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Highlights • The paper presents a new type of conceptual framing for System 1–System 2 thinking in risk science. • It outlines a method for how to better incorporate System 1 type of thinking in risk assessments and management. • Perspectives on risk highlighting uncertainties instead of probabilities are used as a basis for the discussion. Abstract In the field of risk perception and behavioural decision-making, a dichotomy is commonly made by two modes of thought: System 1 , which operates automatically and quickly, instinctively and emotionally, and System 2 , which is slower, more logical, and deliberative. A considerable body of literature exists linking this dichotomy to risk analysis. It is argued that, to properly respond to and handle risk, both types of thinking are needed; they are complementary. However, current risk assessment practice is to a large extent founded on System 2 thinking. System 1 thinking is associated with affects and risk perception, separated from the professional/scientific risk assessments. The present paper argues that this practice is rooted in a traditional risk assessment perspective, highlighting probabilistic and statistical modelling and analysis. Using broader and recent perspectives on risk, highlighting uncertainties instead of probabilities, there is a potential to improve this practice and to also obtain a stronger use of System 1 thinking in risk assessments. The aim of the paper is to provide substance to these theses, by formalising the issues raised and outlining suitable approaches and methods for how to obtain the desired integration of both System 1 and System 2 thinking in professional risk assessment and management. [ABSTRACT FROM AUTHOR]

This paper discusses the basic principles that a government should adopt when it comes to risk. There seems to be broad agreement about general principles, such as openness and transparency, involvement, proportionality and consistency, and making decisions based on evidence, but when it comes to a more detailed level, suitable principles are missing or are inconsistent. For example, what does it mean to base decisions on evidence or to act with proportionality when regulating or managing risk? The present paper aims at stimulating a discussion on this topic by formulating eight specific principles that governments should apply for the effective treatment of risk in society. The authors consider these eight principles to reflect current scientific knowledge produced by the risk analysis field, but like all principles of this type they are grounded in normative requirements of “good” governance. Several examples are used to illustrate the discussion. [ABSTRACT FROM AUTHOR]

This paper addresses ‘sociotechnical perspectives on safety’, highlighting common ideas and principles for understanding, studying and managing the safety of sociotechnical systems, such as high-risk industries. These perspectives can be characterised in different ways, but, for the purpose of the present paper, three features are focused on: i) that a holistic view is needed to manage safety, covering knowledge from different disciplines (technology, social sciences, etc.), ii) that complex systems cannot be fully predicted and controlled, and iii) that safety management consequently needs to highlight robustness and resilience in addition to risk analysis. Some works have been conducted to understand these perspectives in relation to risk, risk analysis and risk management, but most of these have been based on traditional concepts and approaches to risk, using quantitative probabilistic risk assessments. In this paper we revisit the issue, using more recent ideas and approaches for understanding, assessing and managing risk, where uncertainty is a main component of risk. We show that, when framed according to these ideas and approaches, the risk field can provide a supporting platform for the sociotechnical perspectives and supplement the types of means to properly manage safety. Some implications for safety and risk regulation are also discussed. [ABSTRACT FROM AUTHOR]

• A hybrid replacement policy for two-stage degradation system is devised. • The cost-efficiency is improved via the joint of age/state-centered replacement. • Inspection errors with variable probabilities are integrated in decision-making. • The applicability of the proposed policy is validated via a case study. Inspections are crucial to reveal asset health and support preventive maintenance, which, however, are susceptible to multiple uncertainties due to human errors and limitations of diagnosing technologies. This often leads to inspection errors with variable probabilities affected by operational time/state, particularly for multi-stage degradation. This paper investigates a hybrid inspection-replacement policy for two-stage continuously degrading assets subject to time-state-variant inspection errors, either false positive or false negative. The error probabilities upon each inspection are related to (a) the accumulated operational age, and (b) whether encounters defects. To mitigate loss due to errors, a hybrid replacement planning integrating three types of replacements is scheduled. In particular, age-centered replacement is implemented to relieve uncertainty interference of inspection errors. Additionally, threshold-based replacement (when degradation attains a threshold) and defect-induced replacement (when the asset is reported as defective) are executed to ensure timely response to state variations. The long run cost rate is minimized through the joint optimization of the inspection interval, degradation threshold and age limit. The applicability of the proposed model is demonstrated through numerical experiments conducted on operations & maintenance management of high-speed train bearing, which is proved to be cost-effective than several comparative models. [ABSTRACT FROM AUTHOR]

There is an increasing need for organizations to evaluate aspects that are not easily quantified, such as alignment with organizational values, within their strategic planning decisions. These aspects are often insufficiently understood and are rapidly changing, with potential to cause severe negative consequences. Because there is no accepted methodology for characterizing these aspects, this type of risk is often neglected or given inadequate attention. This paper develops a methodology to evaluate risk and uncertainty related to alignment with organizational values. The methodology builds on risk perspectives involving uncertainties and knowledge rather than probability estimates. We illustrate the methodology on an application within the energy sector. This paper is relevant for both public and private sector organizations who face the dilemma of “what you cannot measure you cannot manage”, implying a struggle to include low-data and low-knowledge aspects into risk-based decisions. [ABSTRACT FROM AUTHOR]

This paper models and optimizes a 1-out-of- N: G heterogeneous warm standby system subject to state-dependent component loading and mission abort policies. For circumstances where a system's survival may have a higher priority than continuing a specified mission due to cost or safety reasons, when a certain incident or malfunction condition is met based on a predefined decision rule, the mission objective is aborted and a rescue procedure is performed for system survival. The mission abort policy implemented can affect the system performance significantly. In addition, load levels of system components affect their performances (e.g., productivity) and time-to-failure distributions, and further affect the overall system performance. This paper makes new contributions by formulating and solving a co-optimization problem that finds the optimal combination of mission abort and component loading policies, maximizing the mission success probability while providing a desired level of system survivability. As demonstrated through examples, the co-optimization results can facilitate optimal decisions on effective and safe operations management of warm standby systems. [ABSTRACT FROM AUTHOR]

