Biomimicry improvement of school shuttle routing: incorporating demand balancing nodes

  • Authors

    • P. A. Ozor 1. University of Johannesburg, South Africa2. University of Nigeria Nsukka, Nigeria
    • C. Mbohwa 1. University of Johannesburg
    2018-11-13
    https://doi.org/10.14419/ijet.v7i4.12501
  • Ant Colony Optimization, Biomimicry, Demand Balancing Nodes, Developing Countries, Shuttle Service Improvement.

  • Abstract

    Campus communities in some developing countries are predisposed to multiple modes of transport services (vehicle, motorcycle, tricycle). The security concerns in such area may lead to prohibition of one or more of the existing systems with a consequent unbearable hardship to the community. This study investigated the use of Bio-mimicking based algorithm- Ant Colony Optimization and incorporation of demand balancing nodes to determine an effective shuttle routing plan in a Campus community. The approach was applied to a specific example Campus, namely; Shuttle routing problem in a Nigeria public University. From the analysis of all the data collected for the study, five and three additional shuttle terminals were created for the hypothetical Northern and Southern zones respectively. The distances travelled by the shuttles in the new routes varied from a minimum of 1537.64m to a maximum of 3912.27m in the Northern zone. The mean route distance in the zone is 2509.25m. Similarly, the Southern zone routes have distances varying from 1932.43m to 2260.8m, with a mean route distance of 2120.42m. Comparatively, the shuttle route distances in the existing routes varied from 4134.55m 4706.08m with a mean route distance of 4481.99m. The results show that an average distance reduction of 44% was observed for shuttle routes in the Northern zone. The results also show that average distance reduction of over 52% is obtainable for shuttle routes in southern zone.

     

     

     

  • References

    1. [1] Abdulkadera, M.M.S., Gajpalb, Y., ElMekkawy, T.Y., (2015), Hybridized Ant colony algorithm for the multi compartment vehicle routing problem, Applied Soft Computing, 37, 196–203. https://doi.org/10.1016/j.asoc.2015.08.020.

      [2] Alves, D., Van, J., Cong, Z., Shutter, B., Babuska, R., (2010), Ant colony optimization for traffic dispersion routing, Proceedings of the 13th International IEEE conference on Intelligent Transportation systems, 683-688. https://doi.org/10.1109/ITSC.2010.5625146.

      [3] Androutsopoulos, K.N., Zografos, K.G., (2017), An integrated modeling approach for the bicriterion vehicle routing and scheduling problem with environmental considerations, Transport research part C, Emerging Technologies, 82, 180-209. https://doi.org/10.1016/j.trc.2017.06.013.

      [4] Arias-Rojas, Fernando, J., Montoya, J., (2012), Solving of School Bus Routing problem using Ant colony Optimization, EIA, 17, 193-208.

      [5] Ashok, L., Messinger, W., (2012). A spectral image clustering algorithm based on Ant colony optimization, In Shen, S. S., Lewis, P. E. (Ed.) Algorithms and Technologies for Multispectral, Hyper spectral, and Ultra spectral Imagery XVIII, In Proceeding of SPIE 2012 Vol. 8390, 83901P CCC code: 0277-786X/12/$18 https://doi.org/10.1117/12.919082.

      [6] Bin,Y., Zhong- Zhen ,Y., Baozhen ,Y., (2009), An improved Ant colony optimization for vehicle routing problem, European Journal of Operational Research 196, 171-176. https://doi.org/10.1016/j.ejor.2008.02.028 .Bulhoes, T., Ha, M.H., Martinelli, R., Vida, T., (2017), the vehicle routing problem with service level constraints, Production, manufacturing and logistics, European journal of operational research, 000 (article in press), 1-15, http://dx.doi.org/10.1016/j.ejor.2017.08.027.

      [7] Cheng, C-Y., Chen, Y.Y., Chen, T.L., Yoo, J.J-W., (2015), Using a hybrid approach based on the particle swarm optimization and ant colony optimization to solve a joint order batching and picker routing problem, Int. J. Production Economics, 170, 805–814. https://doi.org/10.1016/j.ejor.2017.08.027.

      [8] Christofides, N., Mingozzi, A., Toth, P., (1979), the vehicle routing problem, in: N.Christofides, A. Mingozzi, P. Toth, C. Sandi (Eds.), Combinatorial Optimization, Wiley, Chichester, 1979, 315–338. https://doi.org/10.1016/j.ijpe.2015.03.021.

