2. About Autonomous Logistics
• Autonomy with each entity
• Robust, flexible and quick:
Decentralized control
Independently manage and process information
Receive and transmit data
Run decisions and initial actions remotely.
Autonomous Logistics
Product and
processes
Connectivity
Sensors
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3. Process of implementation
• Run simulation
• PLASMA
• Track unwanted behavior
• Smooth running system
• Identify errors
• Rectify process
• Choose the best configuration
• Final model
• Define objectives
• Define all the controls• Identify existing elements and
relationships
• Represented using agent based systems
• Tools like ALEM assist
• Autonomy in decision making
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4. Autonomous Control Modeling Techniques
Different algorithms
have shown similar
results independent
of size of production
network
Autonomous control modelling techniques attempts to implement
capability in a system, process or an item to design its input-,
throughput- and output-profiles as an anticipative or reactive
answer to changing constraints of environmental parameters.
Ant Colony Control
• Implemented on shop floor
• Different jobs with pheromones
• Machines develop pheromone concentrations
• Pheromones expire over time
Market Based
• Each part carries list of required operations
• Cost assigned to distance travelled
• Budget specified to each par
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5. Autonomous Logistics Engineering Methodology
(ALEM)
Models a system by defining specifications of Autonomous Logistics
Identifies, Designs & Allocates Decision Processes
Components of ALEM :
ALEM Notation
ALEM Procedure
ALEM Tools
Gives the notational elements to be
used in views (to show specific aspects
of the logistics system)
A procedure model which acts as a
guideline for modelling autonomous
logistic processes
Software tool to aid a logistics engineer
to model and designing automation
logistics processes
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7. Agent Components
• Sensors – quality of shipments
• RFID – stores information in label
• Standard Protocols:
Unanimous communication
EPC global architecture
• PLASMA Simulation
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8. Semantic Mediator
• Smart data detection & integration
• Serves as “GO” between diff agents
• Translates message, filters information
Cloud Computing
• Decentralized decision making
• Software agents on: Embedded systems Or Central server
• Types :
1) Infrastructure as service
2) Platform as service – JADE, PlaSMA
3) Software as service
4) Process as service
• Advantage ?
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9. Product Variation
• Product decides
Operation Order
Machine
Product variant
Customer
Collaborative Transport Planning (CTP)
• Exchange of customer Requests
• Create optimal routes
• Everyone’s benefit
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10. Smart Vehicles and DLRP
• Use of automated guided vehicles
Automated systems
Capable of all operations
Integrate with model
Speedy and eliminate traffic problems
• Distributed Logistics Routing Protocol
Decentralized
Node to node configuration
Best available route is chosen
Helps avoid Delays and errors
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11. Benefits
• Quick decisions and responses
• Automates the whole process
• Minimal human intervention (less errors)
• High efficiency
• Quality of product
• Order traceability
• Optimizes and chooses the best path
possible
• Enables collaboration between enterprises
to get higher profit
• Detailed representation of logistics system
• Improved communication and inter-
objects interaction
Challenges
• Any anomaly or change needs in depth
examination
• Prolonged Simulation time
• Enterprises with different strategies lead
to differences in model
• Computational power
• Interaction complexity
• Power constraints
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12. REFERENCES
• Autonomous logistic processes– new demands and first approaches – b. Scholz-reiter (2), k. Windt, m. Freitag (2003)
• 2. An agent-based approach to autonomous logistic processes collaborative research centre 637: autonomous cooperating logistic
processes - jan d. Gehrke · otthein herzog · hagen langer · rainer malaka · robert porzel · tobias warden (june 2010)
• 3. A classification pattern for autonomous control methods in logistics - katja windt • till becker • oliver jeken • achim gelessus (june
2010)
• 4. Applying autonomous sensor systems in logistics—combining sensor networks, rfids and software agents - reiner jedermann ,
christian behrens, detmar westphal, walter langa – (march 2006)
• 5. The autonomous logistics engineering methodology (alem)- bernd scholz-reiter, jan kolditz, torsten hildebrandt- (september 2007)
• 6. Cloud computing for autonomous control in logistics - a. Schuldt, k. A. Hribernik, j. D. Gehrke, k.-d. Thoben, and o. Herzog –
(october 2010)
• 7. Autonomous control methods in logistics – a mathematical perspective - sergey dashkovskiy, michael görges, lars naujok – (july
2012)
• 8. Limitations in modeling autonomous logistic processes - bernd scholz-reiter, daniel rippel, steffen sowade (june 2011)
• 9. An internet of things for transport logistics - an approach to connecting the information and material flows in autonomous
cooperating logistics processes - hribernik karl a, warden tobias, thoben klaus-dieter, herzog otthein-(2010)
• 10. Analysis of mobile agents considering the fan out - mobile agents for autonomous logistics - markus becker, gulshanara sayyed,
bernd-ludwig wenning, carmelita g¨org - (2006)
• http://www.uni-bremen.de/en.html - BIBA-Bremer Institut für Produktion und Logistik GmbH
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THANK YOU
