Semet Gecco06

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The document discusses using an inoculation approach to solve the problem of rescheduling railway trains following a small perturbation like a delay. The inoculation approach involves first running the algorithm with an "empty" incident to solve initial constraints and find a reasonable starting permutation, then using that solution to initialize the population for solving the actual problem. Experimental results show the inoculation approach finds good solutions faster than alternatives for both easy and hard instances of the scheduling problem.

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On the Effects of Inoculation: an Example in Train Scheduling Y. Semet * , M. Schoenauer INRIA Orsay, France Research funded by SNCF *:now with EUROBIOS (complexity science for business problems)

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Solving conflicts: moving forward in time i Space Time j Train c’ is already scheduled Train c is being scheduled Safety spacing threshold a(c,j) α α

Solving conflicts: “kicking” obstacles out i Space Time j Train c’ is already scheduled Train c is being scheduled α α

Solving conflicts : “kicking” obstacles out i Space Time j Train c is being scheduled α α Kick !

Solving conflicts : “kicking” obstacles out i Space Time j Train c is being scheduled

Solving conflicts : “kicking” obstacles out i Space Time j Train c is now scheduled Train c’ is being re-scheduled

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The Benefits of Inoculation Easy Instances

The Benefits of Inoculation Hard Instances

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Simulators play a major role in analyzing multi-modal transportation networks. As complexity of simulators increases, development of calibration procedures is becoming an increasingly challenging task. Current calibration procedures often rely on heuristics, rules of thumb and sometimes on brute-force search. In this talk we consider a statistical framework for calibration that relies on Bayesian optimization. Bayesian optimization treats the simulator as a sample from a Gaussian process (GP). Tractability and sample efficiency of Gaussian processes enable computationally efficient algorithms for calibration problems. We show how the choice of prior and inference algorithm effect the outcome of our optimization procedure. We develop dimensionality reduction techniques that allow for our optimization techniques to be applicable for real-life problems. We develop a distributed, Gaussian Process Bayesian regression and active learning models. We demonstrate those to calibrate ground transportation simulation models.

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Semet Gecco06

1. On the Effects of Inoculation: an Example in Train Scheduling Y. Semet * , M. Schoenauer INRIA Orsay, France Research funded by SNCF *:now with EUROBIOS (complexity science for business problems)

3. Zoom in: one node…

8. Solving conflicts: moving forward in time i Space Time j Train c’ is already scheduled Train c is being scheduled Safety spacing threshold a(c,j) : violation !! α α

9. Solving conflicts: moving forward in time i Space Time j Train c’ is already scheduled Train c is being scheduled Safety spacing threshold a(c,j) α α

10. Solving conflicts: “kicking” obstacles out i Space Time j Train c’ is already scheduled Train c is being scheduled α α

11. Solving conflicts : “kicking” obstacles out i Space Time j Train c is being scheduled α α Kick !

12. Solving conflicts : “kicking” obstacles out i Space Time j Train c is being scheduled

13. Solving conflicts : “kicking” obstacles out i Space Time j Train c is now scheduled Train c’ is being re-scheduled

14. Global picture : a ( μ + λ ) scheme + μ parents Keep μ best individuals λ offspring Sélection = [3 4 5 1 2 6 8 7]

15. Global picture : a ( μ + λ ) scheme + μ parents Keep μ best individuals λ offspring Sélection = [3 4 5 1 2 6 8 7]

16.

17.

18.

19.

20. The Benefits of Inoculation Easy Instances

21. The Benefits of Inoculation Hard Instances

22. The Benefits of Inoculation Hard Instances

23.

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