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A Simulated Annealing Algorithm Based Solution Method for a Green Vehicle Routing Problem with Fuel Consumption

Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 273)

Abstract

This chapter presents a new G-VRP model that aims to reduce the fuel consumption of the vehicle’s gas tank. The fuel consumption of a vehicle is related to total vehicle weight through route and thus, this changes the CO2 levels as a result of the changes of total weight and distance for any arc {i, j} in the route. To minimize CO2 levels, a simulated annealing-based algorithm is proposed. About the experiments, firstly, we applied small-VRP problem set for defining the proposed algorithm and then, the Christofides et al. (Combinatorial optimization. Wiley, 1979) small/medium scale C1–C14 datasets are used with proposed G-VRP model and a convex composition solution with two objective functions. The proposed methods are compared with statistical analysis techniques to explain the statistical significance of solutions. The procedures are also tested using additional examples previously analyzed in the literature. The result has shown good solutions for minimizing the emitted CO2 levels.

Keywords

Green vehicle routing Simulated annealing Fuel consumption rate 

References

  1. Alkawaleet, N., Hsieh, Y. F., & Wang, Y. (2014). Inventory routing problem with CO2 emissions consideration. In Logistics operations, supply chain management and sustainability (pp. 611–619). Cham: Springer.Google Scholar
  2. Alvarenga, G. B., Mateus, G. R., & De Tomi, G. (2007). A genetic and set partitioning two-phase approach for the vehicle routing problem with time windows. Computers and Operations Research, 34(6), 1561–1584.CrossRefGoogle Scholar
  3. Andelmin, J., & Bartolini, E. (2017). An exact algorithm for the green vehicle routing problem. Transportation Science, 51(4), 1288–1303.CrossRefGoogle Scholar
  4. Aranda, U. A., Ferreira, G., Bribián, Z. I., & Vásquez, Z. D. (2012). Study of the environmental performance of end-of-life tyre recycling through a simplified mathematical approach. Thermal Science, 16(3), 889–899.CrossRefGoogle Scholar
  5. Augerat, P. (1995). VRP problem instances set A-B-P. Retrieved July 27, 2017, from http://vrp.atd-lab.inf.puc-rio.br/index.php/en/
  6. Ayadi, R., ElIdrissi, A. E., Benadada, Y., & Alaoui, A. E. H. (2014). Evolutionary algorithm for a green vehicle routing problem with multiple trips. In 2014 International conference on logistics and operations management (GOL) (pp. 148–154). IEEE.Google Scholar
  7. Aydemir, H., & Cubuk, M. K. (2016). The investigation of the general situation of highways in turkey with recent changes and recommendations on future strategies. Gazi Journal of Engineering Sciences, 2(3), 129–146.Google Scholar
  8. Barnhart, C., & Laporte, G. (Eds.). (2006). Handbooks in operations research and management science: Transportation (Vol. 14). Amsterdam: Elsevier.Google Scholar
  9. Bektas, T. (2017). Freight transport and distribution: Concepts and optimisation models. Boca Raton, FL: CRC Press.CrossRefGoogle Scholar
  10. Bektaş, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45(8), 1232–1250.CrossRefGoogle Scholar
  11. Bouzekri, E. E. A., Aahmed, E. A., & Youssef, B. (2014). The green capacitated vehicle routing problem: Optimizing of emissions of greenhouse gas. In 2014 International conference on logistics and operations management (GOL) (pp. 161–167). IEEE.Google Scholar
  12. Bouzekri, E. A., Elhassania, M. E. S. S. O. U. D., & Alaoui, A. E. H. (2013). A hybrid ant colony system for green capacitated vehicle routing problem in sustainable transport. Journal of Theoretical and Applied Information Technology, 53(2), 198–208.Google Scholar
  13. Bouzekri, E. A., & Alaoui, A. E. H. (2014). A hybrid ant colony system for green capacitated vehicle routing problem in sustainable transport. International Journal of Scientific and Engineering Research, 5(9), 70–77.Google Scholar
  14. Bräysy, O., & Gendreau, M. (2005). Vehicle routing problem with time windows, part I: Route construction and local search algorithms. Transportation Science, 39(1), 104–118.CrossRefGoogle Scholar
  15. Cacchiani, V., Hemmelmayr, V. C., & Tricoire, F. (2014). A set-covering based heuristic algorithm for the periodic vehicle routing problem. Discrete Applied Mathematics, 163, 53–64.CrossRefGoogle Scholar