The advent of ever more complex systems in extensive areas of industries hampers efficient and accurate analysis of reliability and effective reliability-based decision making. Such difficulties may arise from the intricate formulation of system failure events, statistical dependence between component failure events, and the convoluted quantification of underlying probabilities of basic events. So-called k-out-of-N systems, which survive or succeed when at least k components are available among the total of N components, give rise to a high level of complexity. This type of systems are commonly introduced to secure a proper level of redundancy in operating engineering systems, but the intricate definition of the system events may elude the system reliability analysis. It is noted that such k-out-of-N systems are often tested and corrected over a certain period of time before their official usage or release in order to assure the target reliability of the system. For the purpose of reliability prognosis based on the data collected from the test period, reliability growth models (RGMs) have been widely used in software and hardware engineering. However, RGMs have been applied mostly to individual components, not at the system level. Furthermore, in complex systems such as k-out-of-N system, it is challenging to relate the reliability growth of components with that of the system. To address this need, in this paper, the matrix-based system reliability (MSR) method is extended to k-out-of-N systems by modifying the formulations of event and probability vectors. The proposed methods can incorporate statistical dependence between component failures for both homogeneous and non-homogeneous k-out-of-N systems, and can compute measures related to parameter sensitivity and relative importance of components. The reliability growths of components represented by RGMs are incorporated into the proposed system reliability method, so that the trend of system reliability growth can be effortlessly evaluated and predicted. Two numerical examples are introduced in this paper to demonstrate the proposed method and its applications: (1) hypothetical systems each consisting of series, parallel and k-out-of-N subsystems, and (2) a simplified high speed train system modeled by multiple k-out-of-N subsystems. Two types of RGMs, i.e. non-homogeneous Poisson process (NHPP) and Duane models are employed in these examples. [ABSTRACT FROM AUTHOR]

This paper develops a new model for redundancy allocation problem. In this paper, like many recent papers, the choice of the redundancy strategy is considered as a decision variable. But, in our model each subsystem can exploit both active and cold-standby strategies simultaneously. Moreover, the model allows for component mixing such that components of different types may be used in each subsystem. The problem, therefore, boils down to determining the types of components, redundancy levels, and number of active and cold-standby units of each type for each subsystem to maximize system reliability by considering such constraints as available budget, weight, and space. Since RAP belongs to the NP-hard class of optimization problems, a genetic algorithm (GA) is developed for solving the problem. Finally, the performance of the proposed algorithm is evaluated by applying it to a well-known test problem from the literature with relatively satisfactory results. [ABSTRACT FROM AUTHOR]

• The main contributions of the paper are as follows: to frame a comprehensive multi-level hierarchy model using linguistic scale based multi criteria decision-making (MCDM) approach for situational awareness (SA) oriented resilience level assessment of active distribution network (ADN) against different types of high impact low frequency (HILF) events. • To determine the key contributing factors of source uncertainty, with a focus on analysis of uncertain ADN availability leading to non-deterministic resilience (NDR), using a hybrid MCDM-dynamic Bayesian network (DBN) approach. • To illustrate the significance of PDF in NDR and quantify operational performance of ADN to withstand critical loads due to NDR in terms of load not served (LNS) index. • Analysis of the proposed technique on IEEE 33-bus network to demonstrate the efficacy of the approach. The planning and operation of distribution systems have significantly changed due to the extensive osmosis of ICT (information and communication technologies) into power systems. Load growth, together with increased power quality expectations by the customers, has transformed the conventional distribution network to active distribution network (ADN), with penetration of distributed energy resources (DERs). This has imposed the necessity to analyze resilient performance of ADN subjected to the boon of ICT for enhanced situational awareness (SA). This paper focuses on a detailed multi-level hierarchy framework using linguistic scale based multi-criteria decision-making (MCDM) approach for SA oriented resilience level estimation of ADN, considering different high impact low frequency (HILF) events. The rapidly changing environmental conditions and ambiguity in interaction of network parameters lead to non-deterministic resilience (NDR), thus, affecting the operational resilience of ADN on a daily basis. To analyze the uncertain ADN availability due to NDR, hybrid MCDM-dynamic Bayesian network (DBN) approach is used. For better decision-making, the operational performance of ADN, to withstand critical loads due to NDR is analyzed in terms of load not served (LNS) index. The proposed framework is tested on IEEE 33-bus distribution network. [ABSTRACT FROM AUTHOR]

• Novel procedure for the objective and fair evaluation of failure assessment models. • Procedure based on established mathematical methods. • Procedure based on well-defined evaluation index system. • Model performance demonstrated for 250 burst experiments on external axial cracked pipes. • Evaluation of 9 well-known failure assessment models. The quantification for overall performance of failure assessment models (FAM) is a challenging task. In the first part of this paper, we propose a novel procedure to evaluate FAM's overall performance based on reliability engineering and system safety. This procedure is composed of three parts: 1. proposing the evaluation index system for FAM's performance based on the uncertainty of Prediction Accuracy (PA); 2. calculating the FAM's overall performance scores, which contains three steps: determining the weights of the criteria in the evaluation index system; calculating the decision matrix for different FAMs; and computing FAM's overall performance; 3. analyzing the evaluation results to give recommendations. In the second part of this paper, we demonstrate the application of the novel procedure to evaluate the performance of 9 FAMs for the integrity assessment of pipes containing axial-oriented part-through cracks on the outer surface. This evaluation is based on 250 well-documented pipe burst experiments. The results show that: R6-Option 2A-local solution (2016) gives the best overall prediction. In addition, different Multi-Criteria Decision Making (MCDM) methods have different focuses. VIKOR and TOPSIS are more considering the impact of high weight criteria, especially VIKOR. SWM and PROMETHEE II care more about FAM's overall performance. [ABSTRACT FROM AUTHOR]

Storage reliability that measures the ability of products in a dormant state to keep their required functions is studied in this paper. For certain types of products, Storage reliability may not always be 100% at the beginning of storage, unlike the operational reliability, which exist possible initial failures that are normally neglected in the models of storage reliability. In this paper, a new integrated technique, the non-parametric measure based on the E-Bayesian estimates of current failure probabilities is combined with the parametric measure based on the exponential reliability function, is proposed to estimate and predict the storage reliability of products with possible initial failures, where the non-parametric method is used to estimate the number of failed products and the reliability at each testing time, and the parameter method is used to estimate the initial reliability and the failure rate of storage product. The proposed method has taken into consideration that, the reliability test data of storage products containing the unexamined before and during the storage process, is available for providing more accurate estimates of both the initial failure probability and the storage failure probability. When storage reliability prediction that is the main concern in this field should be made, the non-parametric estimates of failure numbers can be used into the parametric models for the failure process in storage. In the case of exponential models, the assessment and prediction method for storage reliability is presented in this paper. Finally, a numerical example is given to illustrate the method. Furthermore, a detailed comparison between the proposed and traditional method, for examining the rationality of assessment and prediction on the storage reliability, is investigated. The results should be useful for planning a storage environment, decision-making concerning the maximum length of storage, and identifying the production quality. [ABSTRACT FROM AUTHOR]

