Search

International audience; Stakeholders are increasingly demanding transparency on food value chain sustainability performance. Today there is no standard framework to meet this demand and support defining indicators to be used to conduct an overall sustainable performance assessment. This paper mobilizes existing frameworks and indicators to build new sustainable performance metrics for actors willing to work together for their value chain sustainability. Popular methods or tools for assessing dimensions of agrifood products or activities are selected and analyzed to determine how they could contribute to this metric. The analysis aims to distinguish the sustainable development pillars addressed (economic, environmental and/or social), the frames concerned (life cycle thinking or not; multi-actor or not), and the focus of performance measured (drivers, pressures, states, impacts, responses). This categorization is then used to develop a proposal for specifications adapted to food value chain sustainability performance assessment. The applicability of the framework is demonstrated through a case study in a pork agrifood value chain.

International audience; We consider decision situations in which a set of points of view (voters, criteria) are to sort a set of candidates to ordered classes (GOOD / BAD). Candidates are judged GOOD when approved by a sufficient set of points of view; this corresponds to noncompensatory sorting. To be accountable, such approval sorting should provide guarantees about the decision process and decisions concerning specific candidates. We formalize accountability using a feasibility problem expressed as a boolean satisfi-ability formulation. We illustrate different forms of accountability when a committee decides with approval sorting and study the information that should be disclosed by the committee.

This paper considers a real-life problem that arises in a leading company in China involving the production of extra-high-voltage and high-voltage switch equipments. It concerns combining the one-dimensional cutting stock and lot-sizing problems. In addition to minimizing material waste, the number of cutting patterns required is also minimized. Such problem also occurs in other enterprises in paper, furniture and plastic film industries. We develop a mixed-integer linear programming model and propose a dynamic programming-based heuristic (DPH) to solve it. With a recursive formulation, each pattern is explored step by step with a global view. The computational results show that DPH is efficient, and yields results close to optimal solutions, where the average gap is 2.20% for small sized instances. The average cost gap is 4.19% compared with lower bound for medium or large sized instances. We also apply the proposed heuristic to real-life data to elaborate joint production and cutting plans and compare with the current procedure in practice. The total cost is reduced by 8.81% on average, which amounts to 3.46 million RMB in cost saving in 2016 for the investigated company. The average cost gap is 5.03% compared with the lower bound. The total cost is reduced by 3.67% on average compared with the two-stage independent decision-making method commonly applied in industry.

International audience; Autonomous Vehicles (AV) are the future evolution of Land Transportation Systems (LTS). They promise an improvement in road safety. However, safety requirements stay a big challenge for their development. The literature presents a lack of insight on the way LTS safety requirements will evolve. This paper proposes an analysis method of LTS safety requirements evolution toward AV. The ASIL metric is used to evaluate the safety criticality. The application on two case studies, the steering by wire and platooning systems, results in a better understanding and characterization of this evolution.

International audience; In seismic risk assessment, the fragility curve is used to estimate the reliability of structures and equipment under seismic loads. The shape of fragility curves is usually approximated by the cumulative distribution function of a lognormal distribution. The estimation of the parameters of the fragility curves requires gathering different sources of information and quantifying the uncertainties coming from these sources. This paper proposes a methodology for the computation of fragility curves for nuclear power plant equipment, based on a Bayesian updating framework that combines the results of numerical simulations and damage data. An artificial neural network is trained iteratively by optimizing its prediction uncertainties over the ground motion sample space, and it is used to conduct numerical simulations. The results of the numerical simulations provide a prior estimation of the seismic capacity of the equipment. The estimation of the uncertainty related to the equipment capacity is taken from the literature. Damage data, collected from the in situ observation and the database of the seismic qualification utility group (SQUG), are used to construct the likelihood function for the Bayesian updating. The posterior equipment capacity is evaluated by Markov chain Monte Carlo simulation and posterior fragility curves are, then, obtained. The main contributions of the work are: (i) proposal of an adaptive training algorithm of artificial neural networks to improve the design of experiments for finite element simulations; (ii) proposal of a two-step transformation method to construct the likelihood function with existing damage data from the SQUG database. The methodology is applied to compute the fragility curves of a low-voltage switchgear of a nuclear power plant, within the so-called KARISMA benchmark.

International audience; Recent years have witnessed an increased awareness of the negative external impacts of freight transportation. The field of Operational Research (OR) has, particularly in the recent years, continued to contribute to alleviating the negative impacts through the use of various optimization models and solution techniques. This paper presents the basic principles behind and an overview of the existing body of recent research on 'greening' freight transportation using OR-based planning techniques. The particular focus is on studies that have been described for two heavily used modes for transporting freight across the globe, namely road (including urban and electric vehicles) and maritime transportation, although other modes are also briefly discussed.

Can machines design? Can they come up with creative solutions to problems and build tools and artifacts across a wide range of domains? Recent advances in the field of computational creativity and formal Artificial General Intelligence (AGI) provide frameworks for machines with the general ability to design. In this paper we propose to integrate a formal computational creativity framework into the Gödel machine framework. We call this machine a design Gödel machine. Such a machine could solve a variety of design problems by generating novel concepts. In addition, it could change the way these concepts are generated by modifying itself. The design Gödel machine is able to improve its initial design program, once it has proven that a modification would increase its return on the utility function. Finally, we sketch out a specific version of the design Gödel machine which specifically aims at the design of complex software and hardware systems. Future work could be the development of a more formal version of the Design Gödel machine and a potential implementation.