  16. Cetin, S., & Gencer, C. (2010). Vehicle routing problems with hard time windows and simultaneous pick up and delivery: A mathematical model. Journal of the Faculty of Engineering and Architecture of Gazi University, 25(3), 579–585.Google Scholar
  17. Christofides, N., Mingozzi, A., & Toth, P. (1979). The vehicle routing problem. In N. Christofides, A. Mingozzi, P. Toth, & C. Sandi (Eds.), Combinatorial optimization (pp. 315–338). Chichester: Wiley.Google Scholar
  18. Clarke, G., & Wright, J. W. (1964). Scheduling of vehicles from a central depot to a number of delivery points. Operations Research, 12(4), 568–581.CrossRefGoogle Scholar
  19. Cooray, P. L. N. U., & Rupasinghe, T. D. (2017). Machine learning-based parameter tuned genetic algorithm for energy minimizing vehicle routing problem. Journal of Industrial Engineering, 2017, 13.CrossRefGoogle Scholar
  20. Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6(1), 80–91.CrossRefGoogle Scholar
  21. Demir, E., Bektaş, T., & Laporte, G. (2014). A review of recent research on green road freight transportation. European Journal of Operational Research, 237(3), 775–793.CrossRefGoogle Scholar
  22. Eglese, R., & Bektaş, T. (2014). Chapter 15: Green vehicle routing. In P. Toth & D. Vigo (Eds.), Vehicle routing: Problems, methods, and applications (2nd ed., pp. 437–458). Philadelphia, PA: Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar
  23. Ene, S., Küçükoğlu, I., Aksoy, A., & Öztürk, N. (2016). A hybrid metaheuristic algorithm for the green vehicle routing problem with a heterogeneous fleet. International Journal of Vehicle Design, 71(1–4), 75–102.CrossRefGoogle Scholar
  24. Erdoğan, S., & Miller-Hooks, E. (2012). A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(1), 100–114.CrossRefGoogle Scholar
  25. Faulin, J., Juan, A., Lera, F., & Grasman, S. (2011). Solving the capacitated vehicle routing problem with environmental criteria based on real estimations in road transportation: A case study. Procedia-Social and Behavioral Sciences, 20, 323–334.CrossRefGoogle Scholar
  26. Franceschetti, A., Honhon, D., Van Woensel, T., Bektaş, T., & Laporte, G. (2013). The time-dependent pollution-routing problem. Transportation Research Part B: Methodological, 56, 265–293.CrossRefGoogle Scholar
  27. Ganesh, K., & Narendran, T. T. (2007). CLASH: A heuristic to solve vehicle routing problems with delivery, pick-up and time windows. International Journal of Services and Operations Management, 3(4), 460–477.CrossRefGoogle Scholar
  28. Gendreau, M., Hertz, A., & Laporte, G. (1994). A tabu search heuristic for the vehicle routing problem. Management Science, 40(10), 1276–1290.CrossRefGoogle Scholar
  29. Gribkovskaia, I., Laporte, G., & Shyshou, A. (2008). The single vehicle routing problem with deliveries and selective pickups. Computers and Operations Research, 35(9), 2908–2924.CrossRefGoogle Scholar
  30. Hassel, H., & Samaras, J. (1999). Methodology for calculating transport emissions and energy consumption (report for the projet MEET). Edinburgh: Transport Research Laboratory.Google Scholar
  31. Hsueh, C. F. (2016). The green vehicle routing problem with stochastic travel speeds. In CICTP 2016 (pp. 1–12).Google Scholar
  32. Huang, Y., Shi, C., Zhao, L., & Van Woensel, T. (2012). A study on carbon reduction in the vehicle routing problem with simultaneous pickups and deliveries. In 2012 IEEE international conference on service operations and logistics, and informatics (SOLI) (pp. 302–307). IEEE.Google Scholar
  33. Jabali, O., Woensel, T., & De Kok, A. G. (2012). Analysis of travel times and CO2 emissions in time-dependent vehicle routing. Production and Operations Management, 21(6), 1060–1074.CrossRefGoogle Scholar
  34. Jaramillo, J. R. (2011). The green vehicle routing. In Proceedings, informs annual meeting, October 5–7, Myrtle Beach, South Carolina, USA.Google Scholar
  35. Jemai, J., Zekri, M., & Mellouli, K. (2012). An NSGA-II algorithm for the green vehicle routing problem. In Evolutionary computation in combinatorial optimization (pp. 37–48). Berlin: Springer.CrossRefGoogle Scholar
  36. Johnson, D. S., Aragon, C. R., McGeoch, L. A., & Schevon, C. (1989). Optimization by simulated annealing: An experimental evaluation; part I, graph partitioning. Operations Research, 37(6), 865–892.CrossRefGoogle Scholar
