Un jumeau numérique pour l’aide à la décision dans un contexte de gestion portuaire avec incertitudes : Une approche à base de connaissances issue de l'apprentissage automatique

With global port traffic having quadrupled in 20 years from 200 million to 800 million containers, controlling the performance of sea freight is proving to be crucial for world trade but delicate. The port, or container terminal, is the basic unit of the global maritime freight network and the hub of interactions where the impact of uncertainties is accrued. The United Nations Conference on Trade and Development has highlighted the diversity of these uncertainties in 2020. The sooner the impact is quantified, the better the reaction. Thus, the work carried out aims at predicting as soon as possible the impact of hazards on the respect of initial objectives.A state of the art on port resource planning has shown the difficulty in formalizing the relationship between the duration of operations and uncertainties. Faced with these limitations, the developed approach based on knowledge engineering proposes first of all an approach to build a digital twin of a container terminal. This digital twin is then exploited to build a prediction model of LSTM time series. The first set of experiments shows that the proposed inference is applicable for learning and predicting port operations. The second set of experiments shows the use of a multi-step LSTM time series prediction model. With periodically renewed predictions, the operations manager of a container terminal will continuously visualize the evolution of the operations schedule, including possible deviations between the initially planned end date and the predicted one based on the actual data at the considered time.Avec un trafic portuaire mondial ayant été multiplié par quatre en 20 ans passant de 200 millions à 800 millions de conteneurs, la maitrise des performances du fret maritime se relève être cruciale pour le commerce mondial mais délicate. Le port, ou terminal conteneur, est l’unité de base du réseau mondial de fret maritime et le noeud des interactions où se cristallise l’impact des incertitudes dont la United Nations Conference on Trade and Development a mis en exergue la diversité en 2020. Plus tôt l’impact est quantifié, meilleure est la réaction. Ainsi, les travaux menés visent à prédire au plus tôt l’impact des aléas sur le respect des objectifs initiaux.Un état de l’art sur la planification des ressources portuaires a montré la difficulté à formaliser les relations entre la durée des opérations et les incertitudes. Face à ces limites, l’approche développée basée sur l’ingénierie des connaissances propose dans un premier temps une démarche de construction d’un jumeau numérique d’un terminal conteneur. Ce jumeau numérique est ensuite exploité afin de construire un modèle de prédiction de séries temporelles LSTM. La première série d'expériences montre que l'inférence proposée est applicable pour l'apprentissage et la prédiction des opérations portuaires. La deuxième série d'expériences montre l'utilisation d’un modèle de prévision de séries temporelles LSTM en plusieurs étapes. Avec des prédictions renouvelées périodiquement, le responsable d'exploitation d’un terminal conteneur visualisera en permanence l’évolution du planning des opérations, notamment les éventuels écarts entre la date de fin planifiée initialement et celle prédite en fonction des données réelles à l’instant considéré

Un jumeau numérique pour l’aide à la décision dans un contexte de gestion portuaire avec incertitudes : Une approche à base de connaissances issue de l'apprentissage automatique

