Building prior distributions to support Bayesian reliability growth modelling using expert judgement

A sound methodology for the elicitation of subjective expert judgement is a pre-requisite for specifying prior distributions for the parameters of reliability growth models. In this paper, we describe an elicitation process that is developed to ensure valid data are collected by suggesting how possible bias might be identified and managed. As well as discussing the theory underpinning the elicitation process, the paper gives practical guidance concerning its implementation during reliability growth testing. The collection of subjective data using the proposed elicitation process is embedded within a Bayesian reliability growth modelling framework and reflections upon its practical use are described.     

An Application of Six Sigma to Reduce Waste

Six Sigma has been considered a powerful business strategy that employs a well‐structured continuous improvement methodology to reduce process variability and drive out waste within the business processes using effective application of statistical tools and techniques. Although there is a wider acceptance of Six Sigma in many organizations today, there appears to be virtually no in‐depth case study of Six Sigma in the existing literature. This involves how the Six Sigma methodology has been used, how Six Sigma tools and techniques have been applied and how the benefits have been generated. This paper presents a case study illustrating the effective use of Six Sigma to reduce waste in a coating process. It describes in detail how the project was selected and how the Six Sigma methodology was applied. It also shows how various tools and techniques within the Six Sigma methodology have been employed to achieve substantial financial benefits. Copyright © 2005 John Wiley & Sons, Ltd.     

Methodology for estimating probability of success of Escape,Evacuation, and Rescue (EER) strategies for arctic offshore facilities

Offshore oil and gas production platforms must have Escape, Evacuation, and Rescue (EER) plans and resources to protect personnel in the event of a major accident. However, for offshore platforms in arctic regions, existing solutions may not be sufficient due to the extreme environmental conditions. This paper presents a methodology for evaluating the relative probabilities of success of arctic EER strategies to (1) help prioritize technology development and data collection efforts needed to develop and select a robust EER strategy and (2) analyze the predicted probabilities of success of various EER strategies in more detail once the input parameters are better known. The methodology is based on well known risk assessment tools (event trees and Monte Carlo simulation) that can easily be adjusted to account for the appropriate level of detail. As an example, the method is applied to hypothetical EER strategies composed of various combinations of helicopters, ice-breaking platform supply vessels, Air Cushioned Vehicles, Tracked Amphibious Vehicles, and ice-strengthened lifeboats. Input parameters used in the analysis include prevalence of various environmental conditions by month (low temperature, high seas, low visibility, etc.), ability to deploy craft in a hazardous situation, ability to transit over ice ridges, ability to transit heavy seas, operator competence, etc.     

Data assimilation in the decision support system rodos

Model predictions for a rapid assessment and prognosis of possible radiological consequences after an accidental release of radionuclides play an important role in nuclear emergency management. Radiological observations, e.g. dose rate measurements, can be used to improve such model predictions. The process of combining model predictions and observations, usually referred to as data assimilation, is described in this article within the framework of the real time on-line decision support system (RODOS) for off-site nuclear emergency management in Europe. Data assimilation capabilities, based on Kalman filters, are under development for several modules of the RODOS system, including the atmospheric dispersion, deposition, food chain and hydrological models. The use of such a generic data assimilation methodology enables the propagation of uncertainties throughout the various modules of the system. This would in turn provide decision makers with uncertainty estimates taking into account both model and observation errors. This paper describes the methodology employed as well as results of some preliminary studies based on simulated data.     

Solving black box computation problems using expert knowledge theory and methods

The challenge problems for the Epistemic Uncertainty Workshop at Sandia National Laboratories provide common ground for comparing different mathematical theories of uncertainty, referred to as General Information Theories (GITs). These problems also present the opportunity to discuss the use of expert knowledge as an important constituent of uncertainty quantification. More specifically, how do the principles and methods of eliciting and analyzing expert knowledge apply to these problems and similar ones encountered in complex technical problem solving and decision making? We will address this question, demonstrating how the elicitation issues and the knowledge that experts provide can be used to assess the uncertainty in outputs that emerge from a black box model or computational code represented by the challenge problems. In our experience, the rich collection of GITs provides an opportunity to capture the experts' knowledge and associated uncertainties consistent with their thinking, problem solving, and problem representation. The elicitation process is rightly treated as part of an overall analytical approach, and the information elicited is not simply a source of data. In this paper, we detail how the elicitation process itself impacts the analyst's ability to represent, aggregate, and propagate uncertainty, as well as how to interpret uncertainties in outputs. While this approach does not advocate a specific GIT, answers under uncertainty do result from the elicitation.     

