LOGISTAR LOGISTAR

LOGISTAR LOGISTAR

 : 

Enhanced data management techniques for real time logistics planning and scheduling Enhanced data management techniques for real time logistics planning and scheduling

A Project coordinated by IIIA.

Principal investigator:

Jordi Levy Jordi Levy

Team members:

Collaborating organisations:

Fundación Deusto, Spain University College Cork (UCC), National University of Ireland, Ireland Drustvo Za Konsalting (DUNAVNET) , Serbia Semantic Web Company (SWC), Austria Preston Solutions, UK MDS Transmodal Limited, UK Software AG (SAG), Germany DBH L

Fundación Deusto, Spain University College Cork (UCC), National University of Ireland, Ireland Drustvo Za Konsalting (DUNAVNET) , Serbia Semantic Web Company (SWC), Austria Preston Solutions, UK MDS Transmodal Limited, UK Software AG (SAG), Germany DBH L

Funding entity:

European Commision European Commision

Funding call:

Horizon 2020 Horizon 2020

Project #:

Grant Agreement No. 76914 Grant Agreement No. 76914

Funding amount:

4.997.548,00€ 4.997.548,00€

Duration:

2018-06-01 2018-06-01

 -

2021-05-31 2021-05-31

The main objective of the project is to allow effective planning and optimizing of transport operations in the supply chain by taking advantage of horizontal and vertical collaboration, relaying on the increasingly real time available data gathered from the interconnected environment. For this, a real-time decision making tool and a real-time visualization tool of freight transport will be developed, with the purpose of delivering information and services to the various agents involved in the supply chain, i.e. freight transport operators, their clients, industries and other stakeholders such as warehouse or infrastructure managers. The operative objectives will be: To analyse stakeholders’ needs incorporating them in the specification of the logistic services delivered. To identify logistic open data sources and harmonizing this data together with the other closed sources (i.e. data retrieved from IoT devices) to be used as real-time information To deploy and enhance artificial intelligence techniques to deliver prediction services and to learn the preferences of logistic chain participants, which will serve as an input for accurate planning of logistic operations. To leverage optimization techniques to transshipment planning and scheduling in hubs as well as freight transport network planning and scheduling to make the best use of the available resources. To apply machine learning techniques to identify and remedy potential incidents or events which could disrupt the supply chain, to take the relevant actions and needed reconfigurations ensuring a continuous, seamless flow of the operations. To make use of distributed constraint satisfaction techniques to allow the negotiation among different agents involved in the supply chain taking into account several constraints that arise in real-time. To develop specific services leveraging the aforementioned processing techniques and delivering 2 main results: Control and decision making tool for logistics operations capable of monitoring of goods through the whole logistics chain, allowing an integrated planning of resources a providing dynamic routing relying on synchromodality. Real time information on sea, rail and road freight transport will be delivered by means of a website positioning sea/rail and road cargo and communicating their arrival time. To elaborate a full exploitation plan for the results and define the disseminations actions for promotion.

The main objective of the project is to allow effective planning and optimizing of transport operations in the supply chain by taking advantage of horizontal and vertical collaboration, relaying on the increasingly real time available data gathered from the interconnected environment. For this, a real-time decision making tool and a real-time visualization tool of freight transport will be developed, with the purpose of delivering information and services to the various agents involved in the supply chain, i.e. freight transport operators, their clients, industries and other stakeholders such as warehouse or infrastructure managers. The operative objectives will be: To analyse stakeholders’ needs incorporating them in the specification of the logistic services delivered. To identify logistic open data sources and harmonizing this data together with the other closed sources (i.e. data retrieved from IoT devices) to be used as real-time information To deploy and enhance artificial intelligence techniques to deliver prediction services and to learn the preferences of logistic chain participants, which will serve as an input for accurate planning of logistic operations. To leverage optimization techniques to transshipment planning and scheduling in hubs as well as freight transport network planning and scheduling to make the best use of the available resources. To apply machine learning techniques to identify and remedy potential incidents or events which could disrupt the supply chain, to take the relevant actions and needed reconfigurations ensuring a continuous, seamless flow of the operations. To make use of distributed constraint satisfaction techniques to allow the negotiation among different agents involved in the supply chain taking into account several constraints that arise in real-time. To develop specific services leveraging the aforementioned processing techniques and delivering 2 main results: Control and decision making tool for logistics operations capable of monitoring of goods through the whole logistics chain, allowing an integrated planning of resources a providing dynamic routing relying on synchromodality. Real time information on sea, rail and road freight transport will be delivered by means of a website positioning sea/rail and road cargo and communicating their arrival time. To elaborate a full exploitation plan for the results and define the disseminations actions for promotion.

2019
Carlos Ansótegui and Maria Luisa Bonet and Jesús Giráldez-Cru and Jordi Levy and Laurent Simon; Community Structure in Industrial SAT Instances. Carlos Ansótegui and Maria Luisa Bonet and Jesús Giráldez-Cru and Jordi Levy and Laurent Simon; 2019.  [BibTeX]
Carlos Ans{\'{o}}tegui and Maria Luisa Bonet and Jordi Levy; Phase Transition in Realistic Random {SAT}Models. 2019.  [BibTeX]
2018
Alexander Baumgartner and Temur Kutsia and Jordi Levy and Mateu Villaret; Term-Graph Anti-Unification. 2018.  [BibTeX]