A Case Study of Bee Algorithm for Ready Mixed Concrete Problem
Authors: W. Wongthatsanekorn, N. Matheekrieangkrai
Abstract:
This research proposes Bee Algorithm (BA) to optimize Ready Mixed Concrete (RMC) truck scheduling problem from single batch plant to multiple construction sites. This problem is considered as an NP-hard constrained combinatorial optimization problem. This paper provides the details of the RMC dispatching process and its related constraints. BA was then developed to minimize total waiting time of RMC trucks while satisfying all constraints. The performance of BA is then evaluated on two benchmark problems (3 and 5construction sites) according to previous researchers. The simulation results of BA are compared in term of efficiency and accuracy with Genetic Algorithm (GA) and all problems show that BA approach outperforms GA in term of efficiency and accuracy to obtain optimal solution. Hence, BA approach could be practically implemented to obtain the best schedule.
Keywords: Bee Colony Optimization, Ready Mixed Concrete Problem.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1093862
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2916References:
[1] M. Lu, and H.C. Lam. 2005. "Optimized concrete delivery scheduling using combined simulation and genetic algorithms.” Winter Simulation Conference: 2572-2580.
[2] D. Naso, M. Surico, B. Turchiano, and U. Kaymak. 2007. "Genetic Algorithms for supply-chain scheduling, A case study in the distribution of ready-mixed concrete.” Ruropean Journal of Operational Research, vol.177, no.3: 2069-2099.
[3] M. Surico, U. Kaymak, D. Naso and R. Dekker. 2007. "A Bi-Objective Evolutionary Approach to Robust Scheduling.” IEEE: 1-6.
[4] D. L. Graham, D.R. Forbes and S.D. Smith. 2006. "Modeling the ready mixed concrete delivery system with neural networks.” Automation in Construction 15: 656–663.
[5] S. Yan, and W. Lai. 2006. "An optimal scheduling model for ready mixed concrete supply with overtime considerations.” Automation in Construction 16 : 734-744.
[6] S. Yan, W. Lai, and M. Chen. 2008. "Production scheduling and truck dispatching of ready mixed concrete.” Transportation Research Part E 44: 164-179.
[7] V. Schmid, K.F Doerner, R.F. Hartl, M.W.P. Savelsbergh, and W. Stoecher. 2009. "A hybrid solution approach for ready-mixed concrete delivery.” Transportation Science 43 (1): 70–85.
[8] V. Schmid, K.F Doerner, R.F. Hartl, and J.J. Salazar-Gonzáez. 2010. "Hybridization of very large neighborhood search for ready-mixed concrete delivery problems.” Computers and Operations Research 37 (3): 559–574.
[9] C.W. Feng, and H.T. Wu. 2000. "Using Genetic Algorithms to Optimize the dispatching Schedule of RMC Cars.”, Proceedings of the 17th International Symposium on Automation and Robotics in Construction, Taipei, Taiwan: 927-932.
[10] C.W. Feng, T.M. Cheng, and H.T. Wu. 2004. "Optimizing the schedule of dispatching RMC trucks through Genetic Algorithms.” Automation in Construction 13: 327-340.
[11] C.W. Feng, and H.T. Wu. 2006. "Integrating fmGA and CYCLONE to optimize the schedule of dispatching RMC trucks.” Automation in Construction 15: 186-199.
[12] D.T Pham, A. Ghanbarzadeh, E. Koç, S Otri, S. Rahim S, and M. Zaidi. 2005. "The Bees Algorithm.” Technical Note, Manufacturing Engineering Centre, Cardiff University, UK.
[13] D.T Pham, A. Ghanbarzadeh, E. Koç, S Otri, S. Rahim S, and M. Zaidi. 2006. "The Bees Algorithm – A Novel Tool for Complex Optimisation Problems, Proceedings of IPROMSConference: 454–461.
[14] Holland, John. 1975. "Adaptation in Natural and Artificial System.” University of Michigan Press, Ann Arbor, Michigan.