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Advanced predictive-analysis-based decision-support engine for logistics

Projektbeschreibung


Intelligent information management
Enabling strategic planning coupled with instant decision making to provide vision in a blizzard of data

Logistics networks accumulate OVER 1 BILLION new items of information per month (customer orders, pallet-vehicle movement, GPS data, postcodes, depot data, etc.), generated every minute of each day by thousands of pallets travelling on hundreds of trailers for more than one million customers under hundreds of thousands of postcodes, each with multiple different service requirements. Patterns and dependencies in 50 million or more data elements can only be analysed by intelligent data-mining approaches linked to strategic decision making based on longer term analyses of billions of pieces of information.

ADVANCE will develop an innovative predictive-analysis-based decision support platform for novel competitive strategies in logistics operations. It will provide a dual perspective on transport requirements and decision making dependent on the latest snapshot information and the best higher-level intelligence. Our software framework will be available open-source, as this way, low initial investment will encourage also smaller enterprises to exploit the solution. Also, previously unidentified needs will receive better coverage as development can proceed in close collaboration with the users.

Logistics networks accumulate OVER 1 BILLION new items of information per month (customer orders, pallet-vehicle movement, GPS data, postcodes, depot data, etc.), generated every minute of each day by thousands of pallets travelling on hundreds of trailers for more than one million customers under hundreds of thousands of postcodes, each with multiple different service requirements. Patterns and dependencies in 50 million or more data elements can only be analysed by intelligent data-mining approaches linked to strategic decision making based on longer term analyses of billions of pieces of information.ADVANCE will develop an innovative predictive-analysis-based decision support platform for novel competitive strategies in logistics operations.The ADVANCE software will have the capacity to both analyse massive data sets for long term planning, and rapidly process huge amounts of new data in real time. It will provide a dual perspective on transport requirements and decision making dependent on the latest snapshot information and the best higher-level intelligence.We will employ data mining, machine learning and optimisation techniques (heuristics, ant colony optimisation, evolutionary algorithms) to aggregate structured but locally confined data, and extract actionable information to improve local dispatching decisions (deadheading minimisation, early detection of missed due-dates, forecast of expected partnership modification, etc.). As a key to incorporating appropriate end-user perspectives and enabling users to interpret and assess automatically suggested decisions, ADVANCE will integrate human expertise (through cognitive modelling; Bayesian belief networks) with data mining algorithms and distributed data mining in particular.Industrial implementations will have a networked enterprise group as main piloting partner, involving three different operational and decision levels, and including multiple independent companies on the local distribution levels.

Wissenschaftliches Gebiet

Programm/Programme

Thema/Themen

Aufforderung zur Vorschlagseinreichung

FP7-ICT-2009-5
Andere Projekte für diesen Aufruf anzeigen

Finanzierungsplan

CP - Collaborative project (generic)

Koordinator

SZAMITASTECHNIKAI ES AUTOMATIZALASI KUTATOINTEZET
EU-Beitrag
€ 797 539,00
Adresse
KENDE UTCA 13-17
1111 Budapest
Ungarn

Auf der Karte ansehen
Region
Közép-Magyarország Budapest Budapest
Aktivitätstyp
Research Organisations
Kontakt Verwaltung
Elisabeth Ilie-Zudor (Dr.)
Links
Gesamtkosten
Keine Daten

Beteiligte (4)

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Letzte Aktualisierung: 16 Juli 2019

Mein Booklet

Permalink: https://cordis.europa.eu/project/id/257398/de

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