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A comparison of regular and disrupted operations forroute planning in freight transportationWED 1.5. Critical Infrastructu...
Agenda1. Introduction2. Methodology   a. Multi Agent Transport Simulation   b. Tour Planning Algorithm3. The case study   ...
Multi-agent transport simulation model    MATSim allows for setting the capacity and travel speed of each link or street ...
Tour planning algorithm in 4flow vista       Sample tour schedules                                               Products...
A retail distribution network – case study I    Network locations in Germany                     Characteristics          ...
Network disruptions – case study II                  Snowfall scenario In order to model disturbances the link capacities...
Results and analysis – case study IIIUndisturbed networkDisturbed networkRe-optimized network                             ...
Added value to integrative risk management        More realistic investigation and assessment of the   I        perpetuall...
Thank you for your attentionMaximilian Brock4flow AGM +49 (0) 173 629 27 77m.brock@4flow.deZhang LiKITli.zhang@kit.edu   ...
Back-up          © 4flow      Seite          29.08.2012     10
Results and analysis – case study IIIUndisturbed network                                        Disturbed network (snowfal...
Results and analysis – case study IIIDisturbed network                                        Re-optimized network        ...
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A comparison of regular and disrupted operations for route planning in freight transportation

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Maximilian BROCK1, Tilman MATTEIS2, Cristina HAYDEN1, Li ZHANG2, Wendelin GROSS1

14flow AG, Germany, Federal Republic of; 2Institute for Economic Policy Research, Karlsruhe Institute of Technology, Germany, Federal Republic of;

Published in: Education, Technology, Business
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A comparison of regular and disrupted operations for route planning in freight transportation

  1. 1. A comparison of regular and disrupted operations forroute planning in freight transportationWED 1.5. Critical Infrastructures IIDavos, August 29th, 2012
  2. 2. Agenda1. Introduction2. Methodology a. Multi Agent Transport Simulation b. Tour Planning Algorithm3. The case study a. A retail distribution network b. Network disruption c. Results and analysis4. Added value to integrative risk management © 4flow Seite 29.08.2012 2
  3. 3. Multi-agent transport simulation model  MATSim allows for setting the capacity and travel speed of each link or street section, respectively.  An agent (green dot) represents a given number of vehicles.  A vehicle travels according to a tour plan.  A genetic algorithm is used in order to simulate intelligence  Each vehicle can change its departure time and route with every iteration. Traffic simulation in Baden- Württemberg on European trunk road network  For each agent the travel distance and time-depended travel times are obtained.  Per day the average travel times are calculated from the time-dependent travel times. © 4flow Seite 29.08.2012 3
  4. 4. Tour planning algorithm in 4flow vista Sample tour schedules  Products are delivered from regional depots in small quantities and with a high delivery frequency.  Several stores are combined to one delivery tour.  Each store has an individual delivery pattern.  For each day master tours are built with the tour planning algorithm of the logistics planning software 4flow vista.  MATSim provides the basis for decision- making for the final route planning process considering traffic. © 4flow vista  Travel times, distances and master tours are interchanged between the models. © 4flow Seite 29.08.2012 4
  5. 5. A retail distribution network – case study I Network locations in Germany Characteristics  Ten regional depots  702 stores  The allocation of stores to depots is not fixed.  A store can be supplied by more than one depot.  The multiple-product demand of the store is turned into a homogeneous volume demand.  The depots and trucks have © 4flow vista maximum capacities and trucks are deployed between 6 and 20 h. © 4flow Seite 29.08.2012 5
  6. 6. Network disruptions – case study II Snowfall scenario In order to model disturbances the link capacities and free-speeds can be adjusted. In the snowfall scenario a decrease in free speed of affected links is assumed around three regional depots. A set of indicators will be used to evaluate the tour performance before and after the disruption as well as in a re-optimized state.  The risk management strategies include the use of alternative roads or routes.  Furthermore, a store might be supplied from different depot then before. © 4flow Seite 29.08.2012 6
  7. 7. Results and analysis – case study IIIUndisturbed networkDisturbed networkRe-optimized network © 4flow Seite 29.08.2012 7
  8. 8. Added value to integrative risk management More realistic investigation and assessment of the I perpetually changing traffic situation Reducing the gap of uncertainty between the tour II planned and tours realized The data interchange allows for the fast optimization III and adaption of tour plans in case of traffic congestions or even catastrophic events. © 4flow Seite 29.08.2012 8
  9. 9. Thank you for your attentionMaximilian Brock4flow AGM +49 (0) 173 629 27 77m.brock@4flow.deZhang LiKITli.zhang@kit.edu The results presented have been achieved in the research project RM-Log, which is funded by the Federal Ministry of Education and Research within the framework of the Federal Governments "Research for Civil Security" program. © 4flow Seite 29.08.2012 9
  10. 10. Back-up © 4flow Seite 29.08.2012 10
  11. 11. Results and analysis – case study IIIUndisturbed network Disturbed network (snowfall) © 4flow Seite 29.08.2012 11
  12. 12. Results and analysis – case study IIIDisturbed network Re-optimized network © 4flow Seite 29.08.2012 12

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