Andrew M. Tobias

Simplification in the simulation of manufacturing systems

Given the widespread acceptance of the importance of simplicity in management science models, the scarcity of research into simplification is perhaps surprising. In the simulation of manufacturing systems, simplification is often not attempted and, on the (misguided) assumption that more detailed models are necessarily more accurate and therefore better, common practice is to build and use the most complex model that can be built in the time available. However, for cases where the only results required are averages, such as long term throughput rates, it will often be possible to reduce the model to such a simple version that an analytical solution becomes feasible and the simulation redundant. An eight stage procedure is proposed for doing the reductions and two manufacturing case studies are described.

Railway Point Mechanisms: Condition Monitoring and Fault Detection

Early attempts at monitoring the condition of railway point mechanisms employed simple thresholding techniques to detect faults, but success was limited and there were large numbers of false alarms and missed failures in the field. More recent research using data collected from line-side equipment and lab-based test rigs, though, is suggesting that it should indeed be possible to predict failures with sufficient accuracy and notice to be of genuine use to infrastructure maintainers and owners. This review into state-of-the-art predictive fault detection and diagnosis methods shows how some very different generic models have been tailored to the various types of mechanisms that are in use worldwide. In any specific case, the most appropriate combination of quantitative and qualitative techniques will be determined by the inherent failure modes of the system and the particular conditions under which it operates. Furthermore, it is vital to have a priori knowledge of the symptoms that are observable under fault conditions if diagnosis is to be reliable.

Determining the range of predictions of a groundwater model which arises from alternative calibrations

A major element in constructing a groundwater model is choosing the parameter values. The traditional approach is to aim for a single best set of values. The parameters used in a model are effective rather than measurable, and this combined with the inherent uncertainties in the modeling process means that there are often many plausible sets of values. A single prediction obtained from a single set of parameter values is not appropriate, but rather the range in predictions from the alternative calibrations should be used. A method is presented for finding the best case and worst case predictions among the plausible parameter sets and is applied to a real case study. Widely different feasible parameter sets were found giving significantly different predictions.

Organizational and Technological Implications of Cognitive Machines: Designing Future Information Management Systems

Organizational cognition concerns the processes which provide agents and organizations with the ability to learn, make decisions, and solve problems.Organizational and Technological Implications of Cognitive Machines: Designing Future Information Management Systems presents new challenges and perspectives to the understanding of the participation of cognitive machines in organizations. Containing extensive research by an international collaboration of experts, this book addresses the possible implications of cognitive machines for current and future organizations.

A new contingency view of the organization: mananging complexity and uncertainty through cognition

This paper proposes a new contingency view of the organization and it contributes to the theme through two complementary perspectives. First, it proposes cognition as a function which acts as the main mediator between the organization and the environment. Second, it introduces cognition as the core organizational ability which supports individuals, groups and organizations with intelligence, autonomy, learning and knowledge management, whereas, in such a perspective, cognition is viewed as the core resource in the service of the organization. Both perspectives, the mediation and the core organizational resource views, imply that cognition contributes toward managing environmental complexity and uncertainty. From this picture, this work analyzes the organization in the pursuit of high degrees of organizational cognition in order to manage high levels of environmental complexity and uncertainty. Grounded in these views, this paper presents a model of the organization as a set of fuzzy abilities. From all these backgrounds, this research opens new directions for future research on organizational abilities which subsume cognition, intelligence, autonomy, learning and knowledge management as important elements of organizational analysis.