In seismic probabilistic risk assessment, fragility curves are used to estimate the probability of failure of a structure or its critical components at given values of seismic intensity measures, e.g. the peak ground acceleration. However, the computation of the fragility curves requires a large number of time-consuming mechanical simulations with the finite element method (FEM). To reduce the computational cost, in this paper a statistical metamodel based on artificial neural networks (ANNs) is constructed to replace the FEM model. An adaptive ANNs learning strategy, aimed at prioritizing the data close to the limit state of the structures, is proposed in order to improve the design of experiments for the fragility analysis. The adaptive learning strategy is developed and tested on a nonlinear Takeda oscillator.

International audience

International audience; Evaluating and comparing the threats and vulnerabilities associated with territorial zones according to multiple criteria (industrial activity, population, etc.) can be a time-consuming task and often requires the participation of several stakeholders. Rather than a direct evaluation of these zones, building a risk assessment scale and using it in a formal procedure permits to automate the assessment and therefore to apply it in a repeated way and in large-scale contexts and, provided the chosen procedure and scale are accepted, to make it objective. One of the main difficulties of building such a formal evaluation procedure is to account for the multiple decision makers' preferences. The procedure used in this article, Electre Tri, uses the performances of each territorial zone on multiple criteria, together with preferential parameters from multiple decision makers, to qualitatively assess their associated risk level. We also present operational tools in order to implement such a procedure in practice, and show their use on a detailed example.

There is a lack of standardized country-specific environmental data to combine with nutritional and dietary data for assessing the environmental impact of individual diets in epidemiology surveys, which are consequently reliant on environmental food datasets based on values retrieved from a heterogeneous literature. The aim of this study was to compare and assess the relative strengths and limits of a database of food greenhouse gas emissions (GHGE) values estimated with a hybrid method combining input/output and LCA approaches, with a dataset of GHGE values retrieved from the literature. France is the geographical perimeter considered in this study, but the methodology could be applied to other countries. The GHGE of 402 foodstuffs, representative of French diet, were estimated using the hybrid method. In parallel, the GHGE of individual foods were collected from existing literature. Median per-food-category GHGE values from the hybrid method and the reviewed literature were found to correlate strongly (Spearman correlation was 0.83), showing similar rankings of food categories. Median values were significantly different for only 5 (out of 29) food categories, including the ruminant meats category for which the hybrid method gave lower estimates than those from existing literature. Analysis also revealed that literature values came from heterogeneous studies that were not always sourced and that were conducted under different LCA modeling hypotheses. In contrast, the hybrid method helps build reliably-sourced, representative national standards for product-based datasets. We anticipate this hybrid method to be a starting point for better environmental impact assessments of diets.

International audience; The increased share of renewable generation and the integration of Distributed Generation (DG) require more electricity system flexibility. One way to increase this flexibility is to use the potentials of demand response (DR). In order to activate the full range of customers in DR, a new market intermediary actor is needed to aggregate the resources in an adequate technical and economical format. These actors, so called " aggregators " , can act as flexibility providers to support security of supply considering network, generation and consumers constraints. However, despite their technical and economical utility, aggregators are not self-emerging in many European countries. Consequently, this paper aims at identifying the main barriers accounting for this lack of aggregators in Europe. Eventually this paper provides a policy review for European market designs that support aggregation.

International audience; Electric vehicle (EV) industry is still in the introduction stage in product life cycle, and dominant design remains unclear. EV companies, both incumbent from the car industry and new comers, have long taken numerous endeavors to promote EV in the niche market by providing innovative products and business models. While most carmakers still take 'business as usual' approach for developing their EV production and offers, Tesla Motors, an EV entrepreneurial firm, stands out by providing disruptive innovation solutions. We review the business model approach in the literature, then classify the innovation dimensions in the EV ecosystem. We study Tesla Motors in terms of: (i) innovation related to the vehicle, (ii) innovation related to the battery (iii) innovation concerning the recharging system, and (iv) innovation toward the EV ecosystem. Lessons for incumbent carmakers for their EV business model design: Tesla Motors 1) holds a product strategy entering from high-end market and moving to mass market, with a high level of innovation adaptation and learning by doing; 2) pays considerable attention to reduce range anxiety by high performance supercharger station network and high capacity battery; 3) shows a very high level of integration of information technology into many aspects of the EV business model, such as advanced in-car services and digital distribute channel; 4) shows a new value configuration which involving in high level of vertical integration towards battery and recharging network. All these lessons of this chapter would be worth the attention of the carmakers if the disruptive choices of Tesla succeed in challenging the dominant design.

International audience; This book showcases a large variety of multiple criteria decision applications (MCDAs), presenting them in a coherent framework provided by the methodology chapters and the comments accompanying each case study. The chapters describing MCDAs invite the reader to experiment with MCDA methods and perhaps develop new variants using data from these case studies or other cases they encounter, equipping them with a broader perception of real-world problems and how to overcome them with the help of MCDAs.

International audience; In distributed design systems, while designers are connected to each other through dimensioning couplings, they have limited control over design and performance variables. Any inconsistency among design objectives and working procedures of heterogeneous designers interacting in the design system can result in design conflicts due to these couplings. Modeling design attitudes can help to understand inconsistencies and manage conflicts in design processes. We extend the conventional bottom-up or design supervision approach through agent-based attitude modeling techniques to a more powerful level. In our model, design agents can set requirements directly on their wellbeing values that represent how their design targets are likely to be met at a given moment of the design process. Some design conflicts can in this manner be prevented at an earlier phase of the design process. Set-based design and constraint programming techniques are used to explore the overall performance of stochastic design collaborations on a product modeled with uncertainties at a given moment of the design process. Monte Carlo simulations are performed to evaluate the performance of our set-based thinking approach, providing a variety of agent attitudes. The results show that the number of design conflicts occurring during the design process and the intensity of design conflicts are both reduced through our collaborative design platform.