With global port traffic having quadrupled in 20 years from 200 million to 800 million containers, controlling the performance of sea freight is proving to be crucial for world trade but delicate. The port, or container terminal, is the basic unit of the global maritime freight network and the hub of interactions where the impact of uncertainties is accrued. The United Nations Conference on Trade and Development has highlighted the diversity of these uncertainties in 2020. The sooner the impact is quantified, the better the reaction. Thus, the work carried out aims at predicting as soon as possible the impact of hazards on the respect of initial objectives.A state of the art on port resource planning has shown the difficulty in formalizing the relationship between the duration of operations and uncertainties. Faced with these limitations, the developed approach based on knowledge engineering proposes first of all an approach to build a digital twin of a container terminal. This digital twin is then exploited to build a prediction model of LSTM time series. The first set of experiments shows that the proposed inference is applicable for learning and predicting port operations. The second set of experiments shows the use of a multi-step LSTM time series prediction model. With periodically renewed predictions, the operations manager of a container terminal will continuously visualize the evolution of the operations schedule, including possible deviations between the initially planned end date and the predicted one based on the actual data at the considered time.Avec un trafic portuaire mondial ayant été multiplié par quatre en 20 ans passant de 200 millions à 800 millions de conteneurs, la maitrise des performances du fret maritime se relève être cruciale pour le commerce mondial mais délicate. Le port, ou terminal conteneur, est l’unité de base du réseau mondial de fret maritime et le noeud des interactions où se cristallise l’impact des incertitudes dont la United Nations Conference on Trade and Development a mis en exergue la diversité en 2020. Plus tôt l’impact est quantifié, meilleure est la réaction. Ainsi, les travaux menés visent à prédire au plus tôt l’impact des aléas sur le respect des objectifs initiaux.Un état de l’art sur la planification des ressources portuaires a montré la difficulté à formaliser les relations entre la durée des opérations et les incertitudes. Face à ces limites, l’approche développée basée sur l’ingénierie des connaissances propose dans un premier temps une démarche de construction d’un jumeau numérique d’un terminal conteneur. Ce jumeau numérique est ensuite exploité afin de construire un modèle de prédiction de séries temporelles LSTM. La première série d'expériences montre que l'inférence proposée est applicable pour l'apprentissage et la prédiction des opérations portuaires. La deuxième série d'expériences montre l'utilisation d’un modèle de prévision de séries temporelles LSTM en plusieurs étapes. Avec des prédictions renouvelées périodiquement, le responsable d'exploitation d’un terminal conteneur visualisera en permanence l’évolution du planning des opérations, notamment les éventuels écarts entre la date de fin planifiée initialement et celle prédite en fonction des données réelles à l’instant considéré

Uncertainty handling in containerized logistics: Unitary Traceability Object approach

International audienc

A Port Digital Twin Model for Operational Uncertainty Management

International audienceLacking information challenges the management of portoperational uncertainty in estimating the situation to support a decision onreactivity planning. This paper applies Digital Twin (DT) to model a replicatedvirtual port operation from a real-world port of Thailand. The proposed DTmodel offers a tool to accelerate generating data of the port operation withconfigurable uncertainty. The model is validated by using generated datafrom the DT model compared with the real-world data. The result shows thatthe DT model produces the same behaviour as the real-world system. Anoutcome of this paper is a DT model eligible to generate port operation datafor later application with machine learning to predict the port capacity underuncertainty to support reactivity planning

Improving risk management by using smart containers for real-time traceability

International audienceThis research proposes implications of application functions by using the chain traceability data acquired from the Smart Object attached with Extended Real-time Data (SO-ERD: e.g. smart container, smart pallet, etc.) to improve risk management at the level of the logistics chain. Recent applications using traceability data and major issues in traceability systems have been explored by an academic literature. Information is classified by the usage of current traceability data for supporting risk detection and decisions in operational, tactical, and strategical levels. It is found that real-time data has been a significant impact on the usage for the transportation activity in all decision levels such the function of food quality control and collaborative planning among partners. However, there are some uncertainties in the aggregation of event-based traceability data captured by various partners which are preventing the adoption of data usage for the chain. Under the environment of Industry 4.0 and the Internet of Things (IoT), the SO-ERD enables independent data tracing through the chain in real-time. Its data has potential to overcome current issues and improve the supply chain risk management. Therefore, Implications of risk management are proposed with the usage of SO-ERD data based on the literature review which reveals current concerns of decision functions in the supply chain. The implications can be an impact to the domain needs

Improvement of the Containerized Logistics Performance Using the Unitary Traceability of Smart Logistics Units

International audienceBased on the emergence of the Internet of Things, smart logistic units (container, pallet, cardboard) offers a new opportunity to improve the responsiveness to disturbances of the supply chain and to develop robust scheduling approach based on the knowledge extracted from the historical data of traceabil-ity on the smart logistic units. The limitations of the current traceability solutions are related in particular to the insufficient level of detail, the late availability of data and the scattering of data in databases of different actors in the supply chain who are reluctant to exchange them. Then, the unitary traceability based on the Internet of Things with a real-time tracking of multiple parameters of each object (position, temperature, vibration, humidity, etc.) is a solution which makes it possible to improve reactivity in real time when facing disturbances and to extract knowledge from historical data. Therefore, this paper proposes a conceptual framework based on seven activities that exploit smart container traceability data for real-time analysis and decision to monitor risks of disruptions and to mitigate the impact of disruptions