Peter Broomhead

Event Tracking for Real-Time Unaware Sensitivity Analysis (EventTracker)

This paper introduces a platform for online Sensitivity Analysis (SA) that is applicable in large scale real-time data acquisition (DAQ) systems. Here, we use the term real-time in the context of a system that has to respond to externally generated input stimuli within a finite and specified period. Complex industrial systems such as manufacturing, healthcare, transport, and finance require high-quality information on which to base timely responses to events occurring in their volatile environments. The motivation for the proposed EventTracker platform is the assumption that modern industrial systems are able to capture data in real-time and have the necessary technological flexibility to adjust to changing system requirements. The flexibility to adapt can only be assured if data is succinctly interpreted and translated into corrective actions in a timely manner. An important factor that facilitates data interpretation and information modeling is an appreciation of the affect system inputs have on each output at the time of occurrence. Many existing sensitivity analysis methods appear to hamper efficient and timely analysis due to a reliance on historical data, or sluggishness in providing a timely solution that would be of use in real-time applications. This inefficiency is further compounded by computational limitations and the complexity of some existing models. In dealing with real-time event driven systems, the underpinning logic of the proposed method is based on the assumption that in the vast majority of cases changes in input variables will trigger events. Every single or combination of events could subsequently result in a change to the system state. The proposed event tracking sensitivity analysis method describes variables and the system state as a collection of events. The higher the numeric occurrence of an input variable at the trigger level during an event monitoring interval, the greater is its impact on the final analysis of the system state. Experiments were designed to compare the proposed event tracking sensitivity analysis method with a comparable method (that of Entropy). An improvement of 10 percent in computational efficiency without loss in accuracy was observed. The comparison also showed that the time taken to perform the sensitivity analysis was 0.5 percent of that required when using the comparable Entropy-based method.

Improving ant colony optimization performance through prediction of best termination condition

The Ant Colony System (ACS) is a well-known bio-inspired optimization algorithm which has been successfully applied to several NP-hard optimization problems, including transportation network optimization. This paper introduces a method to improve the computational time required by the algorithm in finding high quality solutions. The purpose of the method is to predict the best termination iteration for an unseen instance by analyzing the performance of the optimization process on solved instances. A fitness landscape analysis is used to understand the behavior of the optimizer on all given instances. A comprehensive set of features is presented to characterize instances of the transportation network optimization problem. This set of features is associated to the results of the fitness landscape analysis through a machine learning-based approach, so that the behavior of the optimization algorithm may be predicted before the optimization start and the termination iteration may be set accordingly. The proposed system has been tested on a real-world transportation network optimization problem and two randomly generated problems. The proposed method has drastically reduced the computational times required by the ACS in finding high quality solutions.

Composite goal methods for transportation network optimization

We review the state of art in multi-goal real-world supply chain optimization.From the review, we identify four generic strategies.We implement and test seven instances of these strategies.In pursuit of clarity, we formally describe the implemented strategies.We highlight the poor performance of the most adopted method in the literature. Lately the topic of multi-objective transportation network optimization has received increased attention in the research literature. The use of multi-objective transportation network optimization has led to a more accurate and realistic solution in comparison to scenarios where only a single objective is considered. The aim of this work is to identify the most promising multi-objective optimization technique for use in solving real-world transportation network optimization problems. We start by reviewing the state of the art in multi-objective optimization and identify four generic strategies, which are referred to as goal synthesis, superposition, incremental solving and exploration. We then implement and test seven instances of these four strategies. From the literature, the preferred approach lies in the combination of goals into a single optimization model (a.k.a. goal synthesis). Despite its popularity as a multi-objective optimization method and in the context of our problem domain, the experimental results achieved by this method resulted in poor quality solutions when compared to the other strategies. This was particularly noticeable in the case of the superposition method which significantly outperformed goal synthesis.

A generic platform to address event-based problems for universal applications: supervisory control and simulation

This paper proposes the framework and implementation architecture of a combined real-time shopfloor data collection (monitor) and Discrete Event Simulation (DES) system. It highlights the practical implementation and potential benefits of using predictive (multipass) simulation in combination with real-time Data Acquisition (DAQ) in dealing with the complexity of manufacturing environments. Normally, by nature, these environments face unpredictable events that challenge their predetermined plans and schedules. The proposed framework makes the development of a generic hybrid control capability for flexible manufacturing environments possible. This paper describes a solution that combines the advantages of real-time shopfloor control and DAQ systems and mathematical modelling tools such as DES that rely on historical data for improved system performance. It bridges the gap that currently exists between shopfloor level control/monitoring systems and higher level enterprise information systems such as Material Requirement Planning (MRP/MRP II) and Enterprise Resource Planning (ERP).