The impact of cognitive machines on complex decisions and organizational change

Humans and organizations have limitations of computational capacity and information management. Such constraints are synonymous with bounded rationality. Therefore, in order to extend the human and organizational boundaries to more advanced models of cognition, this research proposes concepts of cognitive machines in organizations. From a micro point of view, what makes this research distinct is that, beyond people, it includes in the list of participants of the organization the cognitive machines. From a macro point of view, this paper relies on the premise that cognitive machines can improve the cognitive abilities of the organization. From such perspectives, it presents rationale and principles of a class of cognitive machines with capabilities to carry out complex cognitive tasks in organizations. It also introduces analyses of the cognitive machines in organizations through theories of bounded rationality, economic decision-making, and conflict resolution. The analyses indicate that these machines can solve or reduce intra-individual and group dysfunctional conflicts which arise from decision-making processes in the organization, and thus they can improve the degree of organizational cognition. From all these backgrounds, this research outlines implications of cognitive machines for organizations.

The population genetics of the self-incompatibility polymorphism in Papaver rhoeas . XI. The effects of limited pollen and seed dispersal, overlapping generations and variation in plant size on the variance of S-allele frequencies in populations at equilibrium

The effects of limited pollen and seed dispersal, of overlap between generations and of variation in plant size on the steady-state variance of S-allele frequencies have been investigated in a simulated population of size 3840 containing 16 S-alleles whose initial frequencies were exactly equal. Simulations were run with each of the 16 possible combinations of these four factors to investigate their effects on the time for the population to reach steady state and on the average variance of S-allele frequencies in steady state. The time to steady state appeared to be relatively unaffected by any of the factors and was about 50 generations. However, the steady-state variance was markedly affected, with variation in plant size increasing this variance by an average of 228 per cent and overlapping generations decreasing the variance by an average of 30 per cent. The effects of limited pollen and seed dispersal were individually small, although their combined effect was to increase the steady-state variance by an average of 12 per cent. Limited seed and pollen dispersal, when combined with variation in plant size, caused the alleles to cluster. The four factors together caused a large increase in the average steady-state variance. Furthermore, even when a population is in steady state, the variance for a particular generation can be considerably greater than this average value. Consequently the frequencies of the S-alleles of a population in steady state can be very different. It is possible, therefore, that the large variation in S-allele frequencies found in samples taken from Papaver rhoeas populations is consistent with their being in steady state.

Managing incidents in a complex system: a railway case study

The authors of this paper review how complex entities, composed of many interdependent subsystems, such as international rail operators, can improve their ability to recover from incidents through the better management of key interfaces. The principles of Normal Accident Theory and resilience engineering are discussed, and the case study of the Eurostar incident of 18–19 December 2009 is considered in detail. Lessons learnt from resilience engineering are applied to the case study to extract recommendations by which incident management for open access international rail transport may be improved.

B-Spline Approach for Failure Detection and Diagnosis on Railway Point Mechanisms Case Study

ABSTRACT In railway transportation the safety and reliability in operations of railway point mechanism must be ensured in order to improve the quality of the service. This article presents a case study in a railway turnout (U.S.: switch). The case study reports how the effect of the operating force data sampled and monitored during the switching of a railway point mechanism can be converted into continuous polynomial B-spline functions. These functions are employed to define, and periodically to update, tolerance bands for the purpose of condition monitoring. Data from variously faulty mechanisms were converted similarly, and the profiles found to differ sufficiently not only to detect 100 percent of faults but, from the distinctive shapes of the profiles, to diagnose (i.e., distinguish correctly between) some 70–80 percent of them.

Decision Support at the Wheel-rail Interface: The Development of System Functional Requirements

Abstract Railway decision support systems (DSSs) tend to address track and vehicle assets separately. This paper describes the development of a comprehensive set of high-level functional requirements for an integrated support system that optimizes the system as a whole. Interviews and workshops were held with key industry stakeholders covering regulation, infrastructure, condition monitoring, mass transit, and light rail to elicit their issues, needs, and desires. The needs were checked for relevance, duplication, and overlapping; and rephrased, decomposed, grouped, classified, and prioritized in order to achieve a consistent, complete, and unambiguous set of system stakeholder requirements. They were then used to derive a set of high-level system functional requirements for the DSS. The paper also demonstrates the importance of complete traceability between the two sets of requirements.