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Budapest, 2002. október 30.. 1 Production networks László Monostori - György Kovács Computer and Automation Research Institute, Hungarian Academy of Sciences.

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Presentation on theme: "Budapest, 2002. október 30.. 1 Production networks László Monostori - György Kovács Computer and Automation Research Institute, Hungarian Academy of Sciences."— Presentation transcript:

1 Budapest, 2002. október 30.. 1 Production networks László Monostori - György Kovács Computer and Automation Research Institute, Hungarian Academy of Sciences & Department on Production Informatics, Management & Control, Budapest University of Technology and Economics

2 Budapest, 2002. október 30.. 2 Trends in manufacturing/1 beside mass and serial: one-of-a-kind production, mass customisation of products further integration within CIM (e.g. process planning, scheduling, feature based CNCs) time factor (concurrent engineering, rapid and virtual prototyping, simulation, virtual manufacturing) quality (monitoring, diagnostics, ultra precision machining, quality management) symbiosis of technical and business decisions (business process re-engineering, enterprise integration, management decision support systems)

3 Budapest, 2002. október 30.. 3 Trends in manufacturing/2 globalisation (world-wide distributed, co-operative production, production networks, logistics) sustainable development (green production, life-cycle engineering, assembly-disassembly, recycling) instead of unmanned factory: human factors (education, new organisation paradigms) miniaturisation (submicron- nanotechnologies, manufacturing, assembly) application of artificial intelligence techniques (intelligent manufacturing processes and systems)

4 Budapest, 2002. október 30.. 4 Concept of IMS „are expected to solve, within certain limits, unprecedented, unforeseen problems on the basis even of incomplete and imprecise information”, Hatvany, 1983 MSs with intelligent behaviour „to mobile international industry, government and research resources to drive the co-operative development and spread of manufacturing technologies and systems in a global environment of change ”, Yoshikawa, 1992. World-wide IMS- Programme

5 Budapest, 2002. október 30.. 5 Mission of the Manuf./Prod. Laboratories Research and elaboration of techniques applicable for handling complex production and business systems working in an uncertain, changing environment, with special emphasis on artificial intelligence and machine learning approaches

6 Budapest, 2002. október 30.. 6 Trends towards cooperation and networks

7 Budapest, 2002. október 30.. 7 From the functional factory towards PN

8 Budapest, 2002. október 30.. 8 SCOR (Supply Chain Organisation Reference Model)

9 Budapest, 2002. október 30.. 9 Classification of cooperation concepts

10 Budapest, 2002. október 30.. 10 Evaluation of competencies for partner choice

11 Budapest, 2002. október 30.. 11 New functions and tasks in the network

12 Budapest, 2002. október 30.. 12 Data exchange in production networks

13 Budapest, 2002. október 30.. 13 CPE: Cooperative Production Engineering

14 Budapest, 2002. október 30.. 14 PPC functions in production networks / 1

15 Budapest, 2002. október 30.. 15 PPC functions in production networks / 2

16 Budapest, 2002. október 30.. 16 Types of subcontracting in PNs

17 Budapest, 2002. október 30.. 17 Practical application of network control

18 Budapest, 2002. október 30.. 18 Dead-time-based continuous model of a WS

19 Budapest, 2002. október 30.. 19 Agent-based order management

20 Budapest, 2002. október 30.. 20 Communication in production networks

21 Budapest, 2002. október 30.. 21 Evolution of staff member responsibilities

22 Budapest, 2002. október 30.. 22 Agreements, rules and prerequisites for operating in a network

23 Budapest, 2002. október 30.. 23 Requirements on flexibility at different stages of manufacturing

24 Budapest, 2002. október 30.. 24 Characterisation of manufacturing enterprises: Changeability vs. networkability

25 Budapest, 2002. október 30.. 25 Alliance types in production networks

26 Budapest, 2002. október 30.. 26 Problems in production networks

27 Budapest, 2002. október 30.. 27 Digital enterprises, production networks

28 Budapest, 2002. október 30.. 28 The Digital Factory project in Hungary

29 Budapest, 2002. október 30.. 29 Telepresence and interactive multimedia International integration, globalisation Information for the management and for the customers in every phases of the production (design, manufacturing, final tests) Virtual customer witness and global virtual control room Multimedia data bases, sensor integration, data compression and decoding, high-speed and mobile data transmission, access rights and safety issues

