NHPP models for categorized software defects

We develop NHPP models to characterize categorized event data, with application to modelling the discovery process for categorized software defects. Conditioning on the total number of defects, multivariate models are proposed for modelling the defects by type. A latent vector autoregressive structure is used to characterize dependencies among the different types. We show how Bayesian inference can be achieved via MCMC procedures, with a posterior prediction‐based L‐measure used for model selection. The results are illustrated for defects of different types found during the System Test phase of a large operating system software development project. Copyright © 2005 John Wiley & Sons, Ltd.     

Modelling in the absence of data: a case study of fleet maintenance in a developing country

Adequate and relevant objective data for modelling maintenance decision problems are often incomplete or not readily accessible. This is particularly true in developing countries. In this paper the experience gained between 1991–95 in conducting a maintenance study of an inter-city express bus fleet in a developing country is presented. The lack of available maintenance records and operating data rendered the study the most data-starved maintenance modelling exercise the authors have met before or since. The study required the use of subjective methods to both define the problem and to estimate parameters, and the application of recently developed concepts in maintenance modelling along with snapshot analysis and delay time modelling. This imposed a structured approach to problem recognition and problem solution. The study contributed both directly and indirectly to a change in work culture and to a reduction in bus breakdown rate. The company was re-visited 5?years later specifically to seek evidence of lasting impact. Some evidence existed and is reported in the paper.     

How well do discrete element granular flow models capture the essentials of mixing processes?

Flowing granular materials, undergoing both mixing and segregation, play important roles in industries ranging from minerals and food to pharmaceuticals and ceramics. Sometimes it is desirable to enhance the mixing and inhibit segregation and in other cases it is desirable to minimise the mixing and enhance the segregation. The fundamentals of these processes are poorly understood. Computational modelling of such granular flows offers a good opportunity to study these fundamentals. But how well do these discrete element based modelling techniques capture the essential features of mixing processes? The capabilities of our discrete element modelling package are described. Two different methods for measuring the rates of mixing are presented and three different configurations are studied. Qualitatively reasonable flows are obtained. A detailed study of the mixing demonstrates that the amount and nature of the mixing is quite sensitive to a range of physical parameters.     

What is the role and contribution of models to management and research in the health services? A view from Europe

Modelling in different forms has long been regarded as a cornerstone of health OR as in other fields of application. Models are now tending to become a standard tool in health services management and research. What are the lessons we as operational researchers have learnt during this development? How can health care managers and health service researchers benefit from modelling — with or without OR-analysts to guide them between the pitfalls? After an introductory overview concerning the nature and objectives of modelling, examples will be given of modelling applications from different health service areas in order to illustrate the versatility of the method. Further, the choice of methods and models is discussed with special attention to the problems of interpretation and implementation of results. From this overview some conclusions are drawn with regards to advantages and disadvantages of modelling as tools for policy planning and decision-making in the health area. Finally some observations are made concercing the conditions for the future development in the field.     

Knowledge-based time-critical dynamic decision modelling

Recent interests in dynamic decision modelling have led to the development of several representation and inference methods. These methods however, have limited application under time-critical conditions where a trade-off between model quality and computational tractability is essential. This paper presents a knowledge-based approach to time-critical dynamic decision modelling. A knowledge representation and modelling method, called the time-critical dynamic influence diagram, is proposed. The proposed approach has the ability to represent space-temporal abstraction in dynamic decision models. Several algorithms from different classes for solving time-critical dynamic influence diagrams are described. A knowledge-based meta-reasoning approach is proposed for the purpose of selecting the best abstracted model and algorithm that provide the optimal trade-off between model quality and model tractability. The approach is applied to solve a time-critical medical decision problem. An outline of the knowledge-based model construction procedure is also provided.     

A Case Study of Animated Computer Simulation in the Australian Mining Industry

Computer Simulation is a well-established technique for modelling complex problems. Australian mining has, until quite recently, relied on more ad hoc methods for planning and operating. This case study describes how a large ore mine was successfully studied using animated computer simulation and points out how this approach can overcome traditional resistance to formal models and how useful results were readily obtained and accepted by management.     

Stakeholder preference elicitation and modelling in multi-criteria decision analysis – A case study on urban water supply

Integration of multiple objectives to evaluate the alternative operating rules for urban water supply reservoir systems can be effectively accomplished by multi-criteria decision aid techniques, where preference elicitation and modelling plays an important role. This paper describes a preference elicitation and modelling procedure involving the multi-criteria outranking method PROMETHEE in evaluating these alternative operating rules. The Melbourne water supply system was considered as the case study. Eight performance measures (PMs) were identified under four main objectives to evaluate the system performance under alternative operating rules. Three major hypothetical stakeholder groups namely, resource managers, water users, and environmental interest groups were considered in decision-making. An interviewer-assisted questionnaire survey was used to derive the preference functions and weights of the PMs. The evaluation of alternative operating rules is not covered in this paper, rather an approach to elicit and model stakeholder preferences in decision-making is described.     