This paper investigates how the value of information (VoI) metric can guide information collection and optimal sensor placement in spatially distributed systems. VoI incorporates relevant features to decision-making, such as uncertainty about the state of the system, precision of measurements, the availability of intervention actions, and the overall cost of managing the system. Spatially distributed systems also allow for information propagation, i.e. measurements collected at one location can be used to update knowledge at other related locations. In this paper, while restricting our attention to Gaussian random field and binary state models, we illustrate first how sensor placements depend on the decision-making problem to be addressed, as encoded in a problem-specific loss function, and second how the complexity of VoI computations is impacted by this loss function's characteristics. In doing so, we consider several loss functions and present computational techniques for evaluating VoI under them. Finally, we apply these techniques to efficiently optimize sensor placements by the VoI metric in two example applications. [ABSTRACT FROM AUTHOR]

It is crucial to evaluate the risk associated with marine vessels subjected to inclement weather and sea conditions when developing a decision support system for ship routing. The generalized decision making framework developed in this paper performs a variety of tasks, including, but not limited to quantifying the flexural and fatigue performance of ship structures and employing multi-attribute utility theory to evaluate ship mission performance. A structural reliability approach is utilized to compute the probability of failure considering the uncertainties in structural capacity and load effects; specifically, effects of flexural and fatigue damage are investigated. The expected repair cost, cumulative fatigue damage, total travel time, and carbon dioxide emissions associated with ship routing are considered as consequences within the risk assessment procedure adopted in this paper. Additionally, the decision maker’s risk attitude is integrated into the presented approach by employing utility theory. The presented methodology can assist decision makers in making informed decisions concerning ship routing. In order to illustrate its capabilities the approach is applied to the Joint High-speed Sealift Ship. [ABSTRACT FROM AUTHOR]

• A joint optimization model of rearrangement policy and preventive maintenance policy is formulated for balanced systems. • Three competing failures are considered for heterogeneous balanced systems. • A data transmission method is used to convert the semi-Markov decision model to the markov decision model. • A value iteration algorithm is established to obtain the optimal maintenance action at each state. This paper studies a heterogeneous balanced system composed of multiple interchangeable components. The degradation process of components is described by a gamma process, and deterioration rates in different positions are different due to the effect of loading stress or temperature. Three competing failures may occur: a) shock failure caused by environment shocks, b) soft failure when the deterioration level of any component exceeds a critical value, and c) out of balance when the difference value among components reaches the failure threshold. To avoid system failure, a rearrangement action is adopted to change the position of components. Besides, preventive maintenance is considered when a component deteriorates severely. A semi-Markov decision process (SMDP) is developed to obtain the optimal policy by minimizing the average maintenance cost. To facilitate calculation, the data transmission method is used to convert the semi-Markov decision model to the Markov decision model, and a value iteration algorithm is established to obtain the optimal maintenance action at each state. Considering practical implications, an imperfect preventive and opportunistic maintenance model is formulated under the SMDP framework. Finally, a typical tire rotation problem in motorcycles proves that the imperfect maintenance policy outperforms the other policies when replacement fees are more expensive. [ABSTRACT FROM AUTHOR]

Efficient critical load restoration under extreme natural disasters is a promising solution to establish resilient distribution systems. Deep reinforcement learning (DRL) approaches are widely adopted in the load restoration problem to avoid incorporating the accurate distribution system model and improve online decision efficiency. However, the vulnerability of DRL towards adversarial examples may lead to unpracticable decisions and pose potential threats to load restoration. To address this issue, this paper proposes a robustness assessment and enhancement method for DRL-enabled distribution system load restoration. In particular, the distribution system load restoration problem is formulated as a Markov decision process, and a deep Q-network is adopted to learn the optimal decision policy. Then, an adversarial example generation optimization model incorporating the deep Q-network is established to implement the robustness assessment of the DRL-enabled load restoration against adversarial examples. Furthermore, adversarial training with the experience replay of adversarial examples is adopted to retrain the agent and improve the stability of the load restoration decision-making. Finally, the effectiveness of the proposed method is analyzed and verified in the modified IEEE 33-bus system and IEEE 123-bus system. The results show that robustness evaluation and enhancement significantly reduce the application risk of DRL in load restoration with safety-critical requirements. • Evaluate the robustness of deep reinforcement learning-enabled load restoration for distribution networks. • Establish an adversarial example generation model for the load restoration agent. • Mitigate deleterious impacts of state perturbations on critical load supply using adversarial training. [ABSTRACT FROM AUTHOR]

• A MILP model for parallel machine scheduling and maintenance is proposed. • Job-machine release and maintenance duration/interval flexibility are considered. • A RUL prediction method is proposed to get online machine reliability. • Online machine reliability is used to guide all maintenance activities. The machine remaining useful life (RUL), the job-machine release time and the correlation between the maintenance duration and the machine enlistment age are, in this paper, collectively emphasized at the parallel machine scheduling problem. Based on this, a corresponding mixed integer programming model is constructed to minimize the makespan and the processing loss beyond the machine RUL threshold, where a discrete teaching and learning based optimization algorithm is applied to solve this NP-hard problem, and a fault mode-assisted gated recurrent unit (FGRU) life prediction method is used to guide the predictive maintenance initiation time of all machines. In addition, this paper demonstrates that the FGRU method is more accurate than three common methods (Encoder-Decoder Recurrent Neural Network, Bidirectional Long Short-Term Memory and GRU) through two actual bearing degradation cases, and shows through three benchmark cases that the joint decision-making can effectively reduce the time cost of manufacturing enterprises. [ABSTRACT FROM AUTHOR]

Binary Decision Diagram (BDD) based fault tree analysis algorithms are among the most efficient ones. They allow performing exact probabilistic analyses, as well as to derive a Zero-suppressed BDD (ZBDD) to efficiently encode Significant Prime Implicants (PI) or Minimal Cut Sets (MCS). The present paper describes a dynamic labelling method for BDD/ZBDD to analyse non-coherent fault trees. An L-BDD is a BDD in which the information about the variable type is associated to each node. This information is useful to select, for each node, the corresponding algorithms for performing the probabilistic analysis and for determining PI or MCS. When the computational resources are not sufficient to complete the BDD construction, it is convenient to construct the ZBDD directly from the fault tree. The second part of this paper describes rules for constructing a Truncated Labelled ZBDD (TL-ZBDD) of non-coherent fault trees. Results of the analysis of some non-coherent fault trees by means of L-BDD and TL-ZBDD are provided. [ABSTRACT FROM AUTHOR]

