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Optimization Techniques for Supply Network Models Simone Göttlich Joint Work with: M. Herty, A. Klar (TU Kaiserslautern) M. Herty, A. Klar (TU Kaiserslautern)

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Presentation on theme: "Optimization Techniques for Supply Network Models Simone Göttlich Joint Work with: M. Herty, A. Klar (TU Kaiserslautern) M. Herty, A. Klar (TU Kaiserslautern)"— Presentation transcript:

1 Optimization Techniques for Supply Network Models Simone Göttlich Joint Work with: M. Herty, A. Klar (TU Kaiserslautern) M. Herty, A. Klar (TU Kaiserslautern) A. Fügenschuh, A. Martin (TU Darmstadt) Workshop Math. Modelle in der Transport- und Produktionslogistik Bremen, 11. Januar 2008

2 Simone Göttlich Motivation Investment costs and rentability Topology of the network Production mix and policy strategies Simulation and Optimization of a Production Network Typical questions:

3 Simone Göttlich Network - Model ExistenceExtensions Optimization ContinuousDiscrete Modeling

4 Simone Göttlich Main Assumptions No goods are gained or lost during the production process. The production process is dynamic. The output of one supplier is fed into the next supplier. Each supplier has fixed features. MODEL

5 Simone Göttlich Network Model Idea:  Each processor is described by one arc  Describe dynamics inside the processor  Add equations for queues in front of the processor queues Fixed parameters

6 Simone Göttlich Network Model See Göttlich, Herty, Klar (2005): Network models for supply chains A production network is a finite directed graph (E,V) where each arc corresponds to a processor on the intervall Each processor has an associated queue in front. Definition Processor: Queue:

7 Simone Göttlich We define time-dependent distribution rates for each vertex with multiple outgoing arcs. The functions are required to satisfy and Network Coupling will be obtained as solutions of the optimization problem Example: Inflow into queue of arc 2 Inflow into queue of arc 3 Definition

8 Simone Göttlich Network - Model ExistenceExtensions Optimization ContinuousDiscrete Modeling

9 Simone Göttlich Optimization Problem Cost functional Constraints Positive weightsControls Processor Queue Initial conditions

10 Simone Göttlich Solution Techniques Mixed Integer Programming (MIP) Adjoint Calculus Optimal control problem with PDE/ODE as constraints! How to solve ? MIP LP

11 Simone Göttlich Network - Model ExistenceExtensions Optimization ContinuousDiscrete Modeling

12 Simone Göttlich Mixed Integer Program (MIP) A mixed-integer program is the minimization/maximization of a linear function subject to linear constraints. Problem: Modeling of the queue-outflow in a discete framework See Fügenschuh, Göttlich, Herty, Klar, Martin (2006): A Discrete Optimization Approach to Large Scale Networks based on PDEs 1 See Armbruster et al. (2006): Autonomous Control of Production Networks using a Pheromone Approach Relaxed queue-outflow 1 Solution: Reduce complexity (as less as possible binary variables)

13 Simone Göttlich Derivation MIP Idea: Introduce binary variables (decision variables) Problem: Suitable discretization of the -nonlinearity Example: implies and Outflow of the queue = Inflow to a processor

14 Simone Göttlich Mixed Integer Program maximize outflux processor queue outflow initial conditions Two-point Upwind discretization (PDE) and explicit Euler discretization (ODE) leads to queue

15 Simone Göttlich Advantage MIP  Bounded queues: Optimal inflow profile: In other words: Find a maximum possible inflow to the network such that the queue-limits are not exceeded. Constraints can be easily added to the MIP model:

16 Simone Göttlich Goal  Reduce computational complexity  Avoid rounding errors in the CPLEX solver  Accelerate the solution procedure How to do that?  Create a new preprocessing where PDE-knowledge is included  Compute lower und upper bounds by bounds strengthening Disadvantage MIP The problem has binary variables!

17 Simone Göttlich Network - Model ExistenceExtensions Optimization ContinuousDiscrete Modeling

18 Simone Göttlich Adjoint Calculus See Göttlich, Herty, Kirchner, Klar (2006): Optimal Control for Continuous Supply Network Models Adjoint calculus is used to solve PDE and ODE constrained optimization problems. Following steps have to be performed: 1. Define the Lagrange – functional: with Lagrange multipliers and Cost functional PDE processor ODE queue

19 Simone Göttlich Adjoint Calculus 2. Derive the first order optimality system (KKT-system): Forward (state) equations: Backward (adjoint) equations: Gradient equation:

20 Simone Göttlich Network - Model ExistenceExtensions Optimization ContinuousDiscrete Modeling

21 Simone Göttlich Linear Program (LP) Linear Program (LP) Outflow of the queue Idea: Use adjoint equations to prove the reformulation of the MIP as a LP MIP LP Remark: The remaining equations remain unchanged! Remove the complementarity condition!

22 Simone Göttlich interior network points Example Numerical Results Solved by ILOG CPLEX 10.0

23 Simone Göttlich


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