Abstract
This paper uses a Genetic Programming Hyper-Heuristic (GPHH) to evolve routing policies for the Uncertain Capacitated Arc Routing Problem (UCARP). Given a UCARP instance, the GPHH evolves feasible solutions in the form of decision making policies which decide the next task to serve whenever a vehicle completes its current service. Existing GPHH approaches have two drawbacks. First, they tend to generate small routes by routing through the depot and refilling prior to the vehicle being fully loaded. This usually increases the total cost of the solution. Second, existing GPHH approaches cannot control the extra repair cost incurred by a route failure, which may result in higher total cost. To address these issues, this paper proposes a new GPHH algorithm with a new No-Early-Refill filter to prevent generating small routes, and a novel Flood Fill terminal to better handle route failures. Experimental studies show that the newly proposed GPHH algorithm significantly outperforms the existing GPHH approaches on the Ugdb and Uval benchmark datasets. Further analysis has verified the effectiveness of both the new filter and terminal.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amponsah, S., Salhi, S.: The investigation of a class of capacitated arc routing problems: the collection of garbage in developing countries. Waste Manag. 24(7), 711–721 (2004)
Branke, J., Nguyen, S., Pickardt, C.W., Zhang, M.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Comput. 20(1), 110–124 (2016)
Burke, E.K., Hyde, M., Kendall, G., Woodward, J.: A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics. IEEE Trans. Evol. Comput. 14(6), 942–958 (2010)
Christiansen, C., Lysgaard, J., Wøhlk, S.: A branch-and-price algorithm for the capacitated arc routing problem with stochastic demands. Oper. Res. Lett. 37(6), 392–398 (2009)
Defryn, C., Sörensen, K., Cornelissens, T.: The selective vehicle routing problem in a collaborative environment. Eur. J. Oper. Res. 250(2), 400–411 (2015)
Eglese, R.W., Li, L.Y.O.: A tabu search based heuristic for arc routing with a capacity constraint and time deadline. In: Osman, I.H., Kelly, J.P. (eds.) Meta-Heuristics: Theory and Applications, pp. 633–649. Springer, Boston (1996). https://doi.org/10.1007/978-1-4613-1361-8_38
Fleury, G., Lacomme, P., Prins, C., Ramdane-Chérif, W.: Improving robustness of solutions to arc routing problems. J. Oper. Res. Soc. 56(5), 526–538 (2005)
Golden, B., Dearmon, J., Baker, E.: Computational experiments with algorithms for a class of routing problems. Comput. Oper. Res. 10, 47–59 (1983)
Golden, B., Wong, R.: Capacitated arc routing problems. Networks 11(3), 305–315 (1981)
Handa, H., Chapman, L., Yao, X.: Dynamic salting route optimisation using evolutionary computation. In: IEEE Congress on Evolutionary Computation, pp. 158–165 (2005)
Handa, H., Chapman, L., Yao, X.: Robust route optimization for gritting/salting trucks: a CERCIA experience. IEEE Comput. Intell. Mag. 1(1), 6–9 (2006)
Hertz, A., Laporte, G., Mittaz, M.: A tabu search heuristic for the capacitated arc routing problem. Oper. Res. 48, 129–135 (2000)
Lacomme, P., Prins, C., Ramdane-Cherif, W.: Competitive memetic algorithms for arc routing problems. Ann. Oper. Res. 131(1), 159–185 (2004)
Liu, Y., Mei, Y., Zhang, M., Zhang, Z.: Automated heuristic design using genetic programming hyper-heuristic for uncertain capacitated arc routing problem. In: Proceedings of GECCO, pp. 290–297. ACM (2017)
Mei, Y., Tang, K., Yao, X.: Improved memetic algorithm for capacitated arc routing problem. In: IEEE Congress on Evolutionary Computation, pp. 1699–1706 (2009)
Mei, Y., Tang, K., Yao, X.: Capacitated arc routing problem in uncertain environments. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)
Mei, Y., Zhang, M.: Genetic programming hyper-heuristic for multi-vehicle uncertain capacitated arc routing problem. In: ACM Genetic and Evolutionary Computation Conference (GECCO) (2017)
Nguyen, S., Mei, Y., Zhang, M.: Genetic programming for production scheduling: a survey with a unified framework. Complex Intell. Syst. 3(1), 41–66 (2017)
Speranza, M., Fernandez, E., Roca-Riu, M.: The shared customer collaboration vehicle routing problem. Eur. J. Oper. Res. 265(3), 1078–1093 (2016)
Tsutsui, S., Wilson, G.: Solving capacitated vehicle routing problems using edge histogram based sampling algorithms. In: Proceedings of the 2004 Congress on Evolutionary Computation, vol. 1, pp. 1150–1157 (2004)
Ulusoy, G.: The fleet size and mix problem for capacitated arc routing. Eur. J. Oper. Res. 22(3), 329–337 (1985)
Wang, J., Tang, K., Lozano, J.A., Yao, X.: Estimation of the distribution algorithm with a stochastic local search for uncertain capacitated arc routing problems. IEEE Trans. Evol. Comput. 20(1), 96–109 (2016)
Wang, J., Tang, K., Yao, X.: A memetic algorithm for uncertain capacitated arc routing problems. In: 2013 IEEE Workshop on Memetic Computing, pp. 72–79 (2013)
Weise, T., Devert, A., Tang, K.: A developmental solution to (dynamic) capacitated arc routing problems using genetic programming. In: Proceedings of GECCO, pp. 831–838. ACM (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
MacLachlan, J., Mei, Y., Branke, J., Zhang, M. (2018). An Improved Genetic Programming Hyper-Heuristic for the Uncertain Capacitated Arc Routing Problem. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-03991-2_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03990-5
Online ISBN: 978-3-030-03991-2
eBook Packages: Computer ScienceComputer Science (R0)