Open access

Abstract

Distribution is one of the major sources of carbon emissions and this issue has been addressed by Green Vehicle Routing Problem (GVRP). This problem aims to fulfill the demand of a set of customers using a homogeneous fleet of Alternative Fuel Vehicles (AFV) originating from a single depot. The problem also includes a set of Alternative Fuel Stations (AFS) that can serve the AFVs. Since AFVs started to operate very recently, Alternative Fuel Stations servicing them are very few. Therefore, the driving span of the AFVs is very limited. This makes the routing decisions of AFVs more difficult. In this study, we formulated a multi-objective optimization model of Green Vehicle Routing Problem with two conflicting objective functions. While the first objective of our GVRP formulation aims to minimize total CO2 emission, which is proportional to the distance, the second aims to minimize the maximum traveling time of all routes. To solve this multi-objective problem, we used ɛ-constraint method, a multi-objective optimization technique, and found the Pareto optimal solutions. The problem is formulated as a Mixed-Integer Linear Programming (MILP) model in IBM OPL CPLEX. To test our proposed method, we generated two hypothetical but realistic distribution cases in Izmir, Turkey. The first case study focuses on an inner-city distribution in Izmir, and the second case study involves a regional distribution in the Aegean Region of Turkey. We presented the Pareto optimal solutions and showed that there is a tradeoff between the maximum distribution time and carbon emissions. The results showed that routes become shorter, the number of generated routes (and therefore, vehicles) increases and vehicles visit a lower number of fuel stations as the maximum traveling time decreases. We also showed that as maximum traveling time decreases, the solution time significantly decreases.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1. Dunning J. H. (2014). The Globalization of Business (Routledge Revivals): The Challenge of the 1990s. London: Routledge.

  • 2. Erdoğan S. & Miller-Hooks E. (2012). A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review 48(1) 100-114.

  • 3. Koç Ç. & Karaoglan I. (2016). The green vehicle routing problem: A heuristic based exact solution approach. Applied Soft Computing 39 154-164.

  • 4. Bektaş T. & Laporte G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological 45 (8) 1232-1250.

  • 5. Demir E. Bektaş T. & Laporte G. (2012). An adaptive large neighborhood search heuristic for the pollution-routing problem. European Journal of Operational Research 223(2) 346-359.

  • 6. Koç Ç. Bektaş T. Jabali O. & Laporte G. (2014). The fleet size and mix pollution-routing problem. Transportation Research Part B: Methodological 70 239-254.

  • 7. Demir E. Bektaş T. & Laporte G. (2014). The bi-objective pollution-routing problem. European Journal of Operational Research 232(3) 464-478.

  • 8. Laporte G. Nobert Y. & Taillefer S. (1988). Solving a family of multi-depot vehicle routing and location-routing problems. Transportation Science 22(3) 161-172.

  • 9. Detti P. Papalini F. & de Lara G. Z. M. (2017). A multi-depot dial-a-ride problem with heterogeneous vehicles and compatibility constraints in healthcare. Omega 70 1-14.

  • 10. Bae H. & Moon I. (2016). Multi-depot vehicle routing problem with time windows considering delivery and installation vehicles. Applied Mathematical Modelling 40(13-14) 6536-6549.

  • 11. Sundar K. & Rathinam S. (2017). Algorithms for heterogeneous multiple depot multiple unmanned vehicle path planning problems. Journal of Intelligent & Robotic Systems 88(2-4) 513-526.

  • 12. Crevier B. Cordeau J. F. & Laporte G. (2007). The multi-depot vehicle routing problem with inter-depot routes. European Journal of Operational Research 176(2) 756-773.

  • 13. Cordeau J.F. & Maischberger M. (2012). A parallel iterated tabu search heuristic for vehicle routing problems. Computers & Operations Research 39(9) 2033-2050.

  • 14. Escobar J. W. Linfati R. Toth P. & Baldoquin M. G. (2014). A hybrid granular tabu search algorithm for the multi-depot vehicle routing problem. Journal of Heuristics 20(5) 483-509.

  • 15. Shimizu Y. & Sakaguchi T. (2014). A hierarchical hybrid meta-heuristic approach to coping with large practical multi-depot VRP. Industrial Engineering & Management Systems 13(2) 163-171.

  • 16. Subramanian A. Uchoa E. & Ochi L. S. (2013). A hybrid algorithm for a class of vehicle routing problems. Computers & Operations Research 40(10) 2519-2531.

  • 17. Savelsbergh M. W. P. (1992). The vehicle routing problem with time windows: Minimizing route duration. ORSA Journal on Computing 4(2) 146-154.

  • 18. Desrochers M. Desrosiers J. D. & Solomon M. (1992). A new optimization algorithm for the vehicle routing problem with time windows. Operations Research 40(2) 342-354.

  • 19. Ombuki B. Ross B. J. & Hanshar F. (2006). Multi-objective genetic algorithms for vehicle routing problem with time windows. Applied Intelligence 24(1) 17-30.

  • 20. Taş D. Dellaert N. Van Woensel T. & De Kok T. (2013). Vehicle routing problem with stochastic travel times including soft time windows and service costs. Computers & Operations Research 40(1) 214-224.

  • 21. Angelelli E. & Mansini R. (2002). The vehicle routing problem with time windows and simultaneous pick-up and delivery. In Klose A. Speranza M. G. & Van Wassenhove L. N. (Eds.) Quantitative approaches to distribution logistics and supply chain management (pp. 249-267). Berlin: Springer.

  • 22. Leung S. C. H. Zhang Z. Zhang D. Hua X. & Lim M. K. (2013). A meta-heuristic algorithm for heterogeneous fleet vehicle routing problems with two-dimensional loading constraints. European Journal of Operational Research 225(2) 199-210.

  • 23. Jiang J. Ng K. M. Poh K. L. & Teo K. M. (2014). Vehicle routing problem with a heterogeneous fleet and time windows. Expert Systems with Applications 41(8) 3748-3760.

  • 24. Belfiore P. & Yoshizaki H. T. Y. (2013). Heuristic methods for the fleet size and mix vehicle routing problem with time windows and split deliveries. Computers & Industrial Engineering 64(2) 589-601.

  • 25. Jair J. Paternina-Arboleda C. D. Cantillo V. & Montoya-Torres J. R. (2013). A two-pheromone trail ant colony system—Tabu search approach for the heterogeneous vehicle routing problem with time windows and multiple products. Journal of Heuristics 19(2) 233-252.

  • 26. Baños R. L. Ortega J. Gil C. N. Márquez A. L. & De Toro F. (2013). A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows. Computers & Industrial Engineering 65(2) 286-296.

  • 27. Guerriero F. Surace R. Loscri V. & Natalizio E. (2014). A multi-objective approach for unmanned aerial vehicle routing problem with soft time windows constraints. Applied Mathematical Modelling 38(3) 839-852.

Search
Journal information
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 210 210 50
PDF Downloads 150 150 26