International audience; Multiple criteria sorting aims at assigning alternatives evaluated on several criteria to predefined ordered categories. In this paper, we consider a well known multiple criteria sorting method, ELECTRE TRI, which involves three types of preference parameters: (1) category limits defining the frontiers between consecutive categories, (2) weights and majority level specifying which coalitions form a majority, and (3) veto thresholds characterizing discordance effects. We propose an elicitation procedure to infer category limits from assignment examples provided by multiple decision makers. The procedure computes a set of category limits and vetoes common to all decision makers, with variable weights for each decision maker. Hence, the method helps reaching a consensus among decision makers on the category limits and vetothresholds, whereas finding a consensus on weights is left aside. The inference procedure is based on mixed integer linear programming and performs well even for datasets corresponding to real-world decision problems. We provide an illustrative example of the use of the method and analyze the performance of the proposed algorithms.

France, like many other western countries, observes a massive increase of health care costs. This is partly due to the rapidly ageing population and the growth of the number of patients suffering from chronic diseases. Simultaneously, the number of medical staff decreases in many regions. To overcome these developments, telehealth seems to be one of the responses that ensure accessibility for all to quality care. The main idea of telehealth is to provide a remote access of health care services thus closing the distance with appropriate health care professionals. However, the introduction of telehealth involves a major organizational change. The purpose of this paper is to present the research framework and to discuss the development of methods and tools of industrial engineering suitable for developing a new telemedicine system and realize its medico-economic evaluation. Thus, we propose a research framework to explore several scenarios in order to elaborate an optimal organization in terms of economic development considering all values sought by stakeholders. Specific tools such as systems approach and value analysis are proposed to be used to help design the telehealth system. A first application of these methods and tools is discussed regarding the telehealth experience named Telegeria at the Georges-Pompidou European Hospital of Paris., La France, comme nombre d'autres pays occidentaux, assiste à une inflation des coûts de santé dûe en partie au vieillissement rapide de la population et à l'accroissement du nombre de malades atteints de pathologies chroniques. Simultanément nous assistons à une diminution des densités de personnels médicaux dans de multiples régions. Pour pallier ces évolutions, la télémédecine apporte une réponse qui participe à l'accessibilité de tous à des soins de qualité. Elle permet d'accéder à distance à des professionnels de santé. Toutefois, l'introduction de la télémédecine entraîne un changement organisationnel majeur. L'objet du présent article est de présenter le cadre de recherche ainsi que de discuter le développement de méthodes et outils de génie industriel appropriés permettant d'élaborer un nouveau système de télémédecine et de réaliser son évaluation médico-économique. Ainsi nous proposons un cadre de recherche afin d'explorer plusieurs scenarii d'organisation de l'infrastructure du point de vue économique tout en garantissant l'ensemble de valeurs recherchées par toutes les parties prenantes. Les outils conceptuels spécifiques tels que l'approche systémique et l'identification de valeurs seront utilisés afin d'aider à la conception du système de santé en télémédecine. Une première application de ces méthodes et outils sera évoquée concernant les travaux en cours dans le cadre du programme de télémédecine Télégéria de l'Hôpital Européen Georges-Pompidou de Paris.

International audience; As soon as the products to design must not only deliver pure services but have also to exhibit esteem or subjective functions such as aesthetics, style, cultural values and ergonomic considerations, both individual and group product evaluations as well as product specification are problematic. Indeed, for both design tasks, designers must qualify the expected properties of a product and find relevant measurement scales to quantify them. In this paper, we propose a design method named COMPARE for understanding the perception of existing products (before starting the creation process), for defining the specifications for a new product and, finally, for evaluating the emerging design concepts under the expected properties (perceptual attributes). The originality of this method is to be based on elementary and qualitative Pairwise Comparisons of products under given perceptual attributes (and also further Principal Component Analysis). It results in two fundamental properties of user-friendliness for the designer and easiness for automatically generate measurement scales under perceptual attributes. The COMPARE method has been experimented on the analysis and the design of automotive dashboards.

Multiple criteria decision aid methodologies support decision makers (DM) facing decisions involving conflicting objectives. DM’s preferences should be captured to provide meaningful recommendations. Preference elicitation aims at incorporating DM’s preferences in decision models. We propose a new preference elicitation tool for a ranking model based on reference points (RMP—Ranking with Multiple Profiles). Our methodology infers an RMP model from a list of pairwise comparisons provided by the DM. The inference algorithm makes use of a Mixed Integer mathematical programming formulation. We prove the applicability by performing extensive numerical experiments on datasets whose size corresponds to real-world problem.