This paper considers a multi-period production system where a set of machines are arranged in parallel. The machines are unreliable and the failure rate of machine depends on the load assigned to the machine. The expected production rate of the system is considered to be a non-monotonic function of its load. Because of the machine failure rate, the total production output depends on the combination of loads assigned to different machines. We consider the integration of load distribution decisions with production planning decision. The product demands are considered to be known in advance. The objective is to minimize the sum of holding costs, backorder costs, production costs, setup costs, capacity change costs and unused capacity costs while satisfying the demand over specified time horizon. The constraint is not to exceed available repair resources required to repair the machine breakdown. The paper develops two heuristics to solve the integrated load distribution and production planning problem. The first heuristic consists of a three-phase approach, while the second one is based on tabu search metaheuristic. The efficiency of the proposed heuristics is tested through the randomly generated problem instances. [ABSTRACT FROM AUTHOR]

The use of Bayesian Belief Networks (BBNs) in risk analysis (and in particular Human Reliability Analysis, HRA) is fostered by a number of features, attractive in fields with shortage of data and consequent reliance on subjective judgments: the intuitive graphical representation, the possibility of combining diverse sources of information, the use the probabilistic framework to characterize uncertainties. In HRA, BBN applications are steadily increasing, each emphasizing a different BBN feature or a different HRA aspect to improve. This paper aims at a critical review of these features as well as at suggesting research needs. Five groups of BBN applications are analysed: modelling of organizational factors, analysis of the relationships among failure influencing factors, BBN-based extensions of existing HRA methods, dependency assessment among human failure events, assessment of situation awareness. Further, the paper analyses the process for building BBNs and in particular how expert judgment is used in the assessment of the BBN conditional probability distributions. The gaps identified in the review suggest the need for establishing more systematic frameworks to integrate the different sources of information relevant for HRA (cognitive models, empirical data, and expert judgment) and to investigate algorithms to avoid elicitation of many relationships via expert judgment. [ABSTRACT FROM AUTHOR]

This paper undertakes a general reliability study using both classical and Bayesian semi-parametric degradation approaches. The goal is to illustrate how degradation data can be modelled and analysed to flexibly determine reliability to support preventive maintenance strategy making, based on a general data-driven framework. With the proposed classical approach, both accelerated life tests (ALT) and design of experiments (DOE) technology are used to determine how each critical factor affects the prediction of performance. With the Bayesian semi-parametric approach, a piecewise constant hazard regression model is used to establish the lifetime using degradation data. Gamma frailties are included to explore the influence of unobserved covariates within the same group. Ideally, results from the classical and Bayesian approaches will complement each other. To demonstrate these approaches, this paper considers a case study of locomotive wheel-set reliability. The degradation data are prepared by considering an Exponential and a Power degradation path separately. The results show that both classical and Bayesian semi-parametric approaches are useful tools to analyse degradation data and can, therefore, support a company in decision making for preventive maintenance. The approach can be applied to other technical problems (e.g. other industries, other components). [ABSTRACT FROM AUTHOR]

The benefits of utilizing the probabilistic safety assessment towards improvement of nuclear power plant safety are presented in this paper. Namely, a nuclear power plant risk reduction can be achieved by risk-informed optimization of the deterministically-determined surveillance requirements. A living probabilistic safety assessment tool for time-dependent risk analysis on component, system and plant level is developed. The study herein focuses on the application of this living probabilistic safety assessment tool as a computer platform for multi-objective multi-dimensional optimization of the surveillance requirements of selected safety equipment seen from the aspect of the risk-informed reasoning. The living probabilistic safety assessment tool is based on a newly developed model for calculating time-dependent unavailability of ageing safety equipment within nuclear power plants. By coupling the time-dependent unavailability model with a commercial software used for probabilistic safety assessment modelling on plant level, the frames of the new platform i.e. the living probabilistic safety assessment tool are established. In such way, the time-dependent core damage frequency is obtained and is further on utilized as first objective function within a multi-objective multi-dimensional optimization case study presented within this paper. The test and maintenance costs are designated as the second and the incurred dose due to performing the test and maintenance activities as the third objective function. The obtained results underline, in general, the usefulness and importance of a living probabilistic safety assessment, seen as a dynamic probabilistic safety assessment tool opposing the conventional, time-averaged unavailability-based, probabilistic safety assessment. The results of the optimization, in particular, indicate that test intervals derived as optimal differ from the deterministically-determined ones defined within the existing technical specifications. Substantial risk reduction, supplemented with reduction in costs and dose is implicated if the selected existing surveillance test intervals are replaced with the corresponding optimal ones. By this, the benefits of applying risk-informed decision making are once more emphasized. Also, the importance of the inclusion of safety equipment ageing in the plant long-term probabilistic safety assessment is recognized. [ABSTRACT FROM AUTHOR]

• Real world problems often involve scarce, uncertain, and heterogeneous data. • Proposed methodology uses Bayesian Statistics to produce diagnoses using such data as input. • Aleatory and Epistemic uncertainties are considered and naturally propagated from source to result. Instrumentation and Control systems are often assumed to break and give no readings under certain conditions, but working perfectly otherwise. In reality, aleatory and epistemic factors create a grey area where operators are often unsure of the validity of sensor measurements. Through the use of Bayes' Theorem, this paper proposes a novel approach that first characterizes both aleatory and epistemic uncertainty, and then combines all available information in a Bayesian network, in order to produce quantitative estimates of unobservable variables in the system. Uncertainties are also propagated from sources to results in a natural manner. The approach was applied to a test case, and was able to identify a Vessel Break transient with quantitative probabilities in a timely manner despite the information being scarce, uncertain, and heterogeneous. The approach was thus demonstrated to be a possible alternative method for decision-making under such non-ideal conditions. [ABSTRACT FROM AUTHOR]

• Utilization of the Bayesian probability, utility and decision analysis. • Consistent assessment of information acquirement and physical mitigation strategies. • Identification of optimal, significant and efficient strategies before implementation. This paper describes the assessment of the cost efficiency of risk mitigation strategies for terrorist attacks with Improvised Explosive Devices (IEDs) for an iconic bridge structure. The assessment is performed with a decision theoretical framework building upon very recent advances in the COST Action TU1402 on Quantifying the Value of Structural Heath Monitoring. The decision scenario is formulated for a decision maker constituting an authority responsible for the societal safety of the infrastructure and consequently the direct risks for the infrastructure owner and the indirect risk due to fatalities and importance of the infrastructure are considered. The mitigation strategies are classified within the decision theoretical context as prior analyses for the assessment of protection strategies and as control strategies requiring a pre-posterior decision analysis. The identification of efficient risk mitigation strategies is based (1) on the risk and expected cost based optimization of actions and information and their combination before implementation, (2) on quantifying and ensuring the significance in risk and expected cost reduction and (3) on quantifying and ensuring a high probability of cost efficiency. These criteria, i.e. the optimality, significance and efficiency ensure the performance of the strategies at the decision point in time before implementation. It is found that the strategies are relying on the identification of the threat level and that control strategies are in favor as their significance and probability of efficiency are higher and their costs are adjustable. However, for high threat levels, both the bridge protection strategies and control strategies are cost efficient. [ABSTRACT FROM AUTHOR]

