Home Truck and Trailer Routing Problem Solving by a Backtracking Search Algorithm
Article
Licensed
Unlicensed Requires Authentication

Truck and Trailer Routing Problem Solving by a Backtracking Search Algorithm

  • Shiyi Yuan , Jianwen Fu , Feng Cui and Xin Zhang EMAIL logo
Published/Copyright: July 30, 2020
Become an author with De Gruyter Brill

Abstract

Truck and trailer routing problem (TTRP) is one of the most frequently encountered problem in city distribution, particularly in populated and intensive downtown. This paper addresses this problem and designs a novel backtracking search algorithm (BSA) based meta-heuristics to solve it. The initial population is created by T-sweep heuristic and then based on the framework of backtracking search algorithm, four types of route improvement strategies are used as building blocks to improve the solutions of BSA in the process of mutation and crossover. The computational experiments and results indicate that the proposed BSA algorithm can provide an effective approach to generate high-quality solutions within the satisfactory computational time.


Supported by Premium Funding Project for Academic Human Resources Development in Beijing Union University (BPHR2020CZ06)


References

[1] Dantzig G B, Ramser J H. The truck dispatching problem. Management Science, 1959, 6(1): 80–91.10.1287/mnsc.6.1.80Search in Google Scholar

[2] Braekers K, Ramaekers K, Van Nieuwenhuyse I. The vehicle routing problem: State of the art classification and review. Computers & Industrial Engineering, 2016, 99: 300–313.10.1016/j.cie.2015.12.007Search in Google Scholar

[3] Eksioglu B, Vural A V, Reisman A. The vehicle routing problem: A taxonomic review. Computers & Industrial Engineering, 2009, 57(4): 1472–1483.10.1016/j.cie.2009.05.009Search in Google Scholar

[4] Chao I M. A tabu search method for the truck and trailer routing problem. Computers & Operations Research, 2002, 29(1): 33–51.10.1016/S0305-0548(00)00056-3Search in Google Scholar

[5] Parragh S N, Cordeau J F. Branch-and-price and adaptive large neighborhood search for the truck and trailer routing problem with time windows. Computers & Operations Research, 2017, 83: 28–44.10.1016/j.cor.2017.01.020Search in Google Scholar

[6] Wang C, Mu D, Zhao F, et al. A parallel simulated annealing method for the vehicle routing problem with simultaneous pickup-delivery and time windows. Computers & Industrial Engineering, 2015, 83: 111–122.10.1016/j.cie.2015.02.005Search in Google Scholar

[7] Bortfeldt A, Hahn T, Mnnel D, et al. Hybrid algorithms for the vehicle routing problem with clustered backhauls and 3D loading constraints. European Journal of Operational Research, 2015, 243(1): 82–96.10.1016/j.ejor.2014.12.001Search in Google Scholar

[8] Scheuerer S A. Tabu search heuristic for the truck and trailer routing problem. Computers & Operations Research, 2006, 33(4): 894–909.10.1016/j.cor.2004.08.002Search in Google Scholar

[9] Shih T, Yu H. Probability distribution of return and volatility in crude oil market. The Journal of Global Business Management, 2009, 5(2): 210–220.Search in Google Scholar

[10] Villegas J G, Prins C, Prodhon C, et al. A GRASP with evolutionary path relinking for the truck and trailer routing problem. Computers & Operations Research, 2011, 38(9): 1319–1334.10.1016/j.cor.2010.11.011Search in Google Scholar

[11] Villegas J G, Prins C, Prodhon C, et al. A matheuristic for the truck and trailer routing problem. European Journal of Operational Research, 2013, 230(2): 231–244.10.1016/j.ejor.2013.04.026Search in Google Scholar

[12] Derigs U, Pullmann M, Vogel U. Truck and trailer routing-Problems, heuristics and computational experience. Computers & Operations Research, 2013, 40(2): 536–546.10.1016/j.cor.2012.08.007Search in Google Scholar

[13] Wang C, Zhou S, Gao Y, et al. A self-adaptive bat algorithm for the truck and trailer routing problem. Engineering Computations, 2018, 35: 108–135.10.1108/EC-11-2016-0408Search in Google Scholar

[14] Gunawan A, Lau H C, Wong E. Real-world parameter tuning using factorial design with parameter decomposition, Advances in Metaheuristics. Springer, 2013: 37–59.10.1007/978-1-4614-6322-1_3Search in Google Scholar

[15] Modiri-Delshad M, Kaboli S H A, Taslimi-Renani E, et al. Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options. Energy, 2016, 116: 637–649.10.1016/j.energy.2016.09.140Search in Google Scholar

[16] Civicioglu P. Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 2013, 219(15): 8121–8144.10.1016/j.amc.2013.02.017Search in Google Scholar

[17] Song X, Zhang X, Zhao S, Li L. Backtracking search algorithm for effective and efficient surface wave analysis. Journal of Applied Geophysics, 2015, 114: 19–31.10.1016/j.jappgeo.2015.01.002Search in Google Scholar

[18] El-Fergany A. Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm. International Journal of Electrical Power & Energy Systems, 2015, 64: 1197–1205.10.1016/j.ijepes.2014.09.020Search in Google Scholar