International audience; Nous sommes entrés dans l'ère du risque, qu'il est difficile d'établir une comparaison entre risques réels et risques perçus, et d'identifier quels sont les acteurs qui peuvent intervenir dans ce débat "risque et société". A. Giddens, sociologue britannique, affirme que ce qui différencie le risque moderne des dangers pré-modernes, c'est d'abord la conscience du risque en tant que conséquence d'un savoir développé et appliqué par les hommes dans leurs sociétés. Comment la société et plus particulièrement les entreprises (acteur social essentiel aujourd'hui) peuvent elles affronter cette nouvelle situation ? Comment collaborer ensemble à l'évaluation et la gestion du risque environnementale ? Quel serait le rôle qui reviendrait à la puissance publique pour assurer la couverture des risques ?Deux options sont souvent mises en place pour répondre aux enjeux posés par la nouvelle problématique du risque : mise en place d'une communication nouvelle ou transfert de responsabilité sur d'autres acteurs parties prenantes.

International audience; In this paper, we develop a new method for Multi-Hazards Risk Aggregation (MHRA). A hierarchical framework is first developed for evaluating the trustworthiness of the risk assessment. The evaluation is based on two main attributes (criteria), i.e., the strength of knowledge supporting the assessment and the fidelity of the risk assessment model. These two attributes are further broken down into sub-attributes and, finally, leaf attributes. The trustworthiness is calculated using a weighted average of the leaf attributes, in which the weights are calculated using the Dempster Shafer Theory-Analytical Hierarchy Process (DST-AHP). Risk aggregation is, then, performed by a “weighted posterior” method, considering the level of trustworthiness. An application to the risk aggregation of two hazard groups in Nuclear Power Plants (NPP) is illustrated.

While the transportation sector is responsible for a growing share of greenhouse gas emissions, electric vehicles (EVs) offer solutions for greener mobility. The proportion of electric vehicles in transportation fleets is increasing, but wider adoption will not be possible without an appropriate charging infrastructure. The deployment of such infrastructure should follow a strategy that considers both the environment in which it is deployed and the behavior patterns of electric vehicle users. If these aspects are not taken into consideration, there is a risk of failing to meet users’ needs and generating additional costs. Here we review the literature on location problems for electric vehicle charging stations. We aim to draw up a comparative overview of approaches that have been used up to 2020 for optimizing the locations of charging infrastructure. We first briefly review the issues raised by the deployment of charging infrastructure, namely technical, economic and user acceptance concerns. We then look at the goals of the infrastructure location models in the literature. Schematically, those goals fall into two categories: minimizing the cost of charging infrastructure for a given level of service, or maximizing the service provided for a given cost. Finally, we focus on the approaches used to achieve these goals. Three categories of approaches are identified: node, path, and tour- or activity-based approaches. We then discuss these approaches in relation to technical, economic and user acceptance factors in order to provide a comprehensive analysis for stakeholders involved in EV charging infrastructure planning. Directions are given for future research to develop models that better reflect the real-world picture.

International audience

International audience

Purpose The purpose of this study was to report the clinical evaluation of a 3D-printed protective face shield designed to protect interventional radiologists from droplet transmission of the SARS-Cov-2. Materials and methods A protective face shield consisting in a standard transparent polymerizing vinyl chloride (PVC) sheet was built using commercially available 3D printers. The 3D-printed face shield was evaluated in 31 interventional procedures in terms of ability to perform the assigned intervention as usual, quality of visual comfort and tolerance using a Likert scale (from 1, as very good to 5, as extremely poor). Results The mean rating for ability to perform the assigned intervention as usual was 1.7 ± 0.8 (SD) (range: 1–4). The mean visual tolerance rating was 1.6 ± 0.7 (SD) (range: 1–4). The mean tolerability rating was 1.4 ± 0.7 (SD) (range: 1–3). Conclusion The 3D-printed protective face shield is well accepted in various interventions. It may become an additional option for protection of interventional radiologists.

International audience; This research set out to identify and structure from online reviews the words and expressions related to customers' likes and dislikes to guide product development. Previous methods were mainly focused on product features. However, reviewers express their preference not only on product features. In this paper, based on an extensive literature review in design science, the authors propose a summarization model containing multiples aspects of user preference, such as product affordances, emotions, usage conditions. Meanwhile, the linguistic patterns describing these aspects of preference are discovered and drafted as annotation guidelines. A case study demonstrates that with the proposed model and the annotation guidelines, human annotators can structure the online reviews with high inter-agreement. As high inter-agreement human annotation results are essential for automatizing the online review summarization process with the natural language processing, this study provides materials for the future study of automatization.

International audience; Designers improve urban mobility solutions by investigating archetypal usage problems in existing mobility systems. User-centred design methods help accomplish this task, but lack effectiveness when not supported by appropriate tools. Here we posit that the use of a traveller-centred stimulus improves the effectiveness of travel problem generation. To test this hypothesis, an experiment is conducted with two control groups as a baseline for non-stimulated problem generation and two experimental groups that are provided with a traveller-centred stimulus. The two sets of groups are composed of one group of urban mobility experts and one group of non-experts. Results show that stimulated groups generate novel ideas with a greater variety covering most of the traveller experience dimensions than non-stimulated groups.

International audience; In a context of transition towards a more circular economy (CE), this study undertakes an analysis of the appropriate transfers and applications of best managerial practices, regulations and know-how from the automotive sector to the heavy vehicle one, as well as from the European Union (EU) to the United States of America (U.S.), and vice versa. While the EU appears to be a few steps ahead of policy activity regarding the management of end-of-life automotive vehicles (Directive 2000/53/EC and Extended Producer Responsibility principles), the U.S. heavy vehicle industry presents several aspirational industrial practices, including the collaboration between end-of-life actors, supporting parts remanufacturing and facilitating reuse. New empirical findings and lessons learned from both geographical regions are combined with previous analyses to develop a benchmarking template of commendable CE practices. Against this background, the findings open on the remaining challenges and circular economy opportunities for both regions, as well as for emerging and newly industrialized countries whose automotive markets are growing fast.

