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.

International audience

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

In this report, we aim at determining the probability distribution of components demand in Alpha plants. Alpha is one of the world's top ten auto parts makers (the company's name has been altered for confidentiality issues). Based on the case of Alpha, we develop a general method of components requisitions and forecasts analysis in Assemble-to-Order systems. This method allows to find the probability distribution of components demand and to estimate the parameters of this distribution. We apply the proposed method to the case of an Alpha plant situated in the west of France. We find that component demand follows a compound Poisson distribution.

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

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.

International audience; Operational Technologies (OT) are designed to monitor and control plants. OT are increasingly mixed with Information Technologies (IT) in global solutions. A conventional customer inquiry is no more sufficient to get enough data about Customer Concerns (CC). Indeed, an IT OT solution is the nervous system of a company; it intertwines people processes and functions. For specification step, one must now capture negative perceptions in the interrelationships with other actors of the customer company. The paper creates a database of CC describing dissatisfactions between several involved personas.

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; In a multidisciplinary virtual prototype development project, OEMs (Original Equipment Manufacturers) take the responsibility of requirement specification, system design, and Integration and Verification and Validation (IVV) steps. This is followed by the supplier, which develops the domain level simulation models. Although the simulation model is tested at the supplier level and the OEMs are responsible for the final integration, system and acceptance testing to ensure that the given implementation of a system level simulation model meets its intended goals. However, in current system IVV practices, OEM detects any kind of inconsistencies problems during final integration stage. This significantly increases the risk that errors are found late in the development process. This may create unexpected rework that negatively affects cost and schedule. The aim of this work is to reduce the late inconsistency detection by ensuring the early stage collaboration with a clear simulation model request and design artifact negotiation. For this aim, this paper proposes an early stage virtual prototyping design that contains the formal system architecture design, Model Identity Card (MIC) and Model of Intention (MoI) concepts. An industrial case study is used to show how such mentioned early stage virtual prototyping might reduce the knowledge gap and inconsistencies problems between OEMs and model providers.

International audience; During the last decades modelling and simulation technics has grown in importance in the product development context. For example, from an industrial point of view, simulation models seem to be an excellent alternative on vehicle construction and more specifically, in the decision making process. Nevertheless, the simulation activity becomes more difficult with the complexity of the product, highlighting more and more often a collaborative problem on the organization of the product development. But, how can this problem be defined? Several collaborative approaches have been proposed in this field. However, the majority of those approaches concern only one dimension of the problem. This paper introduces the Collaborative Modelling & Simulation System (CM&SS) from a systemic perspective in vehicle industry context. The systemic approach enables the definition of different dimensions of the system aiming at a successful performance of a collaborative simulation.

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; Plasmonic refractometric nanosensors based on single nanostructures, i.e. spherical, nanorod- and bipyramid-shaped gold nanoparticles, are investigated and compared numerically by employing the finite-difference time-domain method. The results show that the plasmonic sensing ability is distributed anisotropically around the nanorod and bipyramid, even for spherical nanoparticles when the illumination light is linearly polarized. To optimize nanosensor performance, some anisotropy in the shape of nanoparticles is required, this latter serving as an intrinsic light polarization filter to suppress the disturbance from localized surface plasmon resonance in other directions. The plasmonic near-field can be engineered by controlling the shape to achieve a concentrated and localized electromagnetic field, in direct relation with the sensing ability. Taking these factors into account, the gold bipyramid nanoconstruct which is easily available in experiment is proposed as an efficient plasmonic sensing platform. The bipyramid presents both highly localized sensitivity and high scattering cross-section, thus avoiding the trade-off during the selection of the widely used nanorod-shaped sensors. The parameters of the bipyramid structure can be optimized by numerical simulation to improve the plasmonic sensing. Our findings permit a deeper understanding of single-nanoparticle-sensor behavior, and the study provides an opportunity to optimize the plasmonic sensor.

International audience; We propose and justify by the finite-difference time-domain method an efficient strategy to enhance the spontaneous emission of a fluorophore with a multi-resonance plasmonic antenna. The custom-designed asymmetrical antenna consists of two plasmonic nanoparticles with different sizes and is able to couple efficiently to free space light through multiple localized surface plasmon resonances. This design simultaneously permits a large near-field excitation near the antenna as well as a high quantum efficiency, which results in an unusual and significant enhancement of the fluorescence of a single emitter. Such an asymmetrical antenna presents intrinsic advantages over single particle or dimer based antennas made using two identical nanostructures. This promising concept can be exploited in the large domain of light-matter interaction processes involving multiple frequencies.

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

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.

In product platform development the technology selection issue has seldom been studied. This article tries to fill this gap by introducing an original Technology Selection Method (TSM) to help designers to manage technology selection within the design of a product family. This method is based on technological coverage and functional verification, including the behaviour of the future product family. Practically, the proposed approach uses a multi-objective analysis based on dimensional analysis theory to choose the best technology available. The developed approach has three major points of interest. First, dimensional analysis theory is considered as a specific type of multi-objective optimisation approach which aggregates attributes using a weighting method based on the laws of physics. This manner of viewing dimensional analysis theory provides a scientific coherence to the weighting process which is not obtainable with other multi-objective methods. Second, the principle of similarity included in dimensional analysis theory is extended and allows a range of technologies and range of functions to be compared in a unique design space. Third, the dimensional analysis provides a powerful simulating tool for studying different kinds of behaviours and interactions between attributes. Consequently, dynamic aspects can be analysed and robustness analyses can be performed. The interest of such a method is highlighted through a case study involving a family of small excavators. We suggest that the potential scope of the approach is broad and our aim is to demonstrate the entire scope of the approach in future research.