Transportation Network Optimization

Although information and communication technologies (ICT) were an important issue for Travel & Tourism (T&T) since the 1960s (i.e. computer reservations systems, global distribution systems; Werthner & Klein, 1999), the difference today is that ICT has become a strategic issue for every business (Buhalis, 2006). The special benefit tourism gains from ICT can be put down to the characteristics of the tourism product, being a service bundle ideally portrayed by electronic media and being jointly delivered by (usually) small-sized enterprises. Indeed, T&T is a highly information intensive sector, and not surprisingly, within the e-Commerce sector T&T represents the largest branch. In 2009, 25.7% (€ 65.2 Bn.) of the EU online sales volume has been generated by the T&T sector, whereat in 2001 this figure stood only at € 5 Bn. (Marcussen, 2009). Moreover, in the US already 59% of the total travel revenue is generated online (NewMedia TrendWatch, 2012). However, although tourism shows high penetration rates with respect to Web-based marketing & distribution, shortcomings become evident with respect to e-business networks (supply-chains) and integrated (internal) process automation (e-procurement, enterprise resource planning, etc.). Finally, most significant adoption gaps are ascertained for ICTs in tourism SMEs to support market research, product development and strategic decision making (eBusiness Watch, 2006). The attractiveness of tourism destinations particularly depends on how communication and information needs of tourism stakeholders can be satisfied through information and communication technology (ICT)-based infrastructures, so that sustainable knowledge sources can emerge (Buhalis, 2006). Although huge amounts of customerbased data are widespread in tourism destinations (e.g. Web-servers store tourists’ Website navigation, data bases save transaction and survey data, respectively), these valuable knowledge sources typically remain unused (Pyo, 2005). However, managerial effectiveness and organisational learning could be significantly enhanced by applying methods of business intelligence (BI; Sambamurthy & Subramani, 2005; Wong et al., 2006; Shaw & Williams, 2009), offering highly reliable, up-to-date and strategically relevant information, such as tourists’ travel motives and service expectations, information needs, channel use and related conversion rates, occupancy trends, quality of service experience and added value per guest segment (Min et al., 2002; Pyo et al., 2002). This makes clear why ICT and methods of BI are playing a crucial role in effectuating a knowledge destination by enhancing large-scale intra and inter-firm knowledge exchange. Indeed, the major challenge of knowledge management for tourism destinations is to make individual knowledge about customers, products, processes, competitors or business partners available and meaningful to others (Back et al., 2007). Wolfram Höpken University of Applied Sciences Ravensburg-Weingarten, GermanyIndustrialized house-building refers to an efficiently managed construction process based in technical platforms, using highly developed off-site manufactured, modularized, technicalfunctional house-building components or modules (Lessing, 2006). Project design in this context is seen as a process of configuration where variable or interchangeable parts of the technical platform are determined (Hvam et al., 2008). Architectural design deals with complex problems that, aside from technical aspects, also concern user functionality and aesthetics. During the traditional design process, architects have a central role in coordinating different requirements concerning use and construction. This is often done in an iterative manner, defining problems and solutions in parallel. This approach is, however, unsuited for the industrialized house-building design process, where platform development is separated from project design. In design practice, the configuration system for a technical platform contains information about the platform’s technical parts and restrictions in design (Olofsson et al., 2004). This means that the configuration system normally does not include information about user functionality and aesthetics, i.e. if the platform can fulfill the client’s expectations concerning spatial use or aesthetics from a comprehensive architectural view. This information is still supposed to be managed independently by the architect, prior to configuration, in what could be considered a traditional early design process. If information about user activities and aesthetics could be included and managed in a transparent way by the configuration system, early design could be fully integrated in an industrialized design process. This chapter presents the results of research with the aim of investigating premises for architectural design as part of an industrialized house-building design process, focusing on three areas of importance: (1) support for architectural design in platform development and modularization; (2) support for architectural design in product configuration; and (3) organization of design information to support architectural design. The implication of the results of this research is a better understanding of how to use technical platforms as a viable business alternative for a broader spectrum of construction projects, including enhanced knowledge of how to manage and overcome the perceived constraints concerning architectural freedom in the design process when using technical platforms.

EventiC: A Real-Time Unbiased Event Based Learning Technique for Complex Systems

An improved method for the real time sensitivity analysis in large scale complex systems is proposed in this paper. The method borrows principles from the event tracking of interrelated causal events and deploys clustering methods to automatically measure the relevance and contribution made by each input event data (ED) on system outputs. The ethos of the proposed event modeling (EM) technique is that the behavior or the state of a system is a function of the knowledge acquired about events occurring in the system and its wider operational environment. As such it builds on the theoretical and the practical foundation for the engineering of knowledge and data in modern and complex systems. The proposed EM platform EventiC filters noncontributory ED sources and has the potential to include information that was initially thought irrelevant or simply not considered at the design stage. The real-time ability to group and rank relevant input–output ED in order of its importance and relevance will not only improve the data quality, but leads to an improved higher level of mathematical formulization in the modern complex systems. The contribution of the approach to systems’ modeling is in the automation of data analysis, control, and plant process modeling. EventiC has been validated as the monitoring and the control system for a cement factory. In addition to the previously known parameters, the proposed EventiC identified new influential parameters that were previously unknown. It also filtered 18% of the input data without compromising the data quality or the integrity. The solution has improved the quality of input variable selection and simplify plant control strategies.