A. Juan, B. Barrios, J. Faulín , E. Pérez-Bernabeu r

1. 2013 ICSO-HAROSA International Workshop on Simulation-Optimization & Internet Computing July 10-12, 2013 - Barcelona, SPAIN  Horizontal Cooperation in Road Transportation: A Multi-Depot Case Illustrating Savings in Distances and Pollutant Emissions Angel A. Juan Barry B. Barrios IN3-Open University of Catalonia, Barcelona, SPAIN Javier Faulín Public University of Navarre, Pamplona, SPAIN Elena Pérez-Bernabeu Universitat Politècnica de Valencia, Alcoy, SPAIN A. Juan, B. Barrios, J. Faulín, E. Pérez-Bernabeur Horizontal Cooperation in Road Transportation: A Multi-Depot Case Illustrating Savings in Distances and Pollutant Emissions

2. Outline • Introduction to horizontal cooperation • Recent studies on vehicle routing problems • Description of the approach • Overview of the algorithm • Experimental data • Results • Conclusions

3. Outline • Introduction to horizontal cooperation • Recent studies on vehicle routing problems • Description of the approach • Overview of the algorithm • Experimental data • Results • Conclusions

4. Introduction to horizontal cooperation • This study focuses on the Road Transportation Sector. • Most SMEs in this sector suffer from pressures to raise their distribution prices. • The European Union (2001) defines horizontal cooperation as “concerted practices among companies operating at the same level(s) in the market”. • Collaboration among partners in the transportation industry can help reducing environmental footprint as it can reduce the number of necessary trips and increase the efficiency of the haulers.

5. Introduction to horizontal cooperation Non-cooperative scenario: each provider delivers its own customers. Cooperative scenario: each customer is delivered by the closest provider.

6. Outline • Introduction to horizontal cooperation • Recent studies on vehicle routing problems • Description of the approach • Overview of the algorithm • Experimental data • Results • Conclusions

7. Recent studies on vehicle routing problems • Krajewska et al. (2008) analyze the profit margins resulting from horizontal cooperation among freight carriers and the possibilities of sharing these margins fairly among the partners. • Obersneider et al. (2012) use a multicriteria analysis to minimize the CO2 emissions apart from other type of costs. • Ubeda et al. (2011) study the resolution of a green logistics problem in a Spanish retailer integrating the pick-up and delivery activities in conjoint routes of the same fleet vehicles.

8. Outline • Introduction to horizontal cooperation • Recent studies on vehicle routing problems • Description of the approach • Overview of the algorithm used • Experimental data • Results • Conclusions

9. Description of the approach • It’sassumedthatthe carriers and shippers are directly controlled by the same companies. • Full horizontal cooperation among companies takes place. • The routing decisions are made globally such in a way that the overall delivery costs are minimize. This minimization goal can be subjected to some constraints related to the maximum capacity of each firm or depot.

10. Three scenarios to be considered • Cooperative: Companies are willing to cooperate, i.e. customers can be reassigned to other companies. Problem becomes MDVRP • Non cooperative: • Clustered topology: Customers are assigned to each depot according to a distance-based criteria. • Scattered topology: Customers are assigned to each depot at random assuming a dispersed topology.

11. Problem description D • There is a set of suppliers and customers D D D D D D D

12. Problem description Map1 D D D D • Cooperative scenario - MDVRP: • Initial customers assignation is based on Euclidean distance. New maps are formed based on perturbation

13. Pertubateprocess Generate new mapbypertubatingnodes • Some customers change their color after performing a biased-randomization process Map2 D D D D

14. Pertubateprocess Generate new mapbypertubatingnodes • Some customers change their color after performing a biased-randomization process Map3 D D D D

15. Problem description P2 P1 D P3 D D P4 D • Non-cooperative scenario - MVRP: • Clustered topology: Customers are assigned to each depot based on Euclidean distance. Once assigned no changes are allowed.

16. Problem description D D D D • Non-cooperative scenario - MVRP: • Scattered topology: Customers are assigned to each depot randomly. Once assigned no changes are allowed.

17. Problem description P1 D • Non-cooperative scenario - MVRP: • Scattered topology: Customers are assigned to each depot randomly. Once assigned no changes are allowed.

18. Problem description P2 D • Non-cooperative scenario - MVRP: • Scattered topology: Customers are assigned to each depot randomly. Once assigned no changes are allowed.

19. Problem description P3 D • Non-cooperative scenario - MVRP: • Scattered topology: Customers are assigned to each depot randomly. Once assigned no changes are allowed.

20. Problem description P4 D • Non-cooperative scenario - MVRP: • Scattered topology: Customers are assigned to each depot randomly. Once assigned no changes are allowed.

21. Outline • Introduction to horizontal cooperation • Recent studies on vehicle routing problems • Description of the approach • Overview of the algorithm used • Experimental data • Results • Conclusions

22. Overview of the algorithm used

23. Outline • Introduction to horizontal cooperation • Recent studies on vehicle routing problems • Description of the approach • Overview of the algorithm used • Experimental data • Results • Conclusions

24. 10 completed runs per instance and Maximum running time per replica: 2 min Neuman Benchmarks p02 pr02 p01 pr01 NAME:p01-p23 NAME:pr01-pr10 50-360 48-288 Customers Customers 4-14 1-6 Vehicles: Vehicles: 4-9 4,6 Depots Depots 60-500 170-200 Capacity Capacity Demand 1-25 Demand 1-25

25. Our Solution– vs. Best known solution Competitive solutions Average gap of 0.3%

26. Cooperative vs. Clustered and Scattered – Distance base Vs Cluster, >23% VsScattered, >90% Vs Cluster, >5% Vs Scattered, >90%

27. Cooperative vs. Clustered and Scattered – Pollution base Ubeda S, Arcelus FJ and Faulin (2011).Green Logistics at Eroski: A case study. International Journal of Production Economics, 131(1), pp.44-51. Vs Cluster, 23% Vs Scattered, >47% Vs Cluster, >5% Vs Scattered, >92%

28. Outline • Introduction to horizontal cooperation • Recent studies on vehicle routing problems • Description of the approach • Overview of the algorithm used • Experimental data • Results • Conclusions

29. Results

30. Outline • Introduction to horizontal cooperation • Recent studies on vehicle routing problems • Description of the approach • Overview of the algorithm used • Experimental data • Results • Conclusions

31. Conclusions • It has been shown the importance of horizontal cooperation by reducing: • Distribution costs • Pollutant gas emissions • Multi-depot vehicle routing problem (MDVRP) has been applied to show the positive impact of Horizontal cooperation in VR problems • MDVRP has been solved with an efficient ILS approach using: • Clarke and Wright • SR-GCWS-CS by Juan et al. 2011

32. 2013 ICSO-HAROSA International Workshop on Simulation-Optimization & Internet Computing July 10-12, 2013 - Barcelona, SPAIN  Horizontal Cooperation in Road Transportation: A Multi-Depot Case Illustrating Savings in Distances and Pollutant Emissions Angel A. Juan Barry B. Barrios IN3-Open University of Catalonia, Barcelona, SPAIN Javier Faulín Public University of Navarre, Pamplona, SPAIN Elena Pérez-Bernabeu Universitat Politècnica de Valencia, Alcoy, SPAIN A. Juan, B. Barrios, J. Faulín, E. Pérez-Bernabeur Horizontal Cooperation in Road Transportation: A Multi-Depot Case Illustrating Savings in Distances and Pollutant Emissions