Propensity score modelling in observational studies using dimension reduction methods

Conditional independence assumptions are very important in causal inference modelling as well as in dimension reduction methodologies. These are two very strikingly different statistical literatures, and we study links between the two in this article. The concept of covariate sufficiency plays an important role, and we provide theoretical justification when dimension reduction and partial least squares methods will allow for valid causal inference to be performed. The methods are illustrated with application to a medical study and to simulated data.     

The impact of special days in call arrivals forecasting: A neural network approach to modelling special days

A key challenge for call centres remains the forecasting of high frequency call arrivals collected in hourly or shorter time buckets. In addition to the complex intraday, intraweek and intrayear seasonal cycles, call arrival data typically contain a large number of anomalous days, driven by the occurrence of holidays, special events, promotional activities and system failures. This study evaluates the use of a variety of univariate time series forecasting methods for forecasting intraday call arrivals in the presence of such outliers. Apart from established, statistical methods, we consider artificial neural networks (ANNs). Based on the modelling flexibility of the latter, we introduce and evaluate different methods to encode the outlying periods. Using intraday arrival series from a call centre operated by one of Europe’s leading entertainment companies, we provide new insights on the impact of outliers on the performance of established forecasting methods. Results show that ANNs forecast call centre data accurately, and are capable of modelling complex outliers using relatively simple outlier modelling approaches. We argue that the relative complexity of ANNs over standard statistical models is offset by the simplicity of coding multiple and unknown effects during outlying periods.     

A Queueing Framework for Routing Problems with Time-dependent Travel Times

Assigning and scheduling vehicle routes in a dynamic environment is a crucial management problem. Despite numerous publications dealing with efficient scheduling methods for vehicle routing, very few addressed the inherent stochastic and dynamic nature of travel times. In this paper, a vehicle routing problem with time-dependent travel times due to potential traffic congestion is considered. The approach developed introduces the traffic congestion component based on queueing theory. This is an innovative modelling scheme to capture the stochastic behavior of travel times as it generates an analytical expression for the expected travel times as well as for the variance of the travel times. Routing solutions that perform well in the face of the extra complications due to congestion are developed. These more realistic solutions have the potential to reduce real operating costs for a broad range of industries which daily face routing problems. A number of datasets are used to illustrate the appropriateness of the novel approach. Moreover it is shown that static (or time-independent) solutions are often infeasible within a congested traffic environment which is generally the case on European road networks. Finally, the effect of travel time variability (obtained via the queueing approach) is quantified for the different datasets.      

Student problems with a modelling exercise

In this paper an attempt is made to outline some of the problems facing the student who undertakes a course in mathematical modelling. A case study, considered by several cohorts of students, is used as a medium for introducing the various types of obstacles the students encounter. This approach also results in a demonstration of some of the errors that can be made in operating a modelling course and the paper concludes by offering suggestions on how to overcome certain difficulties that are exposed.     

Modelling on fuzzy control systems

A kind of modelling method for fuzzy control systems is first proposed here, which is called modelling method based on fuzzy inference (MMFI). It should be regarded as the third modelling method that is different from two well-known modelling methods, that is, the first modelling method, mechanism modelling method (MMM), and the second modelling method, system identification modelling method (SIMM). This method can, based on the interpolation mechanism on fuzzy logic system, transfer a group of fuzzy inference rules describing a practice system into a kind of nonlinear differential equation with variable coefficients, called HX equations, so that the mathematical model of the system can be obtained. This means that we solve the difficult problem of how to get a model represented as differential equations on a complicated or fuzzy control system.     

Modelling nationwide hospital length of stay: opening the black box

Hospital length of stay is considered to be a reliable and valid proxy for measuring the consumption of hospital resources. Average length of stay, however, albeit easy to quantify and calculate, does not suitably reflect the nature of such underlying distributions and may therefore mask the effects that the different streams of patients have on the system. This paper uses routinely collected and readily available nationwide data on stroke-related patients, aged 65 years and over, who were discharged from English hospitals over a 1-year period. This will be the basis for a running example illustrating the alternative methods of analysis and models of patients' length of stay. The methods include statistical methods: survival analysis, mixed exponential and phase-type distributions; and decision modelling techniques: compartmental and simulation models. The paper concludes by summarizing these various modelling techniques and by highlighting the similarity of the estimated parameters of patient flow as calculated by the phase-type distribution and compartmental modelling techniques.     