  37. Kara, I., Kara, B., & Yetis, M. K. (2007). Energy minimizing vehicle routing problem. In Combinatorial optimization and applications (pp. 62–71). Berlin: Springer.Google Scholar
  38. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.CrossRefGoogle Scholar
  39. Koç, Ç., & Karaoglan, I. (2016). The green vehicle routing problem: A heuristic based exact solution approach. Applied Soft Computing, 39, 154–164.CrossRefGoogle Scholar
  40. 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.CrossRefGoogle Scholar
  41. Kramer, R., Subramanian, A., Vidal, T., & Lucídio dos Anjos, F. C. (2015). A matheuristic approach for the pollution-routing problem. European Journal of Operational Research, 243(2), 523–539.CrossRefGoogle Scholar
  42. Kuo, R. J., & Lin, L. M. (2010). Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering. Decision Support Systems, 49(4), 451–462.CrossRefGoogle Scholar
  43. Kuo, Y. (2010). Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Computers and Industrial Engineering, 59(1), 157–165.CrossRefGoogle Scholar
  44. Küçükoğlu, İ., & Öztürk, N. (2015). An advanced hybrid meta-heuristic algorithm for the vehicle routing problem with backhauls and time windows. Computers and Industrial Engineering, 86, 60–68.CrossRefGoogle Scholar
  45. Kwon, Y. J., Choi, Y. J., & Lee, D. H. (2013). Heterogeneous fixed fleet vehicle routing considering carbon emission. Transportation Research Part D: Transport and Environment, 23, 81–89.CrossRefGoogle Scholar
  46. Li, J. (2012). Vehicle routing problem with time windows for reducing fuel consumption. Journal of Computers, 7(12), 3020–3027.Google Scholar
  47. Lin, C., Choy, K. L., Ho, G. T., Chung, S. H., & Lam, H. Y. (2014). Survey of green vehicle routing problem: Past and future trends. Expert Systems with Applications, 41(4), 1118–1138.CrossRefGoogle Scholar
  48. Lin, L., & Fei, C. (2012). The simulated annealing algorithm implemented by the MATLAB. International Journal of Computer Science Issues (IJCSI), 9(6), 357–360.Google Scholar
  49. Maden, W., Eglese, R., & Black, D. (2010). Vehicle routing and scheduling with time-varying data: A case study. Journal of the Operational Research Society, 61(3), 515–522.CrossRefGoogle Scholar
  50. McKinnon, A. (2010). Environmental sustainability. In Green logistics: Improving the environmental sustainability of logistics. London.Google Scholar
  51. Ohlmann, J. W., & Thomas, B. W. (2007). A compressed-annealing heuristic for the traveling salesman problem with time windows. INFORMS Journal on Computing, 19(1), 80–90.CrossRefGoogle Scholar
  52. Oliveira, P. R. D. C., Mauceri, S., Carroll, P., & Pallonetto, F. (2017). A genetic algorithm for a green vehicle routing problem. In International network optimization conference 2017 (INOC 2017), Lisboa, Portugal, 26–28 February 2017.Google Scholar
  53. Omidvar, A., & Tavakkoli-Moghaddam, R. (2012). Sustainable vehicle routing: Strategies for congestion management and refueling scheduling. In 2012 IEEE international energy conference and exhibition (ENERGYCON) (pp. 1089–1094). IEEE.Google Scholar
  54. Özyurt, Z., Aksen, D., & Aras, N. (2006). Open vehicle routing problem with time deadlines: Solution methods and an application. In Operations research proceedings 2005 (pp. 73–78). Berlin: Springer.Google Scholar
  55. Palmer, A. (2007). The development of an integrated routing and carbon dioxide emissions model for goods vehicles. PhD thesis, School of Management, Cranfield University, Cranfield.Google Scholar
  56. Pan, S., Ballot, E., & Fontane, F. (2013). The reduction of greenhouse gas emissions from freight transport by pooling supply chains. International Journal of Production Economics, 143(1), 86–94.CrossRefGoogle Scholar
  57. Park, Y., & Chae, J. (2014). A review of the solution approaches used in recent G-VRP. International Journal of Advanced Logistics, 3(1–2), 27–37.CrossRefGoogle Scholar
  58. Peiying, Y., Jiafu, T., & Yang, Y. U. (2013). Based on low carbon emissions cost model and algorithm for vehicle routing and scheduling in picking up and delivering customers to airport service. In 2013 25th Chinese control and decision conference (CCDC) (pp. 1693–1697). IEEE.Google Scholar
  59. Pichpibul, T., & Kawtummachai, R. (2013). A heuristic approach based on clarke-wright algorithm for open vehicle routing problem. The Scientific World Journal, 2013, 874349.CrossRefGoogle Scholar