International audience; Foresight studies on scientific, technological and organizational innovation affirm that, during the next 50 years, there may be an explosion of radical eco-efficient innovations (covering energy, environment, the agricultural and farm-produce industry, information and communication, new materials, robotics, space, transportation, medicine and genetic engineering). Much of the debate about the compatibility of sustainable development and innovation-based competitiveness hinges on views about the extent to which technological change will promote competitiveness, employment and eco-efficiency.

International audience

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; With the increasing penetration of electric vehicles (EVs), more and more interactions appear between the transportation system and the power system, which might provide new hazards and channels for the proliferation of failures across the boundaries of the individual systems. In this context, this paper proposes an integrated risk assessment framework for an electric power system, considering scenarios that involve the electrified transportation system enabled by EVs charging technology in New York (NY) State. Firstly, scenarios in the transportation network of NY State, e.g. of reduced capacity and incident, are generated by a Monte Carlo non-sequential algorithm. Then, the cell transmission model (CTM) is used to simulate the evolution of the traffic flows under such scenarios. This allows evaluating the spatial-temporal EV charging loads in different areas of the electrified transportation system of NY State. Correspondingly, the running parameters in the studied power system are updated by the alternative current (AC) power flow model. Finally, the risk for the power system coming from the transportation system scenarios is assessed within a probabilistic risk analysis framework. The proposed integrated risk assessment framework is able to model the propagation of the effects of scenarios in the transportation system onto the power system of NY State and quantify the consequences. A real test case is used to illustrate the proposed framework.

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.

International audience; In healthcare industry, companies like GEHC (General Electric Healthcare) buy back their products at the EOL (End of Life) phase and reuse the spare parts composing them. This process is referred to as spare parts harvesting. The harvested parts are included in the spare parts supply chain which presents specific characteristics like the availability of critical parts and the intermittent demand behavior. Add to that, the unpredictability of the parts' supply capacity from bought back systems is a challenge for healthcare companies. The focus of this paper is to provide an accurate forecasting method of the harvested parts supply capacity for GEHC. To achieve this objective, a comparative study is carried out between statistical forecasting models. Then, a forecasting process employing the most accurate models is provided using TSB-Croston, the 12-month moving average, the best ARIMA model chosen with the Box-Jenkins methodology, and an introduced business knowledge based model. In order to improve the designed method accuracy, the statistical models' forecast is adjusted using contextual information. An error measurement based on a modified MAPE error is introduced to evaluate the forecast. By means of the designed method, the monthly accuracy is improved by 9%.

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; Non-elective surgery patients are on the rise worldwide, generating mounting pressure on the already scarcely available resources (financial, human, and equipment). One of the main issues that has been identified in the literature is the high levels of unexpected occupancy of non-elective operating rooms (ORs) and the major scheduling problems generated by the lack of accuracy in the prediction of the surgery duration for emergency surgery patients. Therefore, accurately predicting surgery duration will positively impact healthcare performance metrics and allow hospitals to better schedule nonelective patients and manage ORs in a more efficient way. This paper aims to increase the efficiency and improve the patient flows in Emergency Operating Rooms (EOR) by predicting the surgery duration by applying Machine Learning (ML) algorithms. Using anonymized hospital data, we extract the patients' records admitted to EOR of La Pitié-Salpêtrière Hospital in Paris between January 2015 and December 2019. After a thorough data cleansing process, we implement and compare the accuracy of four standard machine learning algorithms: linear regression (LR), random forest (RF), support vector machine (SVM) for regression, and artificial neural networks (ANN). Machine Learning methods provide accurate predictions of surgery duration in La Pitié Salpêtrière Hospital. Particularly, ANN model is the most pertinent model for the available database; it is the most accurate for two specialties and gives good results for the other specialties. Then, it can be used in the optimization of EOR by reducing the length of stay (LOS) in the hospital.

International audience; This research aims to identify the current place of walking as a utilitarian active mode in MaaS solutions in the context of the French capital region. The appraisal of value creation mechanisms out of active modes represents a current challenge and a research gap to be filled to develop sustainable MaaS solutions and new business models. This study represents the first stage of a long-term research aiming to appraise the integration of active modes in MaaS solutions deployed in France. To do so, we analyze its context, characteristics, business model, and the current state of integration of pedestrian mobility they propose. A comparative analysis of the information provided for pedestrian itineraries was carried out to identify the type of information provided and the mode hierarchy in each MaaS solution. Firstly, we describe the territorial context, and the mobility dynamics present, including the modal share of active modes and a walkability index developed by the Institut Paris Region (2018). Then, we explore the institutional context of each of the entities having taken the role as MaaS operators. We identify and give an overview of their business models and policies, which directly impact the direction they decide to take with their MaaS solutions. Lastly, we start a discussion to synthesize the main findings and propose future directions for the following stages of this research. One of our research assumptions was that the mode hierarchy responds to the objectives and policies of the project carrier. This was found to be partially valid in the case of the studied MaaS solutions.This research can contribute to the scientific production emphasizing the role of MaaS solutions as catalyzers of sustainable modal shifts among specific users (Sarasini, Sochor and Arby, 2017; Pangbourne et al., 2020a). It also stresses the importance of incorporating better, high-quality information for active modes to enable enhanced intermodality and sustainable MaaS solutions.

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