Abstract: Modern societies are becoming increasingly dependent on critical infrastructure systems (CISs) to provide essential services that support economic prosperity, governance, and quality of life. These systems are not alone but interdependent at multiple levels to enhance their overall performance. However, recent worldwide events such as the 9/11 terrorist attack, Gulf Coast hurricanes, the Chile and Japanese earthquakes, and even heat waves have highlighted that interdependencies among CISs increase the potential for cascading failures and amplify the impact of both large and small scale initial failures into events of catastrophic proportions. To better understand CISs to support planning, maintenance and emergency decision making, modeling and simulation of interdependencies across CISs has recently become a key field of study. This paper reviews the studies in the field and broadly groups the existing modeling and simulation approaches into six types: empirical approaches, agent based approaches, system dynamics based approaches, economic theory based approaches, network based approaches, and others. Different studies for each type of the approaches are categorized and reviewed in terms of fundamental principles, such as research focus, modeling rationale, and the analysis method, while different types of approaches are further compared according to several criteria, such as the notion of resilience. Finally, this paper offers future research directions and identifies critical challenges in the field. [Copyright &y& Elsevier]

Abstract: The use of prognostic methods in maintenance in order to predict remaining useful life is receiving more attention over the past years. The use of these techniques in maintenance decision making and optimization in multi-component systems is however a still underexplored area. The objective of this paper is to optimally plan maintenance for a multi-component system based on prognostic/predictive information while considering different component dependencies (i.e. economic, structural and stochastic dependence). Consequently, this paper presents a dynamic predictive maintenance policy for multi-component systems that minimizes the long-term mean maintenance cost per unit time. The proposed maintenance policy is a dynamic method as the maintenance schedule is updated when new information on the degradation and remaining useful life of components becomes available. The performance, regarding the objective of minimal long-term mean cost per unit time, of the developed dynamic predictive maintenance policy is compared to five other conventional maintenance policies, these are: block-based maintenance, age-based maintenance, age-based maintenance with grouping, inspection condition-based maintenance and continuous condition-based maintenance. The ability of the predictive maintenance policy to react to changing component deterioration and dependencies within a multi-component system is quantified and the results show significant cost savings. [Copyright &y& Elsevier]

Abstract: In security applications probabilities are commonly avoided – it is said that they are impossible to determine and that they are of little interest as a tool to support the decision making and the risk management. Often qualitative assessments are performed on the basis of judgments of actors' intentions and capacities, without references to a probability scale. An example of such a structure is the grading done by the Norwegian Police Security Service (PST), which defines for example a moderate threat level as “One or more parties have the intention and capacity to strike at specific interests. There is an unspecified threat”. In this paper we carry out an in-depth analysis of the meaning of the concept probability in a setting with intentional acts, the main aim being to provide new insights on the scope and use of probabilities in such situations. Comparisons are made with qualitative structures as the PST grading. We question if probabilities have in fact a role to play in security management. The paper concludes that the security field cannot and should not do without judgments of uncertainties using some scale of likelihood or confidence, but such judgments need to be supplemented by other approaches which highlight the background knowledge (including assumptions) that these judgments are based on. [Copyright &y& Elsevier]

Abstract: Asset-intensive companies face great pressure to reduce operation costs and increase utilization. This scenario often leads to over-stress on critical equipment and its spare parts associated, affecting availability, reliability, and system performance. As these resources impact considerably on financial and operational structures, the opportunity is given by demand for decision-making methods for the management of spare parts processes. We proposed an ordering decision-aid technique which uses a measurement of spare performance, based on the stress–strength interference theory; which we have called Condition-Based Service Level (CBSL). We focus on Condition Managed Critical Spares (CMS), namely, spares which are expensive, highly reliable, with higher lead times, and are not available in store. As a mitigation measure, CMS are under condition monitoring. The aim of the paper is orienting the decision time for CMS ordering or just continuing the operation. The paper presents a graphic technique which considers a rule for decision based on both condition-based reliability function and a stochastic/fixed lead time. For the stochastic lead time case, results show that technique is effective to determine the time when the system operation is reliable and can withstand the lead time variability, satisfying a desired service level. Additionally, for the constant lead time case, the technique helps to define insurance spares. In conclusion, presented ordering decision rule is useful to asset managers for enhancing the operational continuity affected by spare parts. [Copyright &y& Elsevier]

Abstract: In recent years several authors have argued for the adoption of certain new types of risk perspectives which highlight uncertainties rather than probabilities in the way risk is understood and measured. The theoretical rationale for these new perspectives is well established, but the practical implications have not been so clearly demonstrated. There is a need to show how the new perspectives change the way risk is described and communicated in real-life situations and in its turn the effects on risk management and decision making. The present paper aims at contributing to this end by considering two cases, related to a national risk level, and a specific analysis concerning an LNG plant. The paper concludes that the new risk perspectives influence the current regime in many ways, in particular the manner in which the knowledge dimension is described and dealt with. Two methods for characterising the strength of knowledge are presented, one of them based on a new concept, the “assumption deviation risk”, reflecting risks related to the deviations from the conditions/states defined by the assumption made. [Copyright &y& Elsevier]

• Optimal scheduling of repair actions reduces maintenance costs of two-unit systems. • Identifying appropriate maintenance level is a critical factor in cost reduction. • Replacement policy enhances reliability while it can be highly expensive. • Allowing repair actions can result in the efficient use of component's life cycle. This paper proposes an opportunistic maintenance policy for maintaining mechanical systems consisting of two series units with economic dependency. Condition monitoring and age information is available for unit 1 and unit 2, respectively. A gamma process describes the stochastic deterioration of unit 1, and condition monitoring data is utilized to estimate its hazard rate using the proportional hazards model (PHM). A matrix-based algorithm is employed to derive the conditional reliability of a two-unit series system with a continuous-state deterioration process. The policy proposes preventive actions on scheduled intervals, based on lifetime and PHM parameters, to enhance system reliability and mitigate unexpected downtimes. Corrective actions rectify the failed unit, while the other unit can be maintained opportunistically. The problem is formulated as a semi-Markov decision process (SMDP) with an objective of minimizing the long-run expected average cost over an infinite time horizon. This paper relaxes a common assumption that repair brings the units to the as-new condition, and provides solutions for the maintenance scheduling of two-unit series systems, where repair is also allowed. [ABSTRACT FROM AUTHOR]

