Planning Issues in Intelligent Manufacturing Systems

1. Planning Issues in Intelligent Manufacturing Systems Paul Valckenaers K.U.Leuven – PMA Belgium Valckenaersp@acm.org

2. Overview • Background information • Planning tasks in manufacturing • Facilities design, process planning, manufacturing control • Intelligent manufacturing systems • Specific research developments • Emergent manufacturing control and stigmergy • Planning tasks in emergent control • Other issues and conclusions

3. Background • Engineering @ K.U.Leuven • Computer Science (S/W & Appl. Maths) • PhD in mechanical engineering (production automation) • Projects: HMS, Mascada, Magecc, MPA … • Agent-based manufacturing control • Design of emergent systems • Integration issues • Critical mass for s/w components

4. Planning in Manufacturing (1) • Facilities (re)design • Requirements (capturing of) • Building blocks (modular plants) • Guarantee safety, cleanness… • Re-usability • Design of manufacturing plants • Adapt to changes, to new locations… • Assess performance of design alternatives • Predicting performance of a design is hard • Hinders the application of planning technology

5. Planning in Manufacturing (2) • Process planning • Recipes • Avoiding resource allocation choices ? • Information representation • Lazy, on-demand • Dialogues • Validation issues • Lock-in issues • Technology & product specifics • Grounding issues

6. Planning in Manufacturing (3) • Manufacturing control • Internal plant logistics • Routing, resource allocation, starting of processes • Detailed scheduling & schedule execution • Objectives of manufacturing control • Going concerns • No objective function ? • Productivity (throughput in money)first • Smooth and effectiveflows// chess analogy • Lead time, wip, due dates second • Not all tasks are equal

7. Product agents Resource agents Order agents Staff agents Architecture Basic: P – R – O Basic agents Responsible Reflect what is/exists Integration Staff agents Know-how, legacy Support, advise OO Specialisation Aggregation Intelligent Manufacturing Systems

8. Concerns in Manufacturing Control • Feasibility • Recipes, deadlock... • Thrashing, starvation... • Operations • Load balancing, lead times, batching... • Staff • Initial solutions, guidance...

9. Tasks in Manufacturing Control • Providing information (reflection) • Feasibility, operational, staff • Decision taking (fragile) • Uses (and respects) the information • Adaptable without causing software maintenance cascading to the information providing parts • Issue: stableagents (s/w components) • Challenge: global coordination (= system-wide) • Stigmergy: ants provide the inspiration

10. Stigmergy and Ants • Stigmergy • Indirect interaction • Signs in the environment • Lightweight compared to direct negotiation • Ants foraging for food • Simple rules, emergent coordinated behaviour • Global information is locally available • Environment is part of the solution

11. Emergent Load Forecasting • Resource agents reflect resources • Environment is part of the solution • Full life cycle, topology, ... • Locations have attached information spaces • What-if mode • Ex. Conveyor belt agent • Attributes, observers... • Modifiers/actions • What-if services

12. Emergent Load Forecasting • Attributes/observers • Information spaces attached to ... • Modifiers/actions • Life cycle • Create, delete, connect, disconnect... • Usage • Start, stop... • Synchronisation with reality

13. Emergent Load Forecasting • What-if services • PropagateUpstream • PropagateDownstream • Adapt time information • Register intentions • GiveLoadForecast • Based on intentions communicated by ...

14. Emergent Load Forecasting • Order agents create mobile agents that virtually move across the resources • Mobile agents behave as if the present time is their estimated arrival time • Mobile agents have the resource agents forecast their travel times • Mobile agents use the content of the info spaces • Mobile agents reflect decision mechanism of the order agent (without making assumptions about it)

15. Emergent Load Forecasting • Mobile agent inform resource agents about the order agent’s intentions • Resource agents combine intentions into a load forecast Order agents and their mobile agents have a tendency to stick to declared intentions When visiting a processing unit, the product agent and resource agent predict processing time and ... Mobile agents may send back an estimated arrival times (= lead time estimations)

16. Emergent Load Forecasting • Critical resource agents (processing units)propagate load forecasts upstream • This information is copied into the information spaces attached to the resource agents (typically attached to exits, entries...) • Upstream propagation adapts the forecasts to account for transportation time, ... • Evaporation & refresh

17. Emergent Load Forecasting

18. Lead time & Throughput Feasibility Flow balancing & In-flow control (Spare capacity) Products & travellers Factories & roads Right product & Proper destinations Heavy traffic & Loaded factories Planning in Emergent Control

19. Grouping Mutual influencing Transactions Heat treatment & Airplane load Exit blocking & Aircraft unloading Do it properly Or not at all Planning in Emergent Control

20. Forecasting Individual Intensions Emergent On-line Nervousness control Bottle-necks (The goal) Prevent problems from happening, both in factories and in traffic Handle problems on detailed customer needs Make forecasts possible Production capacity Road capacity Planning in Emergent Control

21. Non-functional Requirements • Emergent system design to cope with complexity and dynamics • Limited exposure to survive • Critical mass relative to artefact complexity

22. Concluding Remarks • System engineering properties • Stigmergic design • Limited exposure, emergence • Global information locally available • Evolving systems • Actual control decisions come last • Multi-level planning issues • Staff option