A general fuzzy control scheme for nonlinear processes with stability analysis

In this paper we develop a general fuzzy control scheme for nonlinear processes. Assuming little knowledge about the dynamics of the controlled process, the proposed scheme starts by probing the process at different points in its operating region to generate a fuzzy quantisation. A simple local controller is then designed at each fuzzy locality. A fuzzy inference mechanism then links up tje local controllers to form a global controller which can be further refined by the learning algorithm. By employing a newly developed structure-adaptive fuzzy modelling scheme, the appropriate fuzzy rule-base for the inference mechanism can be extracted stably and efficiently. The conditions for the stability of the global controller are rigourously established. Simulation results are presented to illustrate the effectiveness of the scheme.     

Reducing depot-related costs of large bus operators a case study in Bangkok

Despite high passenger levels, many bus operators in large cities operate at a loss because of low fares and inefficient operations management. Methods to reduce the operating costs need to be adapted to the operational environment. This paper addresses one such “unproductive” cost: the idle running of buses between depots and routes. Models are developed to tackle the bus allocation problem in which many different brands of buses exist for which maintenance is organised in the depots. Based on an assessment using 1992 data for the city of Bangkok, a hierarchical modelling approach is retained. It is shown that important cost savings can be made.     

Competitive dynamics and gaming simulation: lessons from a fishing industry simulator

Experiments with a gaming simulator of the fishing industry show that a wide range of firm and industry performance can arise from players’ differing perceptions of the competitive environment. When transferred into the arena of applied strategy development, the results suggest that modellers should give more attention to modelling alternative conceptualizations of strategic intent in the minds of rival business leaders and the firms they create. This new interpretive emphasis should complement the traditional modelling of cross-functional coordination that has been the hallmark of much published work in strategic modelling and simulation. When leaders and firms in the same industry adopt quite different views of the overall system of resources in the industry, it is important to model the heterogeneity of rival firms in order to understand the dynamic performance of the firm and the industry. We propose a modelling approach that captures heterogeneity among the strategic resources that rival firms seek to build and in the operating goals and coordinating processes they use to build them.     

传染病模型的拓展与计算

以经典传染病模型为例,清晰解读模型构造思想,将建模思想应用于埃博拉病毒传播中,拓展传染病模型,并从连续角度和离散角度阐述传染病模型求解技巧。模型的构造思想和求解技术可自然推广至经济模型、人口模型等问题中。     

Solution of Inverse Task of Mechatronic Systems by Means the Classical and Unclassical Methods of Modelling and Analysis

In this paper the modelling by means of the different category graphs and analysis of vibrating subsystem of mechatronic systems by means of the exact and approximate methods have been presented. That approach was to nominate the relevance or irrelevance between the characteristics obtained by means of the exact method (only for the mechanical subsystem) and the approximate method. Such formulation concerns mostly the relevance of the natural frequencies-poles of the characteristics both mechanical subsystems and mechatronic systems. Approximate solutions requiring all the conditions for vibrating mechanical and/or mechatronic systems have been finding and it may be introduction to synthesis of these systems, modeled by different category graphs. Research limitation is following, that torsional vibrating continuous mechanical subsystem and mechatronic systems are linear type. Presentation the introduction to synthesis of considered class of mechatronic bar-systems with a constant changeable cross-section is practical implications of this work. Originality of such formulation is focused on the use of the different category graphs for modelling and synthesising by different methods represented by graphs of vibrating bars to the synthesis of considered discrete-continuous mechatronic systems. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Decision development in facilitated modelling workshops

While the facilitated modelling literature recognises the importance of the group process within facilitated modelling workshops, published empirical research rarely examines their dynamic nature. In this paper, we address this gap in the literature in two ways. First, we propose to locate facilitated modelling workshops as the main focus of investigation, and adopt decision development as the analytical lens. Second, we provide guidance regarding how to implement a research strategy that is informed by such a focus. We start by mapping the different conceptualisations of decision development that seem embedded within the facilitated modelling tradition, and contrast them with theoretical models from the group communication field. Our analysis identifies a number of potentially useful areas for the study of facilitated modelling workshops from a decision development perspective, and articulates a number tentative research questions and testable propositions amenable to empirical research. Central to our proposal are research methods for the study of dynamic group processes. We thus discuss the steps required to extract group process data from facilitated modelling workshops that are usable and open to analysis. This includes a review of issues regarding research design, coding scheme development, data coding and choice of analytical techniques. Finally, we offer conclusions and briefly discuss some feasibility issues related to the implementation of our proposal.