Abstract: There are numerous definitions of the risk concept. When studying the suitability of these definitions one key issue is the degree that the risk concept is able to reflect the data (D), information (I), knowledge (K) and wisdom (W) available, i.e. the various elements of the well-known DIKW hierarchy. In this paper we present and discuss a structure (conceptual framework) for linking some common risk perspectives and the DIKW elements. The structure is based on the following main ideas: Data=the input to the risk assessment, information=the risk description, knowledge (for the decision maker)=understanding the risk description, knowledge (for analysts)=understanding how to do the risk assessment and understanding the risk description, wisdom (for the decision maker)=the ability to use the results of the analysis in the right way and wisdom (for analysts)=the ability to present the results of the analysis in the right way. The principal aim of this paper is to contribute to a better understanding of the link between the risk concept and the DIKW elements, in order to strengthen the foundations of the meaning and characterisation of risk, and in this way provide a basis for improved risk management. [Copyright &y& Elsevier]

Abstract: This paper proposes a variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve a novel mathematical model for multi-objective redundancy allocation problems (MORAP). Most researchers about redundancy allocation problem (RAP) have focused on single objective optimization, while there has been some limited research which addresses multi-objective optimization. Also all mathematical multi-objective models of general RAP assume that the type of redundancy strategy for each subsystem is predetermined and known a priori. In general, active redundancy has traditionally received greater attention; however, in practice both active and cold-standby redundancies may be used within a particular system design. The choice of redundancy strategy then becomes an additional decision variable. Thus, the proposed model and solution method are to select the best redundancy strategy, type of components, and levels of redundancy for each subsystem that maximizes the system reliability and minimize total system cost under system-level constraints. This problem belongs to the NP-hard class. This paper presents a second-generation Multiple-Objective Evolutionary Algorithm (MOEA), named NSGA-II to find the best solution for the given problem. The proposed algorithm demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker (DM) with a complete picture of the optimal solution space. After finding the Pareto front, a procedure is used to select the best solution from the Pareto front. Finally, the advantages of the presented multi-objective model and of the proposed algorithm are illustrated by solving test problems taken from the literature and the robustness of the proposed NSGA-II is discussed. [Copyright &y& Elsevier]

Abstract: This paper presents an approach based on network theory to deal with risk interactions in large engineering projects. Indeed, such projects are exposed to numerous and interdependent risks of various nature, which makes their management more difficult. In this paper, a topological analysis based on network theory is presented, which aims at identifying key elements in the structure of interrelated risks potentially affecting a large engineering project. This analysis serves as a powerful complement to classical project risk analysis. Its originality lies in the application of some network theory indicators to the project risk management field. The construction of the risk network requires the involvement of the project manager and other team members assigned to the risk management process. Its interpretation improves their understanding of risks and their potential interactions. The outcomes of the analysis provide a support for decision-making regarding project risk management. An example of application to a real large engineering project is presented. The conclusion is that some new insights can be found about risks, about their interactions and about the global potential behavior of the project. [Copyright &y& Elsevier]

Highlights • Mobility patterns considering users' subjectivity to asses traffic resilience. • In a traffic network, recovery process after a shock depends on users' perception. • Disperse traffic reduces users' stress during a disruption in an important road. • Communication strategies aimed at adaptation to changes and willingness to disperse. Abstract When assessing the resilience of road transport networks, users' response should be considered as they represent the main capability of the system to adapt to changes when any disruptive event occurs and to recover afterwards. Given the variability in users' response, it seems deterministic approaches might not be adequate to represent the real system performance, thus, a stochastic perspective is required. This paper presents a new approach to assess the resilience of a traffic network when suffering from a disruptive event, considering the stochastic behaviour of the users, where their decisions will be biased by their perception of the traffic conditions rather than by the actual conditions. This approach provides more realistic patterns than the deterministic approach, mainly in terms of recovery times. The real traffic network Luxembourg-Metz has been used to illustrate the approach. [ABSTRACT FROM AUTHOR]

Highlights • An analytical approach for simplification of complex Markov model proposed. • The result of simplified model compared and validated with complex model. • A methodology suggested for minimizing the deviation of result of simplified Markov model from the original model. • Influence of deviation in result on decision-making discussed. Abstract One of the challenges in the use of Markov model in reliability analysis is the exponential growth of number of states and transition rates when the system gets complex. In reliability analysis of a complex safety-instrumented system a simplified model is always desired for reducing the computational cost. In practice, the complex models can be simplified to a much simpler model, which can give almost similar results with some degree of deviation. Depending on the need for the level of accuracy of results, the deviation can be partly compensated with introducing correction factors. The approximated result from the simplified model may or may not influence decision making depending on the degree of deviation and expected accuracy from the analysis. This paper shows how a complex model is simplified through an analytical approach. Comparison of results from both complex and simplified models is presented to discuss the extent in which, this variation influences decision making. [ABSTRACT FROM AUTHOR]

Manufacturing paradigms have played their important roles in modern industry. In recent 20 years, production systems of advanced manufacturing paradigms (e.g. mass customization, reconfigurable manufacturing, sustainable manufacturing and service-oriented manufacturing) have been developed to exceed the traditional “mass production” paradigm. The reasons that make system health management especially difficult include individual machine deteriorations, different system structures, diverse production characteristics and exponential scheduling complexity. To address these gaps, we provide a review of the prognostics and health management (PHM) field focusing on prognostics approaches for asset health, and maintenance policies for more “informed” decisions. This paper addresses recent advances in PHM for advanced manufacturing paradigms to forecast health trends, avoid production breakdowns, reduce maintenance cost and achieve rapid decision-making. Furthermore, an in-depth look at future research interests is provided. [ABSTRACT FROM AUTHOR]

This paper proposes a new software reliability growth model (SRGM), which can be regarded as an extension of the non-parametric SRGMs using support vector regression to predict probability measures of time to software failure. The first novelty underlying the proposed model is the use of a set of weights instead of precise weights as done in the established non-parametric SRGMs, and to minimize the expected risk in the framework of robust decision making. The second novelty is the use of the intersection of two specific sets of weights, produced by the imprecise ε-contaminated model and by pairwise comparisons, respectively. The sets are chosen in accordance to intuitive conceptions concerning the software reliability behaviour during a debugging process. The proposed model is illustrated using several real data sets and it is compared to the standard non-parametric SRGM. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates replacement scheduling for non-repairable safety-related systems (SRS) with multiple components and states. The aim is to determine the cost-minimizing time for replacing SRS while meeting the required safety. Traditionally, such scheduling decisions are made without considering the interaction between the SRS and the production system under protection, the interaction being essential to formulate the expected cost to be minimized. In this paper, the SRS is represented by a non-homogeneous continuous time Markov model, and its state distribution is evaluated with the aid of the universal generating function. Moreover, a structure function of SRS with recursive property is developed to evaluate the state distribution efficiently. These methods form the basis to derive an explicit expression of the expected system cost per unit time, and to determine the optimal time to replace the SRS. The proposed methodology is demonstrated through an illustrative example. [Copyright &y& Elsevier]

