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 Centre National de la Recherche Scientifique  Institut National Polytechnique de Grenoble  Université Joseph Fourier Laboratoire G-SCOP 46, av Félix.

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Presentation on theme: " Centre National de la Recherche Scientifique  Institut National Polytechnique de Grenoble  Université Joseph Fourier Laboratoire G-SCOP 46, av Félix."— Presentation transcript:

1  Centre National de la Recherche Scientifique  Institut National Polytechnique de Grenoble  Université Joseph Fourier Laboratoire G-SCOP 46, av Félix Viallet 38031 Grenoble Cedex www.g-scop.inpg.fr Simulation-based assessment of the robustness of IP-based truck schedules for cross-docking operations Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales This exchange program is funded by

2 Simulation-based assessment of the robustness of IP-based truck schedules for cross-docking operations IP-based truck schedules for cross-docking operations assessment of the robustness of Simulation-based Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion - Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion - 2 Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013

3 Cross-docking 3 Less than 24h of temporary storage docking unloading scanning transfer loading departing Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion

4 Scheduling problem 4 10am-12am 6am-8am 9am-12am 6am-7am 6am-9am 11am-12am 7am-10am Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion  Minimize  Quantity put in storage  Dissatisfaction of the transport providers  Reservation system:

5 IP model (IESM 2013, Rabat) min (penalty on the time window chosen + # pallets put in storage) Flow conservation (for each destination) # trucks present <= # doorsOutbound trucks leave when fully loadedTransfer capacity  Assumptions  Internal operations are done in masked time, within one time unit  The door-to-door distance for the transfer is not taken into account  The pallets unloaded on the floor can be picked in any order  Decision variables  # of units moving from point to point (incl. storage)  Time windows for the trucks 5 Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion

6 Research question How do random events distort the schedule ? How to assess its robustness? What should be changed in the IP model to make the schedule more robust? Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 6 Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion

7 Methodology  Discrete events simulation  Simulate complex stochastic processes  Add logic to react in unplanned situations  Gather data over multiple runs  Software: FlexSim (http://www.flexsim.com) Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 7 Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion

8 Simulation and optimization Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 8 Simulation model Optimization model Simulation model Optimization model Simulation model Optimization model Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion

9 Principle Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 Simulation model Optimization model Truck schedule Truck arrival and departure time Amount in storage Pallet transfer Comparison Logic Random events 9 Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion

10 Model demonstration Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 10

11 Validity range  Some assumptions are rather strong Are they reasonable?  Internal operations are done in masked time, within one time unit Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 Simulation model Optimization model 11 Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion

12 door1 door2 door3 Insights from the simulation Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 12  Correlation between error in docking time and error in stay time? 0 Trucks stay longer than expected Docking times not affected between 0 and 1 Trucks stay longer than expected Docking time shifted 1 Trucks stay longer than expected Docking times shifted accordingly All delayed trucks are critical No truck is critical Some delayed trucks are critical Number of critical trucks a priori ≤ Nb of actual critical trucks Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion

13 Robustness  Everything is deterministic What if random events occur?  Trucks arrival time (early / late) Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 Simulation model Optimization model 13 Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion

14 Robustness  Everything is deterministic What if random events occur?  Content of the inbound trucks Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 Simulation model Optimization model 14 Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion

15  Simulation is used to assess the robustness, but also to gather ideas on robustness improvement  Ideas to make the IP model more robust  Add a flexible « buffer » door  Change the model to use n-1 doors  Avoid critical trucks Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 15 Cross-docking operations IP-based truck schedule Robustness assessment Results Conclusion

16  Centre National de la Recherche Scientifique  Institut National Polytechnique de Grenoble  Université Joseph Fourier Laboratoire G-SCOP 46, av Félix Viallet 38031 Grenoble Cedex www.g-scop.inpg.fr Thank you for your attention! Questions? Contact: Anne-Laure.Ladier@g-scop.fr

17 IP*  Data  Decision variables 17 Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013

18 IP* 18 Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013

19 19

20 Pallets transfer Anne-Laure Ladier, Allen Greenwood, Gülgün Alpan, Halston Hales | EURO-INFORMS 2013 20


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