Abstract
The concrete delivery problem (CDP) is an NP-hard, real world combinatorial optimization problem. The CDP involves tightly interrelated routing and scheduling constraints that have to be satisfied by considering the tradeoff between production and distribution costs. Various exact and heuristic methods have been developed to address the CDP. However, due to the limitation of the exact methods for dealing with such a complex problem, (meta-)heuristics have been more popular. For this purpose, the present study proposes a hybrid algorithm combining simulated annealing (SA) with a time-slot heuristic (TH) for tackling the CDP. The TH is applied for generating new solutions through perturbation while simulated annealing is utilized to decide on whether to accept these solutions. The proposed algorithm, i.e. SA-TH, is compared to an existing CDP heuristic on a diverse set of CDP benchmarks. The computational results conducted through a series of experiments validate the efficiency and success of SA-TH.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kinable, J., Wauters, T., Berghe, G.V.: The concrete delivery problem. Comput. Oper. Res. 48, 53–68 (2014)
Kallehauge, B., Larsen, J., Madsen, O.B., Solomon, M.M.: Vehicle routing problem with time windows. In: Desaulniers, G., Desrosiers, J., Solomon, M.M. (eds.) Column Generation, pp. 67–98. Springer, Boston (2005). doi:10.1007/0-387-25486-2_3
Frizzell, P.W., Giffin, J.W.: The split delivery vehicle scheduling problem with time windows and grid network distances. Comput. Oper. Res. 22(6), 655–667 (1995)
Aarts, E., Korst, J., Michiels, W.: Simulated annealing. In: Burke, E., Kendall, G. (eds.) Search Methodologies, pp. 265–285. Springer, Boston (2014). doi:10.1007/978-1-4614-6940-7_10
Yan, S., Lai, W., Chen, M.: Production scheduling and truck dispatching of ready mixed concrete. Transp. Res. Part E: Logist. Transp. Rev. 44(1), 164–179 (2008)
Asbach, L., Dorndorf, U., Pesch, E.: Analysis, modeling and solution of the concrete delivery problem. Eur. J. Oper. Res. 193(3), 820–835 (2009)
Lu, M.: HKCONSIM: a simulation platform for planning and optimizing concrete plant operations in Hong Kong. In: Proceedings of International Conference on Innovation and Sustainable Development of Civil Engineering in the 21st Century, Beijing, China, pp. 278–283 (2002)
Lu, M., Wu, D., Zhang, J.: A particle swarm optimization-based approach to tackling simulation optimization of stochastic, large-scale and complex systems. In: Yeung, D.S., Liu, Z.-Q., Wang, X.-Z., Yan, H. (eds.) ICMLC 2005. LNCS, vol. 3930, pp. 528–537. Springer, Heidelberg (2006). doi:10.1007/11739685_55
Lu, M., Lam, H.C.: Optimized concrete delivery scheduling using combined simulation and genetic algorithms. In: Proceedings of the 37th Conference on Winter Simulation, Winter Simulation Conference, pp. 2572–2580 (2005)
Liu, Z., Zhang, Y., Li, M.: Integrated scheduling of ready-mixed concrete production and delivery. Autom. Constr. 48, 31–43 (2014)
Lin, P.C., Wang, J., Huang, S.H., Wang, Y.T.: Dispatching ready mixed concrete trucks under demand postponement and weight limit regulation. Autom. Constr. 19(6), 798–807 (2010)
Mayteekrieangkrai, N., Wongthatsanekorn, W.: Optimized ready mixed concrete truck scheduling for uncertain factors using bee algorithm. Songklanakarin J. Sci. Technol. 37(2), 221–230 (2015)
Naso, D., Surico, M., Turchiano, B., Kaymak, U.: Genetic algorithms for supply-chain scheduling: a case study in the distribution of ready-mixed concrete. Eur. J. Oper. Res. 177(3), 2069–2099 (2007)
Maghrebi, M., Periaraj, V., Waller, S.T., Sammut, C.: Column generation-based approach for solving large-scale ready mixed concrete delivery dispatching problems. Comput.-Aided Civil Infrastruct. Eng. 31(2), 145–159 (2016)
Devapriya, P., Ferrell, W., Geismar, N.: Integrated production and distribution scheduling with a perishable product. Eur. J. Oper. Res. 259(3), 906–916 (2017)
Silva, C., Faria, J., Abrantes, P., Sousa, J., Surico, M., Naso, D.: Concrete delivery using a combination of GA and ACO. In: 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference, CDC-ECC 2005, pp. 7633–7638. IEEE (2005)
Yan, S., Lai, W.: An optimal scheduling model for ready mixed concrete supply with overtime considerations. Autom. Constr. 16(6), 734–744 (2007)
Misir, M., Vancroonenburg, W., Verbeeck, K., Berghe, G.V.: A selection hyper-heuristic for scheduling deliveries of ready-mixed concrete. In: Proceedings of the Metaheuristics International Conference (MIC), Udine, Italy, pp. 289–298 (2011)
Berghman, L., Leus, R., Spieksma, F.C.: Optimal solutions for a dock assignment problem with trailer transportation. Ann. Oper. Res. 213, 1–23 (2014)
Bilgin, B., Demeester, P., Misir, M., Vancroonenburg, W., Vanden Berghe, G.: One hyper-heuristic approach to two timetabling problems in health care. J. Heuristics 18(3), 401–434 (2012)
Salomon, R.: Evolutionary algorithms and gradient search: similarities and differences. IEEE Trans. Evol. Comput. 2(2), 45–55 (1998)
Kinable, J.: (2013). https://sites.google.com/site/cdplib
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (NSFC) under grant 61300159, by the Natural Science Foundation of Jiangsu Province of China under grant BK20130808 and by China Postdoctoral Science Foundation under grant 2015M571751. The authors thank J. Kinable, T. Wauters and G. Vanden Berghe, for providing their CDP source code.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Sulaman, M., Cai, X., Mısır, M., Fan, Z. (2017). Simulated Annealing with a Time-Slot Heuristic for Ready-Mix Concrete Delivery. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-68759-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68758-2
Online ISBN: 978-3-319-68759-9
eBook Packages: Computer ScienceComputer Science (R0)