our dataset GCE outperforms other estimates that are generated by both constant and time-varying higher-moments models.