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Thomas Sjöland , Per Kreuger, Emil Åström, Per Danielsson COL/SICS

Coordination of Scheduling and Allocation Agents (extended abstract). Thomas Sjöland , Per Kreuger, Emil Åström, Per Danielsson COL/SICS. Complex Operations Laboratory (COL). - focus on applications - scheduling and planning in transportation and semi-continuous production.

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Thomas Sjöland , Per Kreuger, Emil Åström, Per Danielsson COL/SICS

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  1. Coordination of Scheduling and Allocation Agents (extended abstract) Thomas Sjöland, Per Kreuger, Emil Åström, Per DanielssonCOL/SICS

  2. Complex Operations Laboratory (COL) - focus on applications - scheduling and planning in transportation and semi-continuous production. - interpretation and modeling of data with complex intrinsic properties - forecasting transportation costs

  3. Railway Application TUFF-3 • Support tool for coordination of planners in goods transportation • mOzArt, distributed Oz system • Finite Domain constraints • Agent model • Spaces modeled with FIPA (agent-protocol)

  4. Rail Network: Sweden

  5. Schedule

  6. Agent Model Sales dept. Engine rostering Staff rostering Plan mgr. Track scheduling

  7. Roster (one engine type)

  8. Roster (three engine types)

  9. CCP in coordination? • CCP provides very natural synchronization Reasoning with separate constraint solvers But: - same domain required

  10. Role for tCCP? Reasoning with time on ”metalevel” - combining simulation and scheduling Might be useful in planning with sliding time-window

  11. SICStus Prolog global constraints agent platform (distributed) Oz /mOzArt Oz for networked programs: mOzArt Finite domain constraint solvers CLP/CCP systems from SICS Can a timed approach be integrated?

  12. Distributed OZ: mOzArt • Transparent distribution • mobile objects (and code) • http objects • distributed unification • distributed garbage collection • sites connect and disconnect dynamically

  13. Abstract Constraint Programming • Translation with abstraction • Formally motivated abstract models • More efficient search • complemented with concrete search t

  14. Suggested abstractions • fewer timepoints in the intervals described by the finite domain variables (granularity) • simpler network - only nodes with significant operations such as overturns, meetings and reallocation of engines or staff

  15. Conclusions • Constraints and distribution are enabling technologies in our application project • Explicit coordination requires protocols • Abstract constraint solving maybe useful • Perhaps tCCP can be useful to bridge gap between simulation <-> planning

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