The development of methods for the design and evolution of reconfigurable cellular manufacturing systems.

Doctor of Philosophy in Engineering (Mechanical Engineering)The concept of reconfigurable manufacturing is presently being researched due to the need for production systems that are able to economically respond to changes in markets and the rapid introduction of new products. Cellular Manufacturing Systems (CMS) are a central concept in just-in-time and lean manufacturing. Although CMS are able to provide a strategic operating advantage, machine cell clusters do not remain optimal over an extended period of time. The concept of a Dynamic CMS (DCMS) has received attention in recent years; a DCMS is a system where the layout of machines change in order to improve the responsiveness of CMS to changing production requirements. A deficiency in existing DCMS methods is that reconfiguration plans are generated without the consideration of an initial design of the factory floor space for future change. This research distinguishes Reconfigurable CMS (RCMS) from DCMS, as a system that is designed at the outset for changes to system layout and cell configurations. The concept of a Factory Configuration Template (FCT) is proposed in this research; the FCT is a design of the factory floor space to ensure the feasible implementation of reconfiguration plans generated by mathematical models. A nine step method for FCT design is presented that uses a Simultaneous Fuzzy Clustering Heuristic to develop manufacturing cells and part families. A Tabu Search algorithm was develop to generate the optimal arrangement of machine sites in cells. Three multi-period machine assignment models were developed that determine reconfiguration plans based on changing product demand and the introduction of new products. The models that were developed included two integer linear programs that determine the distribution of machine resources among cells over multiple periods. A quadratic zero-one programming model was developed that distributes machines among available sites in cells to promote unidirectional part flow. The results show that RCMS is able to provide a more economical solution than traditional CMS with the added advantage of improved part flow in the system

Relational clustering models for knowledge discovery and recommender systems

Cluster analysis is a fundamental research field in Knowledge Discovery and Data Mining (KDD). It aims at partitioning a given dataset into some homogeneous clusters so as to reflect the natural hidden data structure. Various heuristic or statistical approaches have been developed for analyzing propositional datasets. Nevertheless, in relational clustering the existence of multi-type relationships will greatly degrade the performance of traditional clustering algorithms. This issue motivates us to find more effective algorithms to conduct the cluster analysis upon relational datasets. In this thesis we comprehensively study the idea of Representative Objects for approximating data distribution and then design a multi-phase clustering framework for analyzing relational datasets with high effectiveness and efficiency. The second task considered in this thesis is to provide some better data models for people as well as machines to browse and navigate a dataset. The hierarchical taxonomy is widely used for this purpose. Compared with manually created taxonomies, automatically derived ones are more appealing because of their low creation/maintenance cost and high scalability. Up to now, the taxonomy generation techniques are mainly used to organize document corpus. We investigate the possibility of utilizing them upon relational datasets and then propose some algorithmic improvements. Another non-trivial problem is how to assign suitable labels for the taxonomic nodes so as to credibly summarize the content of each node. Unfortunately, this field has not been investigated sufficiently to the best of our knowledge, and so we attempt to fill the gap by proposing some novel approaches. The final goal of our cluster analysis and taxonomy generation techniques is to improve the scalability of recommender systems that are developed to tackle the problem of information overload. Recent research in recommender systems integrates the exploitation of domain knowledge to improve the recommendation quality, which however reduces the scalability of the whole system at the same time. We address this issue by applying the automatically derived taxonomy to preserve the pair-wise similarities between items, and then modeling the user visits by another hierarchical structure. Experimental results show that the computational complexity of the recommendation procedure can be greatly reduced and thus the system scalability be improved

Relational clustering models for knowledge discovery and recommender systems

Cluster analysis is a fundamental research field in Knowledge Discovery and Data Mining (KDD). It aims at partitioning a given dataset into some homogeneous clusters so as to reflect the natural hidden data structure. Various heuristic or statistical approaches have been developed for analyzing propositional datasets. Nevertheless, in relational clustering the existence of multi-type relationships will greatly degrade the performance of traditional clustering algorithms. This issue motivates us to find more effective algorithms to conduct the cluster analysis upon relational datasets. In this thesis we comprehensively study the idea of Representative Objects for approximating data distribution and then design a multi-phase clustering framework for analyzing relational datasets with high effectiveness and efficiency. The second task considered in this thesis is to provide some better data models for people as well as machines to browse and navigate a dataset. The hierarchical taxonomy is widely used for this purpose. Compared with manually created taxonomies, automatically derived ones are more appealing because of their low creation/maintenance cost and high scalability. Up to now, the taxonomy generation techniques are mainly used to organize document corpus. We investigate the possibility of utilizing them upon relational datasets and then propose some algorithmic improvements. Another non-trivial problem is how to assign suitable labels for the taxonomic nodes so as to credibly summarize the content of each node. Unfortunately, this field has not been investigated sufficiently to the best of our knowledge, and so we attempt to fill the gap by proposing some novel approaches. The final goal of our cluster analysis and taxonomy generation techniques is to improve the scalability of recommender systems that are developed to tackle the problem of information overload. Recent research in recommender systems integrates the exploitation of domain knowledge to improve the recommendation quality, which however reduces the scalability of the whole system at the same time. We address this issue by applying the automatically derived taxonomy to preserve the pair-wise similarities between items, and then modeling the user visits by another hierarchical structure. Experimental results show that the computational complexity of the recommendation procedure can be greatly reduced and thus the system scalability be improved.EThOS - Electronic Theses Online ServiceUniversity of WarwickUniversity of Warwick. Dept. of Computer ScienceGBUnited Kingdo

Footfall and the territorialisation of urban places measured through the rhythms of social activity

The UK high street is constantly changing and evolving in response to, for example, online sales, out-of-town developments, and economic crises. With over 10 years of hourly footfall counts from sensors across the UK, this study was an opportunity to perform a longitudinal and quantitative investigation to diagnose how these changes are reflected in the changing patterns of pedestrian activity. Footfall provides a recognised performance measure of place vitality. However, through a lack of data availability due to historic manual counting methods, few opportunities to contextualise the temporal patterns longitudinally have existed. This study therefore investigates daily, weekly, and annual footfall patterns, to diagnose the similarities and differences between places as social activity patterns from UK high streets evolve over time. Theoretically, footfall is conceptualised within the framework of Territorology and Assemblage Theory, conceptually underpinning a quantitative approach to represent the collective meso-level (street and town-centre) patterns of footfall (social) activity. To explore the data, the periodic signatures of daily, weekly, and annual footfall are extracted using STL (seasonal trend decomposition using Loess) algorithms and the outputs are then analysed using fuzzy clustering techniques. The analyses successfully identify daily, weekly, and annual periodic patterns and diagnose the varying social activity patterns for different urban place types and how places, both individually and collectively are changing. Footfall is demonstrated to be a performance measure of meso-scale changes in collective social activity. For place management, the fuzzy analysis provides an analytical tool to monitor the annual, weekly, and daily footfall signatures providing an evidence-based diagnostic of how places are changing over time. The place manager is therefore better able to identify place specific interventions that correspond to the usage patterns of visitors and adapt these interventions as behaviours change

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen