Quantitative Risk Assessment Modeling for Nonhomogeneous Urban Road Tunnels

Urban road tunnels provide an increasingly cost‐effective engineering solution, especially in compact cities like Singapore. For some urban road tunnels, tunnel characteristics such as tunnel configurations, geometries, provisions of tunnel electrical and mechanical systems, traffic volumes, etc. may vary from one section to another. These urban road tunnels that have characterized nonuniform parameters are referred to as nonhomogeneous urban road tunnels. In this study, a novel quantitative risk assessment (QRA) model is proposed for nonhomogeneous urban road tunnels because the existing QRA models for road tunnels are inapplicable to assess the risks in these road tunnels. This model uses a tunnel segmentation principle whereby a nonhomogeneous urban road tunnel is divided into various homogenous sections. Individual risk for road tunnel sections as well as the integrated risk indices for the entire road tunnel is defined. The article then proceeds to develop a new QRA model for each of the homogeneous sections. Compared to the existing QRA models for road tunnels, this section‐based model incorporates one additional top event—toxic gases due to traffic congestion—and employs the Poisson regression method to estimate the vehicle accident frequencies of tunnel sections. This article further illustrates an aggregated QRA model for nonhomogeneous urban tunnels by integrating the section‐based QRA models. Finally, a case study in Singapore is carried out.     

Promoting Seismic Retrofit Implementation Through “Nudge”: Using Warranty as a Driver

This article proposes a new type of warranty policy that applies the “nudge” concept developed by Thaler and Sunstein to encourage homeowners in Japan to implement seismic retrofitting. Homeowner adaptation to natural disasters through loss reduction measures is known to be inadequate. To encourage proactive risk management, the “nudge” approach capitalizes on how choice architecture can influence human decision‐making tendencies. For example, people tend to place more value on a warranty for consumer goods than on actuarial value. This article proposes a “warranty for seismic retrofitting” as a “nudge” policy that gives homeowners the incentive to adopt loss reduction measures. Under such a contract, the government guarantees all repair costs in the event of earthquake damage to the house if the homeowner implements seismic retrofitting. To estimate the degree to which a warranty will increase the perceived value of seismic retrofitting, we use field survey data from 1,200 homeowners. Our results show that a warranty increases the perceived value of seismic retrofitting by an average of 33%, and an approximate cost‐benefit analysis indicates that such a warranty can be more economically efficient than an ex ante subsidy. Furthermore, we address the failure of the standard expected utility model to explain homeowners’ decisions based on warranty evaluation, and explore the significant influence of ambiguity aversion on the efficacy of seismic retrofitting and nonanalytical factors such as feelings or trust.     

Two ways to handle dependent uncertainties in multi-criteria decision problems

We consider multi-criteria group decision-making problems, where the decision makers (DMs) want to identify their most preferred alternative(s) based on uncertain or inaccurate criteria measurements. In many real-life problems the uncertainties may be dependent. In this paper, we focus on multicriteria decision-making (MCDM) problems where the criteria and their uncertainties are computed using a stochastic simulation model. The model is based on decision variables and stochastic parameters with given distributions. The simulation model determines for the criteria a joint probability distribution, which quantifies the uncertainties and their dependencies. We present and compare two methods for treating the uncertainty and dependency information within the SMAA-2 multi-criteria decision aid method. The first method applies directly the discrete sample generated by the simulation model. The second method is based on using a multivariate Gaussian distribution. We demonstrate the methods using a decision support model for a retailer operating in the deregulated European electricity market.     

A multicriteria decision analysis model for faculty evaluation

In the context of increasing demands for social and financial accountability of universities, the required implementation of transparent faculty evaluation systems constitutes a challenge and an opportunity for universities strategically aligning the activity of academic staff with the university goals. However, despite growing interest in the performance appraisal of faculty, only a few reported studies propose models that cover the full range of academic activities and the models in use are typically based on ad hoc scoring systems that lack theoretical soundness. This article approaches faculty evaluation from an innovative comprehensive perspective. Based on the concepts and methods of multiple criteria value measurement, it proposes a new faculty evaluation model that addresses the whole range of academic activities and can be applied within and across distinct scientific areas, while respecting their specificities. Constructed through a socio-technical process, the model was designed for and adopted by the Instituto Superior Técnico, the engineering school of the Technical University of Lisbon. The model has a two-level hierarchical additive structure, with top-level evaluation areas specified by second-level evaluation criteria. A bottom non-additive third level accounts for the quantitative and qualitative dimensions of academic activity related to each evaluation criterion. The model allows (a) the comparison of the performance of academic staff with performance targets reflecting the strategic policy concerns of university management; (b) the definition of the multicriteria value profile of each faculty member at the top level of the evaluation areas; (c) the computation of an overall value score for each faculty member, through an optimisation procedure that makes use of a flexible system of weights and (d) the assignment of faculty members to rating categories.     

A Probabilistic Model of the Economic Risk to Britain's Railway Network from Bridge Scour During Floods

Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system‐wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs.     

A Framework for Understanding Uncertainty in Seismic Risk Assessment

A better understanding of the uncertainty that exists in models used for seismic risk assessment is critical to improving risk-based decisions pertaining to earthquake safety. Current models estimating the probability of collapse of a building do not consider comprehensively the nature and impact of uncertainty. This article presents a model framework to enhance seismic risk assessment and thus gives decisionmakers a fuller understanding of the nature and limitations of the estimates. This can help ensure that risks are not over- or underestimated and the value of acquiring accurate data is appreciated fully. The methodology presented provides a novel treatment of uncertainties in input variables, their propagation through the model, and their effect on the results. The study presents ranges of possible annual collapse probabilities for different case studies on buildings in different parts of the world, exposed to different levels of seismicity, and with different vulnerabilities. A global sensitivity analysis was conducted to determine the significance of uncertain variables. Two key outcomes are (1) that the uncertainty in ground-motion conversion equations has the largest effect on the uncertainty in the calculation of annual collapse probability; and (2) the vulnerability of a building appears to have an effect on the range of annual collapse probabilities produced, i.e., the level of uncertainty in the estimate of annual collapse probability, with less vulnerable buildings having a smaller uncertainty.     

Multiple criteria decision making for maritime investment policy in the developing countries

This model for multiple criteria decision making enables one to select an optimal investment policy for developing the marine industry in developing countries. The model is composed of: the mathematical model of national economy (LP model) for optimizing an investment scheme under economic and technological constraints; the model of I-O analysis for estimating the change of industrial structures induced by the investment; and the decision making model to which the results of the preceding models are fed back so as to enable the policy planner to select a feasible policy based on his own multiple criteria such as the value of GDP growth rate, international payments, unemployment rate, etc. Due consideration is paid in this study to the role of the marine industries, e.g. shipping and ports, which can be said to be the pivot of economic development in a developing country in connection with its foreign trade policy. A case study is carried out to verify this hypothesis.     

Assessment of Seismic Loss Dependence Using Copula

The catastrophic nature of seismic risk is attributed to spatiotemporal correlation of seismic losses of buildings and infrastructure. For seismic risk management, such correlated seismic effects must be adequately taken into account, since they affect the probability distribution of aggregate seismic losses of spatially distributed structures significantly, and its upper tail behavior can be of particular importance. To investigate seismic loss dependence for two closely located portfolios of buildings, simulated seismic loss samples, which are obtained from a seismic risk model of spatially distributed buildings by taking spatiotemporally correlated ground motions into account, are employed. The characterization considers a loss frequency model that incorporates one dependent random component acting as a common shock to all buildings, and a copula‐based loss severity model, which facilitates the separate construction of marginal loss distribution functions and nonlinear copula function with upper tail dependence. The proposed method is applied to groups of wood‐frame buildings located in southwestern British Columbia. Analysis results indicate that the dependence structure of aggregate seismic losses can be adequately modeled by the right heavy tail copula or Gumbel copula, and that for the considered example, overall accuracy of the proposed method is satisfactory at probability levels of practical interest (at most 10% estimation error of fractiles of aggregate seismic loss). The developed statistical seismic loss model may be adopted in dynamic financial analysis for achieving faster evaluation with reasonable accuracy.     

DEA决策单元的广义价值效率测度模型

为了克服DEA模型存在权重取值过于灵活的缺陷,在DEA建模原理的基础上,通过引入价值偏好估计对经典DEA模型予以了科学推广,并依据该模型推广建立了能够适用于决策者价值函数为非线性函数情况的DEA权重置信域构造方法与相应的决策单元价值效率分析方法即DEA广义价值效率测度实用模型。该模型的主要优点在于:权重置信域的构造具有科学严密性和价值信息引入的可操作性,从而可以保证分析结论的可靠性。数值验证结果表明,该模型可以得出可靠、可信且具有较强分辨能力的价值效率评价结论。     

Bayesian best-worst method: A probabilistic group decision making model

The best-worst method (BWM) is a multi-criteria decision-making method which finds the optimal weights of a set of criteria based on the preferences of only one decision-maker (DM) (or evaluator). However, it cannot amalgamate the preferences of multiple decision-makers/evaluators in the so-called group decision-making problem. A typical way of aggregating the preferences of multiple DMs is to use the average operator, e.g., arithmetic or geometric mean. However, averages are sensitive to outliers and provide restricted information regarding the overall preferences of all DMs. In this paper, a Bayesian BWM is introduced to find the aggregated final weights of criteria for a group of DMs at once. To this end, the BWM framework is meaningfully viewed from a probabilistic angle, and a Bayesian hierarchical model is tailored to compute the weights in the presence of a group of DMs. We further introduce a new ranking scheme for decision criteria, called credal ranking, where a confidence level is assigned to measure the extent to which a group of DMs prefers one criterion over one another. A weighted directed graph visualizes the credal ranking based on which the interrelation of criteria and confidences are merely understood. The numerical example validates the results obtained by the Bayesian BWM while it yields much more information in comparison to that of the original BWM.     

Influence of Safety Measures on the Risks of Transporting Dangerous Goods Through Road Tunnels

Quantitative risk assessment (QRA) models are used to estimate the risks of transporting dangerous goods and to assess the merits of introducing alternative risk reduction measures for different transportation scenarios and assumptions. A comprehensive QRA model recently was developed in Europe for application to road tunnels. This model can assess the merits of a limited number of "native safety measures." In this article, we introduce a procedure for extending its scope to include the treatment of a number of important "nonnative safety measures" of interest to tunnel operators and decisionmakers. Nonnative safety measures were not included in the original model specification. The suggested procedure makes use of expert judgment and Monte Carlo simulation methods to model uncertainty in the revised risk estimates. The results of a case study application are presented that involve the risks of transporting a given volume of flammable liquid through a 10-km road tunnel.     

Earthquake Risk Perception in Bucharest, Romania

The Municipality of Bucharest is one of the capitals with the highest seismic risk in the world. Bucharest is particularly vulnerable to seismic hazard due to: the high density of inhabitants, especially within the residential districts with blocks of flats; the old public utility fund; the out-of-date infrastructure; the numerous industrial parks that are undergoing a restructuring process, not to mention the inefficient organization of civil protection and poor education of the population regarding seismic risk. This research was designed to examine the attitudes and perceptions of people living with the risk of an earthquake hazard in Bucharest. We were interested in how attitudes and perceptions differ depending on gender, age, education, residential area and socioeconomic status, characteristics of seismic hazard, degree of risk exposure, degree of danger, and casualty awareness. At the same time, we compare the results of this study with those from a previous and similar enquiry in 1997. The statistical processing has indicated a significant difference between the declared perception of seismic risk and the independent variables of gender, age, level of education, level of attachment to the residential area, and degree to which the subjects consider they may be affected and could retrieve their losses. Due to the continuous decrease of their living standard, the most vulnerable is the aged population. The feelings toward the residential area is another factor of statistical significance for the population's seismic danger perception. A strong affective bond offers a feeling of safety and leads to the neglect and even total denial of the hazard. In the case of independent variables regarding the type of dwelling, its age, and property form, deviations of empiric values from the theoretical distribution are not relevant for the correlation searched for, which indicates that this issue goes beyond the above-mentioned criteria.     

Decision Making Under Uncertainty—An Example for Seismic Risk Management

Decision-making techniques are used to select the "best" alternatives under multiple and often conflicting criteria. Multicriteria decision making (MCDM) necessitates to incorporate uncertainties in the decision-making process. The major thrust of this article is to extend the framework proposed by Yager^{( 1 )} for multiple decisionmakers and fuzzy utilities (payoffs). In addition, the concept of expert credibility factor is introduced. The proposed approach is demonstrated for an example of seismic risk management using a heuristic hierarchical structure. A step-by-step formulation of the proposed approach is illustrated using a hypothetical example and a three-story reinforced concrete building.     

一种具有不确定偏好序信息的多指标群决策方法

针对具有不确定偏好序信息的多指标群决策问题,给出了一种决策分析方法。在本文中,首先对具有不确定偏好序信息的多指标群决策问题进行了描述;然后给出了将不确定偏好序转换为投票数的计算公式;进一步地,依据Bernardo方法的基本思想,根据每个专家给出的不确定偏好序信息,计算相应的投票数并构建群体投票矩阵,并基于群体投票矩阵构建0-1整数规划模型,通过求解模型可得到方案排序结果。最后,通过一个算例以及与已有方法的对比分析说明了本文给出的方法的可行性和有效性。     

A multiple criteria decision-making model for justifying the acceptance of rush orders

Rush orders are immediate customer demands that exceed the expectation of a currently effective MPS (master production schedule). Decision-makers are often hesitant in the decision of accepting such orders. This paper presents a multiple criteria decision-making model for justifying the acceptance of rush orders for an assembly-to-order production system. Four criteria or production objectives are simultaneously considered and a multiple objective programming technique, the e-constraints approach, is adopted to solve the decision-making problem. This model could give the cost estimation for producing a rush order under various combinations of production objectives. The computed cost value could serve as a valuable reference for justifying the economics of accepting the rush order, and help to determine its pricing strategy.     

DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making

This paper develops a comprehensive algorithm for multi-expert multi-criteria decision making problems considering quantitative and qualitative criteria in forms of benefit, cost or target types. We focus on using probabilistic linguistic term sets to express the qualitative evaluations due to their excellence in expressing complex individual and collective linguistic assessments. Firstly, we develop a target-based linear normalization technique and a target-based vector normalization technique. A weight adjustment method is proposed to achieve the tradeoff between criteria after normalization. Given that the two target-based normalization techniques have different advantages, we then propose a ranking method, which consists three subordinate models, based on these two target-based normalization approaches and three aggregation techniques. Reliable results of a multi-expert multi-criteria decision making problem are determined by integrating the subordinate utility values and the ranks of alternatives. The proposed method is implemented to solve the green enterprise ranking problems and the excavation scheme selection problem for shallow buried tunnels, respectively. The advantages of the proposed method are emphasized through comparative analyses with other ranking methods.     

A Clustering Algorithm for Computer-Assisted Process Organization

Amathematical programming clustering model that forms groups based on total group membership interactions is extended to include precedence relationships, group size limits, and group time limits. The extended clustering model is most appropriate for structured design of information systems as described by the computer-assisted process organization (CAPO), which requires certain ordering and may have limits on development and production capacity. An efficient algorithm for optimizing the CAPO criteria along with computational results is presented. The results show that the method is viable for the CAPO problems reported in the literature.     

A framework for sustainable forest resource allocation: A Canadian case study

This paper addresses the problem of public-owned forest resource allocation observed in Canada. An integrated framework based on mill abilities to create value (expressed as economic, environmental and social benefits) is proposed. It encompasses three phases: (1) election of sustainable allocation criteria, (2) evaluation of mill performance with regard to the allocation criteria and (3) allocation of wood volumes according to mill performance. The framework is applied to a case study proposed by Ministry of Forests, Fauna and Parks (MFFP) in Québec. It is implemented as follows: first, the international standards, Forest Stewardship Council (FSC) and Global Reporting Initiative (GRI) are used to identify relevant allocation criteria covering the three sustainability dimensions (economy, environment and society). Second, the Group-Analytic Hierarchy Process (Group-AHP) is used to weight the allocation criteria and evaluate mill sustainability performance. Finally, optimization models are formulated to allocate the wood following two strategies: (1) maximizing the total created value by all mills and (2) promoting inter-firm fairness. The numerical results show that integrating sustainability concerns in the evaluation process has a significant impact on the allocation decisions. Moreover, adopting a wood allocation strategy seeking to guarantee fairness between forest companies is a win–win strategy as it also leads to maximizing the created value with a minimum deviation from the optimal targeted value. Lessons learned from this collaboration with MFFP are presented in order to help other interested researchers and public organizations develop their own roadmap to sustainable public resource allocation.     

Manufacturing strategy: A study of machine tool companies in the UK

This paper describes findings from an empirical study carried out in a group of small to medium sized companies within the U.K. machine tool industry. Notions of manufacturing strategy are discussed and a contingency model of change within production systems is proposed. Based on the model, a research programme is described, the object of which was to determine why firms adopt particular production systems, under what conditions these are changed, and the criteria and procedures which are used. A focus for change is taken to be the make-or-buy decision. The results showed that the key criteria used by the firms in their evaluations were: capacity, utilization and process capability (technology). From the study, it is concluded that response to change was largely based upon operational considerations and that the firms lacked a framework for reviewing the strategic implications of their decisions. Hence, the need is argued for a methodology whereby firms can establish consistent functional strategies, with the plans and policies to achieve them.     

Bipolar value-driven joint product costing

There exists a joint-cost allocation problem in the public sector, particularly highlighted by the cost-based national pricing of blood products in England, supposedly derived using volume-driven activity-based costing (ABC): an approach criticised by the implementation criteria propounded for ABC within the academic literature. In contrast, we believe that the contributions of quality-associated operations-based drivers, described within ABC, together with the net realisable value method, usually recommended as the optimal joint-cost allocation method, provide the beginnings of a solution. This paper sets out a methodological development from this platform that links allocation decisions with the issue of value. The bipolar nature of the model is based upon the combination of two types of allocation. One allocates joint costs according to the altered value of source material when competing non-joint alternative products exist. The other allocates joint costs according to product outcome values ascertained separately for the physical specification elements that comprise each of the range of joint products. The overall emphases are on academic context, model generalisation, the facilitation of cost-effective choices between joint products and non-joint alternatives, and possible future trends in the application of the method.