Product Service Systems (PSS) are increasingly complex and collaborative. For instance, manufacturing companies, service providers, and other companies collaborate and jointly develop and operate a PSS (ex: smart grid), where its constituent elements are managed and operated independently. Managerial independence and operational independence are commonly considered key characteristics of a System of Systems (SoS). Hence, a collaborative PSS exhibits System of Systems (SoSs) characteristics. These systems have previously been introduced as Product Service Systems of Systems (PSSoSs). In this paper, we propose to identify relevant uncertainties in the PSSoS design process. For this purpose, we go beyond the PSSoS concept definition and propose a comprehensive framework for PSS and PSSoS characterization. Moreover, based on both a literature review and an industrial diagnosis, we identify PSSoSs-specific design uncertainties.

Multi-objective system reliability optimization has attracted the attention of several researchers, due to its importance in industry. In practice, the optimization regards multiple objectives, for example, maximize the reliability, minimize the cost, weight, and volume. In this article, an adaptive particle swarm optimization is presented for multi-objective system reliability optimization. The approach uses a Lévy flight for some particles of the swarm, for avoiding local optima and insuring diversity in the exploration of the search space. The multi-objective problem is converted to a single-objective problem by resorting to the weighted-sum method and a penalty function is implemented to handle the constraints. Nine numerical case studies are presented as benchmark problems for comparison; the results show that the proposed approach has superior performance than a standard particle swarm optimization.

International audience; Design space exploration (DSE) describes the systematic activity of discovery and evaluation of the elements in a design space in order to identify optimal solutions by reducing the design space to an area of performance. Designers sample thousands of design points iteratively, explore the design space, gain knowledge about the problem and make design decision. The literature tells us that DSE results in a decision of quality called informed decision, which is supported by information visualization. The representation of design points is seen as primordial to gain an understanding of the problem and make an informed decision. In our work, we have sought to identify what type of graph is best suited to the discovery phase, and enables designers to make an informed decision. We designed a web platform with four design problems, and carried out an experiment with 42 participants. We found a graph that was better suited to making a decision of quality and to gaining greater understanding: the scatter plot matrix.

International audience; Fast urbanization generates increasing amounts of travel flows, urging the need for efficient transport planning policies. In parallel, mobile phone data have emerged as the largest mobility data source, but are not yet integrated to transport planning models. Currently, transport authorities are lacking a global picture of daily passenger flows on multimodal transport networks. In this work, we propose the first methodology to infer dynamic Origin-Destination flows by transport modes using mobile network data e.g., Call Detail Records. For this study, we pre-process 360 million trajectories for more than 2 million devices from the Greater Paris as our case study region. The model combines mobile network geolocation with transport network geospatial data, travel survey, census and travel card data. The transport modes of mobile network trajectories are identified through a two-steps semi-supervised learning algorithm. The later involves clustering of mobile network areas and Bayesian inference to generate transport probabilities for trajectories. After attributing the mode with highest probability to each trajectory, we construct Origin-Destination matrices by transport mode. Flows are up-scaled to the total population using state-of-the-art expansion factors. The model generates time variant road and rail passenger flows for the complete region. From our results, we observe different mobility patterns for road and rail modes and between Paris and its suburbs. The resulting transport flows are extensively validated against the travel survey and the travel card data for different spatial scales

International audience; We introduce a new perspective to improve the reliability of a network, which aims at finding cost-effective portfolios of Prognostics and Health Management (PHM) systems to be installed throughout the network. To do this, we estimate the reliability of the single network element equipped with a PHM system, whose prognostic performance is measured in terms of the − α λ performance, false positive and false negative metrics. Then, we apply genetic algorithms for finding the portfolios of PHM systems to be installed on the network elements, which are optimal with respect to cost and a global reliability efficiency index of the network. The workbench case study of the IEEE 14 bus network is considered as application.

International audience; The way project actors are organized is crucial in determining how they will be able to collectively cope with nontrivial complex problems and risks. Current project organizations are generally based on single-criterion decomposition, whether product, process, or organization based. The proposed approach forms complementary clusters of actors based on the interdependencies between the risks they manage. More precisely, distinction has been made between the interdependencies connecting two risks that are owned by different actors and those owned by the same actor. We argue that interdependency between two risks managed by the same actor is less dangerous, meaning that clustering algorithm is tailored to distinguish mono-and biactor risk interdependencies. The complementary structure offered by interdependency-based clustering tends to put together strongly interconnected actors, albeit they were often initially not grouped together. It increases the likelihood of a better communication, coordination, and collective decision-making in complex situations. Some risks remain out of proposed clusters and are declared transverse, which means that their owners act as information hubs and are not involved in a single cluster. An industrial application is presented with operational results and perspectives for further work are drawn from it.

International audience; A sequential Bayesian approach is presented for remaining useful life (RUL) prediction of dependent competing failure processes (DCFP). The DCFP considered comprises of soft failure processes due to degradation and hard failure processes due to random shocks, where dependency arises due to the abrupt changes to the degradation processes brought by the random shocks. In practice, random shock processes are often unobservable, which makes it difficult to accurately estimate the shock intensities and predict the RUL. In the proposed method, the problem is solved recursively in a two-stage framework: in the first stage, parameters related to the degradation processes are updated using particle filtering, based on the degradation data observed through condition monitoring; in the second stage, the intensities of the random shock processes are updated using the Metropolis-Hastings algorithm, considering the dependency between the degradation and shock processes, and the fact that no hard failure has occurred. The updated parameters are, then, used to predict the RUL of the system. Two numerical examples are considered for demonstration purposes and a real dataset from milling machines is used for application purposes. Results show that the proposed method can be used to accurately predict the RUL in DCFP conditions.