Near real-time monitoring and control of critical infrastructure is essential for the operation and management of cities in a world that is, today, more complex and interconnected than ever. Such an infrastructure can be represented as complex networks an some of their related indices and statistics, many of them based on the shortest paths, play a pivotal role in the decision making for public services such as internet, energy or water. Particularly, the literature has shown that shortest paths are key for resilience and criticality assessment in a water distribution systems (WDS). This paper proposes a procedure to speed-up the computation of shortest paths in a WDS, as it can straightforwardly benefit any critical infrastructure. The proposal is based on a reduced dimension of a complex network representing any critical infrastructure. Despite the consequent decrease in the number of all possible paths in the network, the main advantage and novelty of this proposal is to continue finding the exact solution for the shortest paths. Experimental results show that the procedure brings a computational-time reduction consistently over 50% and up to 90% in some cases. In addition, the paper reveals how the use of shortest paths benefits WDS operation and management, as well as playing a key role in near real-time contamination detection and leakage control. • Novel dimensionality-reduction process to compute shortest paths in networked systems. • Innovative use of shortest paths supporting near real-time decision making of water utilities. • Relationship between shortest paths and contamination spread on water distribution systems. • Shortest-paths based approach for dynamically management of district areas of water supply. [ABSTRACT FROM AUTHOR]

• The paper reviews the dragon-king, black swan, grey swan & perfect storm metaphors. • A structure is presented which highlights differences and similarities. • The structure is centred around two key features: knowledge and predictability. • Some implications for risk management and decision-making are discussed. • Recommendations are provided for how to define and interpret the metaphors. Different metaphors have been introduced to reflect the occurrence of rare and surprising types of events with extreme impacts, including black swans, grey swans and dragon-kings. Despite considerable research on clarifying the meaning of these concepts, their relationship still remains unclear. The present paper aims to meet this challenge, by reviewing current definitions and interpretations found in the literature and referred to in practice, analysing these definitions and interpretations, and providing a structure for improved understanding of the differences and similarities between the various metaphors. The paper also discusses some of the implications the use of these concepts has for risk management and decision-making. [ABSTRACT FROM AUTHOR]

Abstract: Key ideas underlying the application of Quantification of Margins and Uncertainties (QMU) to nuclear weapons stockpile lifecycle decisions are described. While QMU is a broad process and methodology for generating critical technical information to be used in U.S. nuclear weapon stockpile management, this paper emphasizes one component, which is information produced by computational modeling and simulation. In particular, the following topics are discussed: (i) the key principles of developing QMU information in the form of Best Estimate Plus Uncertainty, (ii) the need to separate aleatory and epistemic uncertainty in QMU, and (iii) the properties of risk-informed decision making (RIDM) that are best suited for effective application of QMU. The paper is written at a high level, but provides an extensive bibliography of useful papers for interested readers to deepen their understanding of the presented ideas. [Copyright &y& Elsevier]

Abstract: The objective of a maintenance policy generally is the global maintenance cost minimization that involves not only the direct costs for both the maintenance actions and the spare parts, but also those ones due to the system stop for preventive maintenance and the downtime for failure. For some operating systems, the failure event can be dangerous so that they are asked to operate assuring a very high reliability level between two consecutive fixed stops. The present paper attempts to individuate the set of elements on which performing maintenance actions so that the system can assure the required reliability level until the next fixed stop for maintenance, minimizing both the global maintenance cost and the total maintenance time. In order to solve the previous constrained multi-objective optimization problem, an effective approach is proposed to obtain the best solutions (that is the Pareto optimal frontier) among which the decision maker will choose the more suitable one. As well known, describing the whole Pareto optimal frontier generally is a troublesome task. The paper proposes an algorithm able to rapidly overcome this problem and its effectiveness is shown by an application to a case study regarding a complex series–parallel system. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to propose an adaptive maintenance model for a gradually deteriorating system. The system considered initially deteriorates with a nominal deterioration rate and at an unknown time the system''s deterioration rate changes and the new deterioration rate is a time-dependent function. To deal with the transition of mode of deterioration in the framework of the maintenance decision rule an adequate online change detection algorithm is used. The maintenance decision rule is chosen in order to minimise the total maintenance cost including the cost of unavailability. The main result of this paper is to point out the interest of using a detection algorithm and hence the appreciation of a decision rule which takes into account transitions in the deterioration rate. [Copyright &y& Elsevier]

Abstract: This paper proposes a framework for estimating the qualitative and quantitative consequences of human errors that occur during maintenance tasks involving the balance of plant in nuclear power plants. One of the remedies for unexpected reactor shutdowns may be a methodical tool designed to warn potential hazards arising from given maintenance tasks, taking into account human error modes in a proactive manner, in terms of risk and/or loss of electrical power. The entire framework that we established is composed of four components: (1) the human-error analyzer to connect possible failure modes resulting from human errors with other estimators, (2) the frequency estimator to quantify the occurrence of maintenance-related failure modes, (3) the risk estimator to determine minimal cutsets and to compute the variation of the core damage frequency using the fault tree analysis and turbine cycle simulation, and (4) the derate estimator to determine the electrical power loss under abnormal plant configurations caused by human error. The final result is characterized by a cost metric that can be used for decision-making possibly resulting in revisions of procedures, or task modifications. This paper also discusses case studies to illustrate the feasibility of the proposed framework. [Copyright &y& Elsevier]