  60. Piecyk, M. (2010). Carbon auditing of companies, supply chains and products. In 2010 Green logistics: Improving the environmental sustainability of logistics (pp. 49–67). Kogan Page.Google Scholar
  61. Ramos, T. R. P., Gomes, M. I., & Barbosa-Póvoa, A. P. (2012). Minimizing CO2 emissions in a recyclable waste collection system with multiple depots. In EUROMA/POMS joint conference (pp. 1–5).Google Scholar
  62. Salhi, S., Imran, A., & Wassan, N. A. (2014). The multi-depot vehicle routing problem with heterogeneous vehicle fleet: Formulation and a variable neighborhood search implementation. Computers and Operations Research, 52, 315–325.CrossRefGoogle Scholar
  63. Suzuki, Y. (2011). A new truck-routing approach for reducing fuel consumption and pollutants emission. Transportation Research Part D: Transport and Environment, 16(1), 73–77.CrossRefGoogle Scholar
  64. Taha, M., Fors, M. N., & Shoukry, A. A. (2014). An exact solution for a class of green vehicle routing problem. In International conference on industrial engineering and operations management (pp. 7–9).Google Scholar
  65. Toro, O., Eliana, M., Escobar, Z., Antonio, H., & Granada, E. (2016). Literature review on the vehicle routing problem in the green transportation context. Luna Azul, 42, 362–387.Google Scholar
  66. Treitl, S., Nolz, P. C., & Jammernegg, W. (2014). Incorporating environmental aspects in an inventory routing problem. A case study from the petrochemical industry. Flexible Services and Manufacturing Journal, 26(1–2), 143–169.CrossRefGoogle Scholar
  67. Tunga, H., Bhaumik, A. K., & Kar, S. (2017). A method for solving bi-objective green vehicle routing problem (g-vrp) through genetic algorithm. Journal of the Association of Engineers, India, 87(1–2), 33–48.CrossRefGoogle Scholar
  68. Turkish Statistical Institute. (2014). Greenhouse gas emissions inventory, annual statistics, Ankara, Turkey. Retrieved July 21, 2017, from http://www.turkstat.gov.tr/PreHaberBultenleri.do?id=21582
  69. Ubeda, S., Arcelus, F. J., & Faulin, J. (2011). Green logistics at Eroski: A case study. International Journal of Production Economics, 131(1), 44–51.CrossRefGoogle Scholar
  70. Úbeda, S., Faulin, J., Serrano, A., & Arcelus, F. J. (2014). Solving the green capacitated vehicle routing problem using a tabu search algorithm. Lecture Notes in Management Science, 6, 141–149.Google Scholar
  71. Urquhart, N., Scott, C., & Hart, E. (2010). Using an evolutionary algorithm to discover low CO2 tours within a travelling salesman problem. In European conference on the applications of evolutionary computation (pp. 421–430). Berlin: Springer.CrossRefGoogle Scholar
  72. Vincent, F. Y., Redi, A. P., Hidayat, Y. A., & Wibowo, O. J. (2017). A simulated annealing heuristic for the hybrid vehicle routing problem. Applied Soft Computing, 53, 119–132.CrossRefGoogle Scholar
  73. Xiao, Y., Zhao, Q., Kaku, I., & Xu, Y. (2012). Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers and Operations Research, 39(7), 1419–1431.CrossRefGoogle Scholar
  74. Yan, S., Chi, C. J., & Tang, C. H. (2006). Inter-city bus routing and timetable setting under stochastic demands. Transportation Research Part A: Policy and Practice, 40(7), 572–586.Google Scholar
  75. Yasin, M., & Vincent, F. Y. (2013). A simulated annealing heuristic for the green vehicle routing problem. In Proceedings of the institute of industrial engineers Asian conference 2013. Singapore: Springer.CrossRefGoogle Scholar
  76. Zhang, Z., Long, K., Wang, J., & Dressler, F. (2014). On swarm intelligence inspired self-organized networking: Its bionic mechanisms, designing principles and optimization approaches. IEEE Communications Surveys and Tutorials, 16(1), 513–537.CrossRefGoogle Scholar
  77. Zhou, Y., & Lee, G. M. (2017). A lagrangian relaxation-based solution method for a green vehicle routing problem to minimize greenhouse gas emissions. Sustainability, 9(5), 776.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Honaz Vocational High School, Pamukkale UniversityDenizliTurkey
  2. 2.Business Administration DepartmentMehmet Akif Ersoy UniversityBurdurTurkey
  3. 3.Industrial Engineering DepartmentSüleyman Demirel UniversityIspartaTurkey
  4. 4.Department of EconomicsPamukkale UniversityDenizliTurkey

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