International audience; In this work, we propose a multi-perspective framework of analysis of critical infrastructures (CIs) with respect to supply service, topology and controllability. The framework enables identifying the role of CI elements and quantifying the consequences of scenarios of multiple failures, with respect to the different perspectives considered. To present the analysis framework, a benchmark network representative of a real gas transmission network across several countries of the European Union (EU) is considered. The information extracted from such analysis can help us to identify the critical elements and how the properties of the network are affected by failures, and to propose corresponding improvements for CIs. The findings of this paper demonstrate the interest of considering several perspectives in the analysis of CIs for providing useful information for ensuring their safe and reliable operation.

We consider reliability engineering in modern civil aviation industry, and the related engineering activities and methods. We consider reliability in a broad sense, referring to other system characteristics that are related to it, like availability, maintainability, safety and durability. We covered the entire lifecycle of the equipment, including reliability requirement identification, reliability analysis and design, verification and validation of reliability requirements (typically involved in the equipment design and development phase), quality assurance (which typically enters in the manufacturing phase), and fault diagnosis and prognosis and maintenance (which are connected to the operation phase). Lessons learnt from reliability engineering practices in civil aviation industry are given, which might serve as reference for reliability managers and engineers, also from other industries with high reliability requirements. Keywords: Civil aviation, Commercial aircraft, Fault diagnosis and prognosis, Human reliability analysis, Maintenance, Quality control, Reliability design, Reliability engineering

International audience; In this paper, we propose a conceptual framework for classifying operations management-related issues for returnable transport items (RTI). RTI closed-loop supply chain (CLSC) issues are classified into three categories. Our proposal is based on a global literature review in the field of CLSC and reverse logistics (RL), with a specific focus on transport items reuse. We also derive our work from observations made in the automotive industry. The motivation for dealing with this topic is that more and more companies are adopting RTIs in their supply chain, and despite the growing attention that CLSC and RL research have gained during the last years, the focus is still more oriented toward remanufacturing issues. The objectives of this paper are twofold. Firstly, to provide practitioners with a clear vision on different issues that arise when implementing a RTI system from an operations management point of view, and secondly, to give suggestions to researchers regarding existing gaps that still need to be filled in this field.

International audience; A B S T R A C T Phased-mission systems (PMSs) are widely used, especially in the aerospace industry. As in the outer space there are many kinds of cosmic rays, such as the Galactic Cosmic Rays (GCR), randomly hitting on these systems and causing significant impact on the electronics inside or outside the equipment, a reliability model for PMSs considering both finite and infinite random shocks is proposed in this paper. The modularization method is used to simplify the state space model for each phase and reduce the amount of system states, and the Markov regenerative process (MRGP) is used to describe the hybrid components' lifetime distributions and the dynamic behaviors within the modules. Then, two kinds of scenarios, finite and infinite random shocks effect, are both integrated into the dynamic modules. For demonstration, a phased altitude and orbit control system (AOCS) subjected to infinite random shocks is illustrated to demonstrate the procedure of the proposed Monte Carlo simulation. Thirdly, the evaluated system reliability under infinite random shocks is compared with the same system without considering random shocks. At last, a sensitivity analysis is also provided for completion.

International audience; SO/TR 14121-2: 2012 considers three factors to describe the likelihood of the occurrence of an incident scenario: the frequency of exposure of persons to the hazard, the probability of occurrence of the hazardous event and the technical and human possibilities of avoiding harm. The assessment of these factors can be quite controversial, especially when it concerns the amount of risk removable by protective measures: their mapping onto the risk factors is not always clear and this can lead to non-conservative over-estimations of the risk reduction. We propose a methodological framework compliant with ISO 12100 to systemically carry out repeatable risk analyses in support to the design of industrial machinery in which protective measures can be introduced to reduce risk. The methodology first proposes a scheme for identifying the contribution of PMs to the reduction of risk in a machinery under design. Then, the methodology classifies the protective measures and builds a clear mapping between these classes and the risk factors they impact on. This helps decision makers to identify the protective measures guaranteeing that the residual risk is acceptable. The methodology is applied to a real case study concerning a curing machine for tyre vulcanization, where it has proven to be beneficial for the clarity of the analysis and its repeatability.

International audience; The increasing demand for goods, especially in urban areas, together with the technological advances are creating both opportunities and challenges for planning urban freight systems. One of these promising opportunities is to use the underused assets in people-based systems to transport goods. In this paper, we consider an integrated system in which a set of freight requests needs to be delivered using a fleet of grounded, and autonomous, pickup and delivery (PD) robots where a public transportation service (referred to as scheduled line (SL)) can be used as part of PD robot's journey. Passengers and PD robots (carrying freight) share the available capacity on SLs where passengers are prioritized, and their transport demand is stochastic. Thus, the number of available places for PD robots is only revealed upon shuttle arrival to the corresponding SL station. We first formulate this problem as a Pickup and Delivery Problem with Time Windows and Scheduled Lines (PDPTW-SL). We then introduce a sample average approximation (SAA) method along with an Adaptive Large Neighborhood Search (ALNS) algorithm for solving the stochastic optimization problem. Finally, we present an extensive computational study, analyze its results, and give some directions for future research.

