A Multi-Agent System Architecture for Coordination of Just-In-Time Production and Distribution

1. A Multi-Agent System Architecture for Coordination of Just-In-TimeProduction and Distribution Paul Davidsson and Fredrik Wernstedt Department of Software Engineering and Computer Science Blekinge Institute of Technology SWEDEN

2. Overview • Characterization of problem • MAS architecture • Case study: District Heating Systems • Simulation experiments • Conclusions

3. The Problem: Just-In-Time Production and Distribution • A set of producers of resources (P1,…, Pn) • A set of consumers of resources (C1,…, Cm) C2 C1 C3 P1 C10 P2 C8 P3 C4 C9 C6 C7 C5

4. The Problem: Just-In-Time Production and Distribution C2 C1 C3 P1 C10 P2 C8 P3 C4 C9 C6 C7 C5 • We can control how much resources are produced • We cannot control the demands of the consumers • We do not know future consumer demands • We can monitor the actual consumption

5. The Problem: Just-In-Time Production and Distribution C2 C1 DT C3 P1 C10 P2 C8 P3 C4 C9 C6 C7 C5 • The Production time (PT) and/or the Distribution time (DT) is relatively long • Resources must be consumed relatively soon • limited storage capacity, or • quality of resources degrades quickly, etc

6. The Problem: Just-In-Time Production and Distribution C2 C1 C3 P1 C10 P2 C8 P3 C4 C9 C6 C7 C5 • It is possible to redistribute resources between consumers that are close in proximity relatively cheap and fast

7. The Problem: Just-In-Time Production and Distribution C2 C1 C3 P1 C10 P2 C8 P3 C4 C9 C6 C7 C5 • There is a single “owner” of the producers, i.e., no competition between the producers • There is a long term “contract” between producers and consumers (about the prize of resources etc.)

8. The Problem: Just-In-Time Production and Distribution C2 C1 C3 P1 C10 P2 C8 P3 C4 C9 C6 C7 C5 • Examples: • car production (the retailers are the consumers) • iron and steel production • district heating

9. The Problem: Just-In-Time Production and Distribution C2 C1 C3 P1 C10 P2 C8 P3 C4 C9 C6 C7 C5 • Sub-problem 1: produce the right amount of resources at the right time • Sub-problem 2: distribute these resources to the right consumers

10. The Problem: Just-In-Time Production and Distribution C2 C1 C3 P1 C10 P2 C8 P3 C4 C9 C6 C7 C5 • Conflicting goals! • Produce as little resources as possible • Satisfy the demands of all consumers

11. Solution: Just-In-TimeProduction and Distribution C2 C1 C3 P1 C10 P2 C8 P3 C4 C9 C6 C7 C5 • Increase the knowledge about the current and the future states of the system (i.e., a decision support system at the producer side) • Redistribution of resources between consumers

12. MAS architecture Producer agent Redistribution agents Consumer agents • Each consumer has a consumer agent • Consumers that are ”close” forms a cluster and each cluster has a redistribution agent • One producer agent (interacts with all plants)

13. MAS architecture Consumer agents • Make predictions of future demands • Monitor the actual consumption • Communicate this to the redistribution agent • Perform received redistribution instructions

14. MAS architecture Redistribution agents • Make predictions for the whole cluster • Monitor the actual consumption of the cluster • Communicate this to the producer agent • Compute and send redistribution instructions

15. MAS architecture Producer agent • Interacts with production operators • Compiles predictions for the whole system • Compiles consumption for the whole system • Informs redistributor if demands cannot be met

16. Case study: District heating • Production plants heat water (cheaply) • Distribute hot water to consumer substations • Substations exchange heat to secondary flows within buildings (both radiator and tap water) • Cold water is returned to plant in separate pipes • Long distribution time, up to 24 hours!

17. Substation Outdoor temperature sensor Control unit Hot water, in Hot tap water Radiator water Return water Cold water • New type of substation is being developed by Cetetherm that programmable and supports two-way communication

18. 1200 1000 800 600 400 200 0 0 6 12 18 24 Example: the total consumption in a network serving 500 households Total consumption [kW] Time [h]

19. P R C C C C C C R Multi-Agent System • Redistribution is done by issuing “restrictions” (upper limits for consumption) • Tap water has higher priority than radiator water

20. P R C C C C C C R Multi-Agent System • Predictions are made for each 10 min interval • Each C computes the average consumption for the corresponding interval over the last 5 days

21. CG CG CG CG CG CG R C C C C C P R C Simulator SIMULATOR MAS PG • Consumption generated using a statistical model • Both MAS and simulator implemented in JADE

22. Experiment I: Quality of Service vs. Surplus production 140 120 100 radiator water 80 Number of Restrictions (one minute at one substation) 60 40 tap water 20 0 5 0 1 2 3 4 Surplus production (%) • Cluster has 10 substations (5*40 and 5*60 households) • Reference: 7% surplus needed to get 0 restrictions

23. Experiment II: Quality of Service vs. Size of cluster 300 250 200 Number of restrictions 150 100 50 0 2 4 8 16 Cluster size • Note: the cluster size is often limited by factors beyond our control, e.g., proximity of consumers

24. Conclusions • Suggested MAS approach makes it possible to control the trade-off between Quality of Service and the degree of surplus production • Possible to reduce the amount of production while maintaining the same Quality of Service • The larger the cluster size, the higher is the Quality of Service that can be achieved • However, cluster size is often limited by factors beyond our control, e.g., proximity of consumers

25. Future work • Improve the prediction mechanism • Improve the simulation environment • Extend experiments to several producers • Perform actual field tests • Evaluate the generality of the result in other just-in-time domains • Test other restriction policies than fairness, e.g., based on priorities between consumers

26. Software architecture • Different approaches possible • centralized, semi-distributed and distributed • agent-based and traditional approaches • We have chosen a semi-distributed agent-based approach…

27. Why a semi-distributed approach? • District heating systems are distributed per se • at least sensor reading and heat exchanger control must be distributed • Possible to centralize all computation, but • communication bottleneck at the central computer • computational bottleneck at the central computer (e.g., for computing the forecasts) • complex (many different types of substations etc) • private information should be kept locally • Possible to distribute all computation, but • increase the number of messages sent

28. Why an agent-based approach? • District heating systems have all the character-istics of the ”perfect” agent application [Parunak]: • modular • decentralized • changeble • ill-structured • complex • More general arguments include increased: • robustness, efficiency, flexibilty, openness, scalability, and economy

29. future restriction consumption consumption redistribute redistribute Interaction protocol Consumer Producer Consumer agent Redistributionagent Produceragent total predicted demand predicted cluster demand predicted demand t0-(TP+TD) production production future restriction t0-TD consumption t0+1 cluster consumption total consumtion redistribute t0+2 consumption t0+n cluster consumption total consumtion redistribute TP = production time TD = distribution time t0 = the start time of the actual consumption interval during each “prediction interval” the consumption is reported n times

30. Reference production 1200 1000 800 600 consumption [kW] 400 200 0 0 4 8 12 16 20 24 time [h] • 7% surplus production needed to get 0 restrictions