[19] Modiri-Delshad M, Rahim N A. Solving non-convex economic dispatch problem via backtracking search algorithm. Energy, 2014, 77: 372–381.10.1016/j.energy.2014.09.009Search in Google Scholar

[20] Santini A, Plum C E, Ropke S. A branch-and-price approach to the feeder network design problem. European Journal of Operational Research, 2018, 264(2): 607–622.10.1016/j.ejor.2017.06.063Search in Google Scholar

[21] Drexl M. A branch and price algorithm for the truck and trailer routing problem. Technical Report, RWTH Aachen University, Germany. http://www.dpor.rwth-aachen.de/de/publikationen/pdf/or\_2006-06.pdf. 2007.Search in Google Scholar

[22] Drexl M. Branch-and-price and heuristic column generation for the generalized truck-and-trailer routing problem. Journal of Quantitative Methods for Economics and Business Administration, 2011, 12: 5–38.Search in Google Scholar

[23] Clarke G, Wright J W. Scheduling of vehicles from a central depot to a number of delivery points. Operations Research, 1964, 12(4): 568–581.10.1007/978-3-642-27922-5_18Search in Google Scholar

[24] Caramia M, Guerriero F. A heuristic approach for the truck and trailer routing problem. Journal of the Operational Research Society, 2010, 61(7): 1168–1180.10.1057/jors.2009.59Search in Google Scholar

[25] Reed M, Yiannakou A, Evering R. An ant colony algorithm for the multi-compartment vehicle routing problem. Applied Soft Computing, 2014, 15: 169–176.10.1016/j.asoc.2013.10.017Search in Google Scholar

[26] Mirmohammadsadeghi S, Ahmed S. Metaheuristic approaches for solving truck and trailer routing problems with stochastic demands: A case study in dairy industry. Mathematical Problems in Engineering, 2015. http://doi.org/10.1155/2015/494019.10.1155/2015/494019Search in Google Scholar

[27] Lin S W, Vincent F Y, Chou S Y. Solving the truck and trailer routing problem based on a simulated annealing heuristic. Computers & Operations Research, 2009, 36(5): 1683–1692.10.1016/j.cor.2008.04.005Search in Google Scholar

[28] Lin S W, Vincent F Y, Chou S Y. A note on the truck and trailer routing problem. Expert Systems with Applications, 2010, 37(1): 899–903.10.1016/j.eswa.2009.06.077Search in Google Scholar

[29] Lin S W, Vincent F Y, Lu C C. A simulated annealing heuristic for the truck and trailer routing problem with time windows. Expert Systems with Applications, 2011, 38(12): 15244–15252.10.1016/j.eswa.2011.05.075Search in Google Scholar

[30] Blum C, Puchinger J, Raidl G R, et al. Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing, 2011, 11(6): 4135–4151.10.1016/j.asoc.2011.02.032Search in Google Scholar

[31] Akhtar M, Hannan M, Begum R, et al. Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization. Waste Management, 2017, 61: 117–128.10.1016/j.wasman.2017.01.022Search in Google Scholar PubMed

[32] Zhang C, Zhou J, Li C, et al. A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting. Energy Conversion and Management, 2017, 143: 360–376.10.1016/j.enconman.2017.04.007Search in Google Scholar

[33] Gillett B E, Miller L R. A heuristic algorithm for the vehicle-dispatch problem. Operations Research, 1974, 22(2): 340–349.10.1287/opre.22.2.340Search in Google Scholar

[34] Savelsbergh M W. The vehicle routing problem with time windows: Minimizing route duration. ORSA Journal on Computing, 1992, 4(2): 146–154.10.1287/ijoc.4.2.146Search in Google Scholar

[35] Christofides N, Mingozzi A, Toth P. The vehicle routing problem. Eds. by Christofides N M A, Toth P, Sandi C. Combinatorial Optimization. UK: Wiley, Chichester, 1979: 315–338.Search in Google Scholar

[36] Cattaruzza D, Absi N, Feillet D, et al. A memetic algorithm for the multi trip vehicle routing problem. European Journal of Operational Research, 2014, 236(3): 833–848.10.1016/j.ejor.2013.06.012Search in Google Scholar

[37] Lin Q, Gao L, Li X, et al. A hybrid backtracking search algorithm for permutation flow-shop scheduling problem. Computers & Industrial Engineering, 2015, 85: 437–446.10.1016/j.cie.2015.04.009Search in Google Scholar

[38] Tan K C, Chew Y H, Lee L. A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. Computational Optimization and Applications, 2006, 34(1): 115–151.10.1007/s10589-005-3070-3Search in Google Scholar

[39] Tu W, Li Q, Li Q, et al. A spatial parallel heuristic approach for solving very large-scale vehicle routing problems. Transactions in GIS, 2017, 21(6): 1130–1147.10.1111/tgis.12267Search in Google Scholar

Received: 2020-03-20
Accepted: 2020-05-16
Published Online: 2020-07-30
Published in Print: 2020-07-28

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 29.9.2025 from https://www.degruyterbrill.com/document/doi/10.21078/JSSI-2020-253-20/html
Scroll to top button