International audience; Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants.

International audience; Scenario planning methods tend to work at an aggregate level and to consider homogeneous populations, thus levelling the variable effects of future developments on different social groups. Decision-making based on such scenarios bears the risk of missing undesired impacts on specific groups of people, which may cause social tensions or require costly countermeasures. New approaches are needed to provide a better basis for socially responsible planning by making clearer the complex social impacts of future scenarios. In marketing and user-centered design, persona models are developed to represent typologies of users to cover a broad range of needs and requirements. We propose a systematic method, called the Scenario Personarrative method, for combining scenario thinking and personas into a structured but nonetheless flexible process, which allows generating fine-grained individualized narratives. We describe a pilot application in a case study on urban mobility. This pilot used existing scenarios and focused on a two-hour workshop where twelve experts created personas and the associated narratives across three scenarios of urban mobility in 2030. This pilot shows the applicability of the method for making potential effects on different social groups more tangible. We propose ways forward for further evaluation of the proposed methodology.

International audience; One major issue that is source of uncertainty holding back the hydrogen deployment, is the infrastructure development needs and costs. Different studies in the literature tackled this issue in France but partially, assessing either one part of the hydrogen supply chain or the whole supply chain but for one possible delivery pathway. This paper compares five hydrogen pathways, going from the production step up to the fuelling station and tackling pipeline and truck options. In order to capture the time evolution aspect of the infrastructure deployment needs, three demand scenarios are investigated, going from 1% of market penetration up to 15%. Additionally, two scenarios are taken into account when it comes to the location of the hydrogen production sites vis-à-vis the demand centres. According to the results, economies of scale that can be driven by higher market penetration rates have significant impact on lowering the hydrogen cost.

International audience; We describe a project where Discrete-Event Simulation (DES) is combined with Soft Systems Methodology (SSM) and ethnographic observation to support the improvement of patient flows in an outpatient chemotherapy unit and a chemotherapy preparation pharmacy unit. The project combines three interesting characteristics. First, we rely on ethnographic observation to explore the problem situation and frame the problem, a technique rarely reported in operational research (OR). Second, this leads us to define several root definitions, an aspect that remains overlooked in studies combining SSM and DES. Finally, the project stands at the interface between two departments (outpatient oncology unit and chemotherapy preparation pharmacy unit), a situation seldom explored in healthcare OR but where SSM has arguably a lot to offer to help identify and accommodate diverging objectives. We framed the problem, developed a simulation model, explored scenarios and refined a preferred one into a new business process. However, the proposed changes were not implemented. We analyse the outcomes of the project, the contribution of each method and their integration into an intervention. The paper provides insights on how SSM and DES can be combined in practice, and how the potential of ethnographic observation can be leveraged in this context.

International audience; Consumers in smart grids are expected to engage demand-response programs by two-way communication. This makes smart grids vulnerable to cyber attacks. In this paper, we study the false pricing attacks and model the interaction between attackers and defenders using a zero-sum Markov game, where neither player has full knowledge of the game model. A multi-agent reinforcement learning method is used to solve the Markov game and find the Nash Equilibrium policies for both players. An application to a simple radial power distribution system is worked out. The results show that the proposed algorithm can help the players find mixed strategies to maximize their long-term return.