      [9] Dorigo, M. and Gambardella, L. (1997), Ant colonies for the travelling salesman problem, BioSystems, vol.43, no.2, pp.73-81.

      [10] El Yakine, K.N., Menaa, M., Hasni, M., Boudour M., (2017), Using differential search algorithm for solving optimal frequency regulation problem in interconnected power system, International Journal of Bio-Inspired Computation 9, (3), 182, https://doi.org/10.1016/S0303-2647(97)01708-5.

      [11] Ezzatneshan A., (2015), Vehicle routing optimization using spanning tree and Ant colony, Visi jurnal Akademik, 6, 107-115. https://doi.org/10.1504/IJBIC.2017.083719.

      [12] Gao, S., Wang, Y., Cheng, J., Inazumi, Y., Tang, Z., (2016), Ant colony optimization with clustering for solving the dynamic location routing problem, Applied Mathematics and computation 285, 149–173.

      [13] Huber, S., Geiger, M.J., (2017), Order matters - A variable neighborhood search for the swap-body vehicle routing problem, European journal of operational research, 263, (2), 419-445. https://doi.org/10.1016/j.amc.2016.03.035.

      [14] Kuo, R.J., Wibowo, B.S., Zulvia, F.E., (2016), Application of a fuzzy ant colony system to solve the dynamic vehicle routing problem with uncertain service time, Applied Mathematical modeling, 40, 9990 –10001. https://doi.org/10.1016/j.ejor.2017.04.046.

      [15] Mavrovouniotis, M., Yang S., (2015), Ant algorithms with immigrants schemes for the dynamic vehicle routing problem, Information Sciences 294 (2015) 456–477. https://doi.org/10.1016/j.apm.2016.06.025.

      [16] Narasimha, K.V., Kivelevitch, E., Sharma, B, Kumar, M., (2013), an ant colony optimization technique for solving min–max multi-depot vehicle routing problem, Swarm and evolutionary computation, 13, 63–73. https://doi.org/10.1016/j.ins.2014.10.002.

      [17] Nogueira, K., Aquiar, P., Weigang, L., (2014), Using Ant algorithm to arrange Taxiway sequencing in Airport, International journal of computer theory and engineering, 6, (4) 357-361. https://doi.org/10.1016/j.swevo.2013.05.005.

      [18] Polat, O., (2017), A parallel variable neighborhood search for the vehicle routing problem with divisible deliveries and pickups, Computers & Operations research, 85, 71-86.

      [19] Reed, M., Yiannakou, A., Evering, R., (2014), An Ant colony algorithm for the multi-compartment vehicle routing problem, Applied soft computing, 15, 169–176. https://doi.org/10.1016/j.cor.2017.03.009.

      [20] Sevkli A.Z., Guler, B., (2017), A multi-phase oscillated variable neighbourhood search algorithm for a real-world open vehicle routing problem, Applied soft computing, 58, 128-144. https://doi.org/10.1016/j.asoc.2013.10.017.

      [21] Sharma, J.K. (2005) Operations Research: Theory and Applications, 2nd ed., Macmillan India Ltd., Rajiv Beri, 2/10 Ansari road, Daryanganj, New Delhi. https://doi.org/10.1016/j.asoc.2017.04.045.

      [22] Wu, W., Tian, Y., Jin, T., (2016), A label based Ant colony algorithm for heterogeneous vehicle routing with mixed backhaul, Applied soft computing, 47, 224-234.

      [23] Zhang, M., Zhang, Y., (2017), Ant colonial-based approach for the minimal full trie problem, International Journal of Bio-Inspired Computation 9(4):235. https://doi.org/10.1016/j.asoc.2016.05.011.

      [24] Zhang, M., Zhang, Y., (2017), Ant colonial-based approach for the minimal full trie problem, International Journal of Bio-Inspired Computation 9(4):235 https://doi.org/10.1504/IJBIC.2017.10005264.

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  • How to Cite

    A. Ozor, P., & Mbohwa, C. (2018). Biomimicry improvement of school shuttle routing: incorporating demand balancing nodes. International Journal of Engineering & Technology, 7(4), 4658-4666. https://doi.org/10.14419/ijet.v7i4.12501

    Received date: 2018-05-05

    Accepted date: 2018-08-24

    Published date: 2018-11-13