Enhanced data management techniques for real time logistics planning - - PowerPoint PPT Presentation

Enhanced data management techniques for real time logistics planning and scheduling

Andrew Palmer

5th International SRF Workshop, Cambridge 30th November 2018

About LOGISTAR

• A consortium of 15 partners from across Europe,

coordinated by the University of Deusto (Spain)

• Overall budget: 4.997.548,75 €
• Duration: 36 months (Started June 2018)
• Project funded by H2020:
• Work programme: Smart, green and integrated

transport

• Call: MG-5.2-2017: Innovative ICT solutions for

future logistics operations

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LOGISTAR

• verall concept
• The LOGISTAR system will include horizontal and

vertical collaboration combined with real time data to make planning and optimising of transport

• perations more effective
• A real-time decision making and visualization tool of

freight transport will be developed to deliver information and services to the various agents involved in the supply chain

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Other key innovation aspects

• Artificial Intelligence focused on prediction
• Inference based on event detection and probabilistic programming frameworks
• Optimisation planning
• Realistic optimisation models based on robust and multi-objective optimisation.
• Hybrid metaheuristics based on paradigms of parallel computing
• Automated negotiation and planning re-optimization
• Constraint satisfaction problem solving techniques
• Event Identification Rules
• A new application domain for the processing of complex events and their

aggregation

• Service layer – Decision making tool
• Increased data gathering, cleansing and structuring
• Data gathering techniques
• ETL tools for linked data. Scraping and transforming

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Partners and roles

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Project Coordinator Global optimization planning techniques Implementation and integration of services Artificial Intelligence techniques focused on prediction Geo-special oriented software solutions Automated negotiation algorithms Testing and validation – Real time co- loading in chemical industries use case Cloud IoT data Testing and validation – Synchromodality use case Dissemination activities Data gathering and harmonization Testing and validation – Backhauling and co-loading use case Functional requirements and end- users engagement Testing and validation – Backhauling and co-loading use case New and emerging business models assessment Testing and validation – Synchromodality use case Predictive analysis and processing of real-time data

Work packages structure

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Living labs

LOGISTAR services will be tested in an operational environment in three use cases

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Real time backhauling in the FMCG sector

Process of various information coming from the different companies (schedules, resources, constraints, truck, positions, empty return legs…) to improve backhaul management Overview of the status of the

• perations through the real-time

dashboards and the real-time information on road transport system.

Synchromodality

Real time re-planning due to disrupting events: corrective and preventive Planning of synchromodal routes based on real time events. Dynamic assigning of freight transport networks. Real time status on goods movements: position of vehicles, arrival time of cargo fleets.

Real time co-loading in the chemicals sector

Real time planning of resources looking for transport synergy and bundling opportunities. Real-time alerts and recommendations to take action, facilitating the decision-making process.

List of companies interviewed

www.logistar-project.eu 8

21 companies interviewed

The TMS process

www.logistar-project.eu 9

Load building Vehicle scheduling

Owned vehicles Yes Yes Yes Yes Yes Contracted carriers - dedicated Yes Yes Yes Yes Yes Contracted carriers - non dedicated Yes No No No Yes Ad hoc carriers Yes No No No Yes

Transport planning Order processing Delivery execution POD/CMR Shipper or LSP

• perations for:

Logistar overview

Dashboard information

Cloud based system

Project Project coordination coordination : enrique.onieva@deusto.es

Contact details

LOGISTAR project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769142.

www.logistar-project.eu