Degradation is an unavoidable phenomenon that affects engineering components and systems, and which may lead to their failures with potentially catastrophic consequences depending on the application. The motivation of this Thesis is trying to model, analyze and predict failures with prognostic methods that can enable a predictive management of asset maintenance. This would allow decision makers to improve maintenance planning, thus increasing system availability and safety by minimizing unexpected shutdowns. To this aim, research during the Thesis has been devoted to the tailoring and use of both model-based and data-driven approaches to treat the degradation processes that can lead to different failure modes in industrial components, making use of different information and data sources for performing predictions on the degradation evolution and estimating the Remaining Useful Life (RUL).The Ph.D. work has addressed two specific prognostic applications: model-based prognostics for fatigue crack growth prediction and data-driven prognostics for multi-step ahead predictions of time series data of Nuclear Power Plant (NPP) components.Model-based prognostics relies on the choice of the adopted Physics-of-Failure (PoF) models. However, each degradation model is appropriate only to certain degradation process under certain operating conditions, which are often not precisely known. To generalize this, ensembles of multiple degradation models have been embedded in the model-based prognostic method in order to take advantage of the different accuracies of the models specific to different degradations and conditions. The main contributions of the proposed ensemble of models-based prognostic approaches are the integration of filtering approaches, including recursive Bayesian filtering and Particle Filtering (PF), and novel weighted ensemble strategies considering the accuracies of the individual models in the ensemble at the previous time steps of prediction. The proposed methods have been validated by case studies of fatigue crack growth simulated with time-varying operating conditions.As for multi-step ahead prediction, it remains a difficult task of Prognostics and Health Management (PHM) because prediction uncertainty tends to increase with the time horizon of the prediction. Large prediction uncertainty has limited the development of multi-step ahead prognostics in applications. To address the problem, novel multi-step ahead prediction models based on Long Short- Term Memory (LSTM), a deep neural network developed for dealing with the long-term dependencies in the time series data have been developed in this Thesis. For realistic practical applications, the proposed methods also address the additional issues of anomaly detection, automatic hyperparameter optimization and prediction uncertainty quantification. Practical case studies have been considered, concerning time series data collected from Steam Generators (SGs) and Reactor Coolant Pumps (RCPs) of NPPs., La dégradation est un phénomène inévitable qui affecte les composants et les systèmes d'ingénierie, et qui peut entraîner leurs défaillances avec des conséquences potentiellement catastrophiques selon l'application. La motivation de cette Thèse est d'essayer de modéliser, d'analyser et de prédire les défaillances par des méthodes pronostiques qui peuvent permettre une gestion prédictive de la maintenance des actifs. Cela permettrait aux décideurs d'améliorer la planification de la maintenance, augmentant ainsi la disponibilité et la sûreté du système en minimisant les arrêts imprévus. Dans cet objectif, la recherche au cours de la thèse a été consacrée à l'adaptation et à l'utilisation d'approches basées sur des modèles et d'approches pilotées par les données pour traiter les processus de dégradation qui peuvent conduire à différents modes de défaillance dans les composants industriels, en utilisant différentes sources d'informations et de données pour effectuer des prédictions sur l'évolution de la dégradation et estimer la durée de vie utile restante (RUL).Les travaux de thèse ont porté sur deux applications pronostiques spécifiques: les pronostics basés sur des modèles pour la prédiction de la croissance des fissures par fatigue et les pronostics pilotées par les données pour les prédictions à pas multiples des données de séries chronologiques des composants des Centrales Nucléaires.Les pronostics basé sur des modèles compter sur le choix des modèles adoptés de Physics-of-Failure (PoF). Cependant, chaque modèle de dégradation ne convient qu'à certains processus de dégradation dans certaines conditions de fonctionnement, qui souvent ne sont pas connues avec précision. Pour généraliser, des ensembles de multiples modèles de dégradation ont été intégrés dans la méthode pronostique basée sur les modèles afin de tirer profit des différentes précisions des modèles spécifiques aux différentes dégradations et conditions. Les principales contributions des approches pronostiques proposées basées sur l'ensemble des modèles sont l'intégration d'approches de filtrage, y compris le filtrage Bayésien récursif et le Particle Filtering (PF), et de nouvelles stratégies d'ensemble pondérées tenant compte des précisions des modèles individuels dans l'ensemble aux étapes de prédiction précédentes. Les méthodes proposées ont été validées par des études de cas de croissance par fissures de fatigue simulées dans des conditions de fonctionnement variables dans le temps.Quant à la prédictions à pas multiples, elle reste une tâche difficile pour le Prognostics and Health Management (PHM) car l'incertitude de prédiction a tendance à augmenter avec l'horizon temporel de la prédiction. La grande incertitude de prédiction a limité le développement de pronostics à pas multiples dans les applications. Pour résoudre le problème, de nouveaux modèles de prédiction à pas multiples basés sur la Long Short-Term Memory (LSTM), un réseau de neurones profond développé pour traiter les dépendances à long terme dans les données de séries chronologiques, ont été développés dans cette Thèse. Pour des applications pratiques réalistes, les méthodes proposées abordent également les problèmes supplémentaires de détection d'anomalie, d'optimisation automatique des hyper-paramètres et de quantification de l'incertitude de prédiction. Des études de cas pratiques ont été envisagées, concernant les données de séries chronologiques collectées auprès des Générateurs de Vapeur et de Pompes de Refroidissement de Réacteurs de Centrales Nucléaires.

Engineering design methods are typically evaluated via case studies, surveys, and experiments. Meanwhile, domains such as the health sciences as well as software engineering have developed further powerful evaluation approaches. The objective of this paper is to show how evaluation approaches from the health sciences and software engineering might further the evaluation of engineering design methods. We survey these approaches and show which approaches could be transferred to the evaluation of engineering design methods.

Drinking water for human health and well-being is crucial. Accidental and intentional water contamination can pose great danger to consumers. Optimal design of a system that can quickly detect the presence of contamination in a water distribution network is very challenging for technical and operational reasons. However, on the one hand improvement in chemical and biological sensor technology has created the possibility of designing efficient contamination detection systems. On the other hand, methods and tools from complex network theory, which was primarily the domain of mathematicians and physicists, provide analytical output for engineers to design, optimize, operate, and maintain complex network systems such as power grids, water distribution networks, telecommunication systems, internet, roads, supply chains, traffic and transportation systems. In this work, we develop a new modeling approach for the optimal placement of sensors for contamination detection in a water distribution network. The approach originally combines classical optimization and complex systems theory., 23 pages, 8 figures, 7 tables

International audience; Modeling the evolution of system reliability in the presence of Condition Monitoring (CM) signals is an important issue for improved reliability assessment and system lifetime prediction. In practice, during its lifetime, a system usually works under varying operating conditions due to internal or external factors such as the ambient environments, operational profiles or workloads. In this context, the system reliability can show varying evolution behaviors (follow changing underlying trajectories), which presents new challenges to describe precisely the dynamics of system reliability. Thus, this paper proposes a novel data-driven approach to address the problems including the identification of varying operating conditions, the construction and dynamical updating of evolution model, and finally the online prediction of system reliability, focusing on systems under one common and typical case of varying operating conditions, the multi-state operating condition. Experiments based on artificial data and some widely studied real reliability cases reveal that the proposed method has superior performance compared with some existing benchmark approaches, in the case under consideration. This improved reliability prediction provides fundamental basis for advanced prognostics such as the Remaining Useful Life (RUL) estimation.