Synthesis of energy-induced waste minimization networks (EIWAMINs) for simultaneous waste reduction and heat integration

 This article introduces a process synthesis methodology for identifying cost-effective Energy-Induced Waste Minimization Networks (EIWAMINs). The synthesis methodology is targeted toward retrofit designs for an existing plant or site where environmental emissions (aqueous or gaseous) must be reduced. This approach uses heat-induced separation units that separate mass via a phase change (condensers, dryers, crystallizers, evaporators, etc.) and heat exchangers to simultaneously address waste minimization and heat integration within a large plant or site. While heat-induced separators may be employed for optimal waste separation and allocation (via a phase change), heat exchange equipment (e.g. heaters, coolers, chillers, etc.) may be employed to improve the thermodynamics of existing unit operations. In addition, pressurization/depressurization devices (compressors, turbines, etc.) are also employed to enhance the effectiveness of the heat-induced separators. The problem is tackled via two mathematical formulations which are mixed-integer non-linear programs (MINLPs). The mathematical formulations are combined to identify the most cost-effective design(s) that simultaneously addresses the plant waste minimization and heat integration tasks. Linearization techniques and graphical insights are incorporated to simplify the mathematical formulation of the problem. A case study is provided to highlight the features of this design procedure and its economic merits against several alternative designs. Received: 19 November 1998 / Accepted: 21 January 1999     

A hierarchical process monitoring strategy for a serial multi-stage manufacturing system

This paper illustrates the process monitoring strategy for a multistage manufacturing facility with the aid of cluster analysis and multiple multi-block partial least squares (MBPLS) models. Traditionally, a single MBPLS model is used for monitoring multiple process and quality characteristics. However, modelling all the responses together in a single model may cause poor model fit in the events of: (i) uncorrelated response variables; and (ii) groups of response variables having high correlation amongst the variables within a group but no or negligible correlations between the groups. This paper overcomes this problem by combining cluster analysis with MBPLS through development of multiple MBPLS models. Each of the MBPLS models is used to detect out-of-control observations and a superset of the out-of-control observations is created. Two new fault diagnostic statistics for stage-wise and variable-wise contribution are developed for the superset. The developed methodology is applied to a steel making shop for monitoring. The case study results show that the proposed methodology performs better as compared to the traditionally employed single MBPLS model.     

Investigations of ion induced re-emission using the thermal desorption technique

The technique of thermal desorption of previously trapped inert gas atoms from polycrystalline tungsten has been used to continue our study of the ion-induced re-emission process. It is shown that preferential reduction in gas atom densities from different trapping centres below the target surface may be effected by bombardment with a suitable secondary ion type and energy. Results fit our model which suggests a maximum in the gas sputtering process when the collision cascade produced by the secondary ion overlaps the range distribution of the primary specie.     

Challenges to Bayesian decision support using morphological matrices for design: empirical evidence

A novel Bayesian design support tool is empirically investigated for its potential to support the early design stages. The design support tool provides dynamic guidance with the use of morphological design matrices during the conceptual or preliminary design stages. This paper tests the appropriateness of adopting a stochastic approach for supporting the early design phase. The rationale for the stochastic approach is based on the uncertain nature of the design during this part of the design process. The support tool is based on Bayesian belief networks (BBNs) and uses a simple but effective information content–based metric to learn or induce the model structure. The dynamically interactive tool is assessed with two empirical trials. First, the laboratory-based trial with novice designers illustrates a novel emergent design search methodology. Second, the industrial-based trial with expert designers illustrates the hurdles that are faced when deploying a design support tool in a highly pressurised industrial environment. The conclusion from these trials is that there is a need for designers to better understand the stochastic methodology for them to both be able to interpret and trust the BBN model of the design domain. Further, there is a need for a lightweight domain-specific front end interface is needed to enable a better fit between the generic support tool and the domain-specific design process and associated tools.     

Randomly reinforced composites: properties, failure and aspects of material design

This article concerns several aspects of mechanical properties and tensile failure of randomly reinforced, chopped fiber strand, polymeric composites. Scatter in material properties and their dependence on sample size are investigated by means of an analytical/computational model and compared against the experimental data. Predictions are also provided for deviations from isotropy and homogeneity. Failure is modeled in a manner akin to the methodology employed for laminated composites and predictions are compared against the experimental data. The model is utilized to select combinations of multi-size strands so as to minimize material inhomogeneity.     

Performance assessment of run-to-run control in semiconductor manufacturing based on IMC framework

The objective of this paper is to propose a universal methodology for performance assessment of run-to-run control in semiconductor manufacturing. The slope of the linear semiconductor process model is assumed to be known or subjected to mild plant/model mismatch. Based on an internal model control framework, analytical expressions of minimum variance performance (MVP) and best achievable performance (BAP) for a series of run-to-run control schemes are derived. In the methodology, closed-loop identification is utilised as the first step to estimate the noise dynamics via routine operating data, and numerical optimisation is employed as a second step to calculate the best achievable performance bounds of the run-to-run control loops. The validity of the methodology is justified by examples of performance assessment for EWMA control, double EWMA control and RLS-LT control, even under circumstances where the processes encounter model mismatch, metrology delay and more sophisticated noises. Several essential characteristics of run-to-run control are discovered by performance assessment, and valuable advice is offered to process engineers for improving the run-to-run control performance. Furthermore, a useful application example for online performance monitoring and optimal tuning of run-to-run controller demonstrates the advantage of the methodology.     

Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model

This article seeks to offer a systematic approach to establishing a reliable network of facilities in closed loop supply chains (CLSCs) under uncertainties. Facilities that are located in this article concurrently satisfy both traditional objective functions and reliability considerations in CLSC network designs. To attack this problem, a novel mathematical model is developed that integrates the network design decisions in both forward and reverse supply chain networks. The model also utilizes an effective reliability approach to find a robust network design. In order to make the results of this article more realistic, a CLSC for a case study in the iron and steel industry has been explored. The considered CLSC is multi-echelon, multi-facility, multi-product and multi-supplier. Furthermore, multiple facilities exist in the reverse logistics network leading to high complexities. Since the collection centres play an important role in this network, the reliability concept of these facilities is taken into consideration. To solve the proposed model, a novel interactive hybrid solution methodology is developed by combining a number of efficient solution approaches from the recent literature. The proposed solution methodology is a bi-objective interval fuzzy possibilistic chance-constraint mixed integer linear programming (BOIFPCCMILP). Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model in a supply chain environment and to help decision makers facilitate their analyses.     

A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation

The paper presents an innovative approach to integrate Human and Organisational Factors (HOF) into risk analysis. The approach has been developed and applied to a case study in the maritime industry, but it can also be utilised in other sectors. A Bayesian Belief Network (BBN) has been developed to model the Maritime Transport System (MTS), by taking into account its different actors (i.e., ship-owner, shipyard, port and regulator) and their mutual influences. The latter have been modelled by means of a set of dependent variables whose combinations express the relevant functions performed by each actor. The BBN model of the MTS has been used in a case study for the quantification of HOF in the risk analysis carried out at the preliminary design stage of High Speed Craft (HSC). The study has focused on a collision in open sea hazard carried out by means of an original method of integration of a Fault Tree Analysis (FTA) of technical elements with a BBN model of the influences of organisational functions and regulations, as suggested by the International Maritime Organisation's (IMO) Guidelines for Formal Safety Assessment (FSA). The approach has allowed the identification of probabilistic correlations between the basic events of a collision accident and the BBN model of the operational and organisational conditions. The linkage can be exploited in different ways, especially to support identification and evaluation of risk control options also at the organisational level. Conditional probabilities for the BBN have been estimated by means of experts’ judgments, collected from an international panel of different European countries. Finally, a sensitivity analysis has been carried out over the model to identify configurations of the MTS leading to a significant reduction of accident probability during the operation of the HSC.     

Calibration of Expert Judgments Applied to Uncertainty Assessment

Abstract:

This article contributes a methodology for eliciting expert judgment in support of decision analysis associated with the conceptual design of advanced engineering systems. To provide a basis for decision making in the presence of model and input parameter uncertainties, experts in several disciplines can be utilized to provide model parameter estimates to facilitate analyses. The judgment elicitation methodology was developed to cover a multitude of system disciplines using multiple experts. To address consistency in expert assessments, the methodology includes expert assessment calibration means. A sample application of the resultant expert judgment methodology is discussed. An engineering manager can use the methodology described in this article to assess viability of potential courses of action in high-risk or advanced state-of-the-art technology systems development ventures.     

An elemental NTU- model for vapour-compression liquid chillers

This paper presents a steady-state model for predicting the performance of vapour-compression liquid chillers over a wide range of operating conditions. The model overcomes the idealisations of previous models with regard to modelling the heat exchangers. In particular, it employs an elemental NTU- methodology to model both the shell-and-tube condenser and evaporator. The approach allows the change in heat transfer coefficients throughout the heat exchangers to be accounted for, thereby improving both physical realism and the accuracy of the simulation model. The model requires only those inputs that are readily available to the user (e.g. condenser inlet water temperature and evaporator water outlet temperature). The outputs of the model include system performance variables such as the compressor electrical work input and the coefficient of performance (COP) as well as states of the refrigerant throughout the refrigeration cycle. The methodology employed within the model also allows the performance of chillers using refrigerant mixtures to be modelled. The model is validated with data from one single screw chiller and one twin-screw chiller where the agreement is found to be within ±10%.     

CNC并行多任务调度算法及其实现

在分析ＣＮＣ系统多任务特点的基础上，介绍了ＣＮＣ系统的多任务分类方法，并提出了基于通用３２位操作系统，运行于保护模式下的多任务调度方法。     

Using an integrated process of data and modeling in HRA

The paper describes an approach taken to estimate the probabilities of failure associated with various railroad tasks to prevent accidents (principally collisions and derailments). These probabilities were estimated using an expert elicitation process that used partially relevant data available from a variety of databases and that were filtered and scaled to make them more directly relevant to the analyses being performed. Extensive qualitative studies were performed prior to the elicitation process to identify relevant contexts under which the tasks can be performed.     

基于模糊层次分析法的包装方案评估模型研究

针对包装多元化的现象,依据层次分析法建立了包装方案评估模型,并给出了分析实例。该模型不仅可以对包装方案进行综合评判,还可以比较方案在次级评价指标方面的优劣以作为优化设计的理论依据。使用该模型对包装方案进行评估,可以使评估过程定量化更容易操作,评估结果也更加直观。该模型在包装方案评估中有广泛的适用性。     

Intelligent decision support system for the adaptive control of a flexible manufacturing system with machine and tool flexibility

This paper describes an intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS). The controller is capable of classifying symptoms in developing the control policies on FMSs with flexibility in operation assignment and scheduling of multi-purpose machining centres which have different tools with their own efficiency. The proposed system is implemented by coupling of rule-based IDSS, simulation block and centralised simulation optimiser for elicitation of shop floor control knowledge. This posteriori adaptive controller uses a new bilateral mechanism in simulation optimiser block for offline training of IDSS based on multi-performance criteria simulation optimisation. The proposed intelligent controller receives online information of the FMS current state and trigger appropriate control rule within real-time simulation data exchange. Finally the FMS intelligent controller is validated by a benchmark test problem. Application of this adaptive controller showed that it could be an effective approach for real time control of various flexible manufacturing systems.