Abstract: This paper analyzes the optimal distribution of defense resources between protecting the genuine system elements and deploying imperfect false targets (FTs) in simple series and parallel systems. The FTs are not perfect and the attacker can detect a FT with a non-zero probability. Once the attacker has detected certain number of FTs, it ignores them and chooses such number of undetected targets to attack that maximizes the expected damage to the system. The defender decides how many FTs to deploy in order to minimize the expected damage to the system assuming that the attacker uses the most harmful strategy to attack. The expected damage to a series system is proportional to the probability of system destruction. The expected damage to a parallel system can be defined as proportional to the probability that the demand is not met, or as the amount of the unsupplied demand. The paper demonstrates the methodology of analysis of optimal defense strategy as function of different parameters (number of genuine system elements, contest intensity, total attacker''s resource). It presents the decision curves that can be used for the making a decision about efficiency of deploying FTs depending on their cost and detection probability. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to discuss how risk regulation is related to fundamental concepts about risk. We distinguish between two risk perspectives: a traditional risk perspective where risk is considered an objective property of the activity studied and an alternative perspective where uncertainty is the main component of risk. The discussion is substantiated by drawing comparisons between two cases, namely the risk regulation concerning wireless networks and base stations of Salzburg municipality and that of Stavanger municipality. Salzburg has adopted a more precautionary approach to their regulation of sources emitting electromagnetic radiation than Stavanger. Uncertainty constitutes not only an integral, but also a crucial part of decisions and decision-making processes, and gives grounds for the precautious measures that are taken by regulating authorities. Stavanger can be characterised as more reactive than Salzburg, in that its risk regulation heavily relies on some defined limiting values and is, to a large extent, guided by staying within these. Here, electromagnetic radiation is judged by the regulators as representing an insignificant risk to our health. This has direct implications for the degree of regulatory intervention and measures taken (or not taken). The risk regulations of Salzburg and Stavanger are based on different pools of scientific evidence, the result being two different “worlds” of risk regulation. In the paper we show that the traditional risk perspective and the alternative risk perspective to a large extent support the risk regulation of the Stavanger and Salzburg cases, respectively. [Copyright &y& Elsevier]

Abstract: The prevailing perspectives and definitions of risk, at least in the engineering community, are based on probabilities. In this paper we argue that such perspectives and definitions are too narrow. The probability component of the risk concept should be replaced by uncertainty. By jumping directly into probabilities, important uncertainty aspects could easily be overlooked or truncated. In the paper we point at several extended risk definitions, and a formal structure for the various perspectives and definitions is developed. Fundamental concepts such as second-order probabilities and uncertainties are discussed. Examples are provided showing the importance of the choice of risk perspective in a risk assessment and decision-making context. The examples cover offshore operations, security and market price risks. [Copyright &y& Elsevier]

Abstract: Autonomous systems are becoming more commonly used, especially in hazardous situations. Such systems are expected to make their own decisions about future actions when some capabilities degrade due to failures of their subsystems. Such decisions are made without human input, therefore they need to be well-informed in a short time when the situation is analysed and future consequences of the failure are estimated. The future planning of the mission should take account of the likelihood of mission failure. The reliability analysis for autonomous systems can be performed using the methodologies developed for phased mission analysis, where the causes of failure for each phase in the mission can be expressed by fault trees. Unmanned autonomous vehicles (UAVs) are of a particular interest in the aeronautical industry, where it is a long term ambition to operate them routinely in civil airspace. Safety is the main requirement for the UAV operation and the calculation of failure probability of each phase and the overall mission is the topic of this paper. When components or subsystems fail or environmental conditions throughout the mission change, these changes can affect the future mission. The new proposed methodology takes into account the available diagnostics data and is used to predict future capabilities of the UAV in real time. Since this methodology is based on the efficient BDD method, the quickly provided advice can be used in making decisions. When failures occur appropriate actions are required in order to preserve safety of the autonomous vehicle. The overall decision making strategy for autonomous vehicles is explained in this paper. Some limitations of the methodology are discussed and further improvements are presented based on experimental results. [Copyright &y& Elsevier]

Abstract: The starting point for this paper is a traditional approach to maintenance optimization where an object function is used for optimizing maintenance intervals. The object function reflects maintenance cost, cost of loss of production/services, as well as safety costs, and is based on a classical cost–benefit analysis approach where a value of prevented fatality (VPF) is used to weight the importance of safety. However, the rationale for such an approach could be questioned. What is the meaning of such a VPF figure, and is it sufficient to reflect the importance of safety by calculating the expected fatality loss VPF and potential loss of lives (PLL) as being done in the cost–benefit analyses? Should the VPF be the same number for all type of accidents, or should it be increased in case of multiple fatality accidents to reflect gross accident aversion? In this paper, these issues are discussed. We conclude that we have to see beyond the expected values in situations with high safety impacts. A framework is presented which opens up for a broader decision basis, covering considerations on the potential for gross accidents, the type of uncertainties and lack of knowledge of important risk influencing factors. Decisions with a high safety impact are moved from the maintenance department to the “Safety Board” for a broader discussion. In this way, we avoid that the object function is used in a mechanical way to optimize the maintenance and important safety-related decisions are made implicit and outside the normal arena for safety decisions, e.g. outside the traditional “Safety Board”. A case study from the Norwegian railways is used to illustrate the discussions. [Copyright &y& Elsevier]

Abstract: Distribution network service providers (DNSP) are companies dealing with network infrastructure, such as distribution of gas, water, electricity or telecommunications, and they require the development of special maintenance management (MM) capabilities in order to satisfy the needs of their customers. In this sector, maintenance management information systems are essential to ensure control, gain knowledge and improve decision making. The aim of this paper is the study of specific characteristics of maintenance in these types of companies. We will investigate existing standards and best management practices with the scope of defining a suitable ad-hoc framework for implementation of maintenance management. The conclusion of the work supports the proposition of a framework consisting on a processes framework based on a structure of systems, integrated for continuous improvement of maintenance activities. The paper offers a very practical approach to the problem, as a result of more of 10 years of professional experience within this sector, and specially focused to network maintenance. [Copyright &y& Elsevier]

Increasing research efforts have been devoted to mission-oriented preventive maintenance (PM) policies that are restricted mainly to certain missions. This paper investigates the optimization problem of a repair-replacement policy for systems subject to intermittent missions of random types and random durations, which is more realistic in practice. When a mission is completed, the system is assigned a new mission with the type dependent on the previous type and the duration following a general distribution. The randomness of missions affects the failure rate of the system, the penalty for failed missions, and the schedules of PM actions. Taking advantage of the shutdown opportunity of switching missions, the decision maker determine among three possible actions: do-nothing, preventive repair, and preventive replacement to minimize the long-run average cost rate. The optimization problem is formulated as a semi-Markov decision process and is solved by the policy iteration algorithm. An extended policy involving two monotonic control limits is proposed and optimized by a modified policy iteration algorithm. Two numerical examples illustrate the effectiveness of the proposed approach. • Integrate random mission type and random mission duration into maintenance decision-making. • Formulate the optimization problem in the semi-Markov decision process framework. • Extend the proposed policy by considering monotonic control limits. • Develop modified policy-iteration algorithms for optimization. [ABSTRACT FROM AUTHOR]