IOT & Autonomous Logistics
- 1. Autonomous Logistics TEAM 5: Akash Yadav Ankit Thakur Sayantan Biswas Thomas George 1
- 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 2
- 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 3
- 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 4
- 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 5
- 6. Agent Based approach Customer order Order management agent Inventory management agent Shipping agent Broker Agent Transport agent 6
- 7. Agent Components • Sensors – quality of shipments • RFID – stores information in label • Standard Protocols: Unanimous communication EPC global architecture • PLASMA Simulation 7
- 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 ? 8
- 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 9
- 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 10
- 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 11
- 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 12 THANK YOU