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.

Web page:

Principal investigator:

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), G...

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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

Funding call URL:

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

Extension date:

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.

2020
Maria Luisa Bonet,  & Jordi Levy (2020). Equivalence Between Systems Stronger Than Resolution. Proc. of the 23rd Int. Conf. on Theory and Applications of Satisfiability Testing, SAT'20, Alghero, Italy, July 3-10, 2020 (pp. 166--181). https://doi.org/10.1007/978-3-030-51825-7\_13. [BibTeX]  [PDF]
Thomas Bläsius,  Tobias Friedrich,  Andreas Göbel,  Jordi Levy,  & Ralf Rothenberger (2020). The Impact of Heterogeneity and Geometry on the Proof Complexity of Random Satisfiability. CoRR, abs/2004.07319. https://doi.org/https://arxiv.org/abs/2004.07319. [BibTeX]  [PDF]
2019
Carlos Ansótegui,  Maria Luisa Bonet,  Jesús Giráldez-Cru,  Jordi Levy,  & Laurent Simon (2019). Community Structure in Industrial SAT Instances. J. Artif. Intell. Res., 66, 443--472. https://doi.org/10.1613/jair.1.11741. [BibTeX]  [PDF]
Carlos Ansótegui,  Maria Luisa Bonet,  & Jordi Levy (2019). Phase Transition in Realistic Random {SAT}Models. Jordi Sabater{-}Mir, Vicenç Torra, Isabel Aguiló, & Manuel González Hidalgo (Eds.), Artificial Intelligence Research and Development - Proceedings of the 22nd International Conference of the Catalan Association for Artificial Intelligence, {CCIA}2019, Mallorca, Spain, 23-25 October 2019 (pp. 213--222). {IOS}Press. https://doi.org/10.3233/FAIA190126. [BibTeX]  [PDF]
2018
Alexander Baumgartner,  Temur Kutsia,  Jordi Levy,  & Mateu Villaret (2018). Term-Graph Anti-Unification. H{\\'{e}}l{\\`{e}}ne Kirchner (Eds.), 3rd International Conference on Formal Structures for Computation and Deduction, {FSCD}2018, July 9-12, 2018, Oxford, {UK} (pp. 9:1--9:17). Schloss Dagstuhl - Leibniz-Zentrum f{\"{u}}r Informatik. https://doi.org/10.4230/LIPIcs.FSCD.2018.9. [BibTeX]  [PDF]
Carles Sierra
Research Professor
Phone Ext. 231
Christian Blum
Scientific Researcher
Phone Ext. 214
Dave de Jonge
Contract Researcher
Phone Ext. 262
Felip Manyà
Tenured Scientist
Phone Ext. 248
Filippo Bistaffa
Contract Researcher
Phone Ext. 209
Jordi Levy
Tenured Scientist
Phone Ext. 240
Juan A. Rodríguez-Aguilar
Research Professor
Phone Ext. 218
Pedro Meseguer
Scientific Researcher
Phone Ext. 237