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Future of DELS Research and Practice Dirk C. Mattfeld TU Braunschweig

Future of DELS Research and Practice Dirk C. Mattfeld TU Braunschweig. CIM Integration. Engineering. Business Admin. Capacity based continuous static deterministic Event based discrete dynamic stochastic. Planning. Control. Need for Integration.

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Future of DELS Research and Practice Dirk C. Mattfeld TU Braunschweig

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  1. Future of DELSResearch and PracticeDirk C. MattfeldTU Braunschweig

  2. CIM Integration Engineering Business Admin • Capacitybased • continuous • static • deterministic • Event based • discrete • dynamic • stochastic Planning Control

  3. Need for Integration • Bottleneck resources, e.g. wafer scheduling • Stochastic environment, e.g. ad-hoc supply chains • Pricy products, e.g. aerospace spare parts • Missing process definition, e.g. micro-production • Explicit customer commitments, e.g. courier services • Complex constraints, linked activities  dependent events • Process variation, human interaction  stochastic env.

  4. Requirements of the Computational Model • An event based decision determines future states • State transitions are important features in DELS • Important features should go into the model • Dynamic optimization models are required • Events also introduce probabilities of occurence • Stochastic optimization is desired as well • Dynamic stochastic optimization is desired

  5. Courier, Express and Parcel Services • Less-than-truckloadtransports • Stochasticcustomerdemands • Online vehiclerouting • Diversification of products • For instance UPS: Standard, Express, Express Plus • Constraint: • Keep customer service commitment • Objective: • Maximize number of customers visited

  6. DetailedToy Problem • Description • Dynamicvehiclerouting • One vehicle in oneperiod • Givendepots • Knowncustomerrequests • Stochasticcustomerrequests • Decisions • Sequence of visits • Acceptance of customerrequest • Conditionalwait for incomingrequests

  7. ApproximateDynamic Programming • Size of problem depends on • Number of decisions • Number of stoch. events • Number of periods • Usually DP does not work for DELS • Approximate DP • Restricts the state space • Plans on a rolling horizon • Uses fast heuristics for decisions • Simulates subsequent trajectories • Learns the expected reward from simulation

  8. Learning the Expected Value of Future States

  9. Comparisonwith COG BasedHeuristics

  10. Problems vs. Methods Dependent Stochastic Challenge Simulation ? Heuristics ? ADP Negotiation ? Problem Meta-heuristics Rounding Trivial MILP Independent Deterministic LP Operations Control Strategic Planning Support

  11. Implications • Only specific areas of application require DELS support • There is a need for integrated planning and control • Forecasts are to be part of the optimization procedure • Optimization, simulation and learning are ingredients • ADP provides a footing for optimizing DELS • Suitable optimization support still is a grand challenge

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