30 Budapest, 2002. október 30.. 30 VCR-concept in GE- Hungary

31 Budapest, 2002. október 30.. 31 Wearable computer applications

32 Budapest, 2002. október 30.. 32 The Digital Factory project in Hungary

33 Budapest, 2002. október 30.. 33 Sensors for machine and process monitoring

34 Budapest, 2002. október 30.. 34 Monitoring of complex production structures 5-10000 patterns / second Information for control, maintenance and management purposes Search for hidden dependencies, model building, determination of decision criterion, decision support Decomposition of large models Integration with the other parts of the project

35 Budapest, 2002. október 30.. 35 Large-scale scheduling Problems –Long/medium term allocation of production resources –Detailed short term scheduling –Closing the gap between planning and execution Detailed, intuitive models Uncertainties Efficient solution techniques –Close-to-optimal solutions of very hard combinatorial optimization problems –Guaranteed response time Decision support –What-if analysis –Mixed-initiative, interactive problem solving

36 Budapest, 2002. október 30.. 36 R & D methodology Looking for an appropriate match between –Problem formulation –Available domain knowledge –Efficient utilization of this knowledge Methods applied up to now –Iterative development of detailed, verbal models –Building declarative models of resource allocation and scheduling problems –Simulation of subproblems –Efficient solution methods for generalized subproblems Integration of –Linear Programming (LP) –Constraint programming (CP) –Discrete event simulation

37 Budapest, 2002. október 30.. 37 Case 1: project scheduling Large-scale multi-project scheduling –Covering the life-cycle of one-of-a-kind products –Production planning (1 year) and scheduling (1 month) –Due date performance and minimal subcontracting –Uncertainty of planned projects Model: resource-constrained project scheduling –Demand Projects composed of tasks Time windows, precedences, overlaps Resource requirements Fixed vs. planned projects –Capacity Internal vs. external Time-varying (skyline)

38 Budapest, 2002. október 30.. 38 Project scheduling (2) Solution approach –Preemptive relaxation –Narrowing time windows of tasks as tight as possible (CP) –Building up and solving a minimal-size network flow model (LP) –Project plans put constraints on detailed scheduling (CP) Example

39 Budapest, 2002. október 30.. 39 Case 2: Factory scheduling Complex production technology –Machining, welding, assembly, test –Long paths, cycles in jobs –~200 machines, qualified workforce –Alternative resources Problems –Medium term assignment of resources (machines, workforce) –Short-term (re-) scheduling Uncertainties: quality of raw material, repair, rush order Solution approach –Medium-term scheduling as RCPS  LP solution –Detailed constraint-based scheduling problem  CP solution –Testing schedule robustness  simulation

40 Budapest, 2002. október 30.. 40 Core of both problems Resource-constrained project scheduling –Satisfying time and resource constraints –Minimizing external resource usage Elastic tasks –Time window –Amount of work, requiring several resources –Generalized precedence constraints between tasks Complexity: hopeless to solve optimally Solution approach –Preemptive relaxation –Building minimal LP model and solving it with cutting planes –Adjusting model and/or solutions by CP Development of a generic-purpose scheduler

41 Budapest, 2002. október 30.. 41 Factory simulation

42 Budapest, 2002. október 30.. 42 Optimization and simulation constraint- based optimization discrete event simulation plans, schedules evaluations demand resources activities optimization criteria actual state of the shop floor constraints uncertainties constraints

43 Budapest, 2002. október 30.. 43 Summary Up-to-date topics, which fit into the world-trends Favourable combination of the consortium partners for research, development, application and dissemination Occasional contradiction between short term goals and strategic thinking Administration burden Positive reaction: further interest Potential for further development (industrial partners, attractiveness for young people)

44 Budapest, 2002. október 30.. 44 Highlights of the present and the near future External University Department on Integrated Production Information Systems A number of international (IMS-GEM, IMS-NoE, MPA) and national projects The Digital Factory project Establishment of the University Department of Production Informatics, Management and Control Virtual Institute for Production and Business Management (IPA – SZTAKI) IFAC IMS Workshop, 2003; CIRP MS Seminar, 2004, Budapest

45 Budapest, 2002. október 30.. 45 Co-operation opportunities Intelligent manufacturing processes and systems Management of complexity, changes and disturbances in production Agent-based control structures Distributed manufacturing, Extended Enterprises Production networks, supply chain information systems The digital factory, factory planning and logistics management Quality management, modelling and optimisation of process chains Life cycle assessment Environmentally benign production Data mining

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