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An Improved Ant Colony Algorithm to Solve Vehicle Routing Problem with Time Windows

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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Abstract

This paper presents an improved ant colony optimization algorithm (ACO algorithm) based on Ito differential equations, the proposed algorithm integrates the versatility of Ito thought with the accuracy of ACO algorithm in solving the vehicle routing problem (VRP), and it executes simultaneous move and wave process, and employs exercise ability to unify move and wave intensity. Move and wave operator rely on attractors and random perturbations to set the motion direction. In the experiment part, this improved algorithm is implemented for solving vehicle routing problem with soft time windows (VRPSTW), and tested by Solomon Benchmark standard test dataset, the result shows that the proposed algorithm is effective and feasible.

This work was supported by the Open Foundation of Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis (No. HCIC201411), the National Undergraduate Training Programs for Innovation and Entrepreneurship (No. 201410605055), and the Education Scientific Research Foundation of Guangxi Province (No. KY2015YB254).

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Notes

  1. 1.

    Source: http://web.cba.neu.edu/~msolomon/problems.htm

References

  1. Kim, S., Lewis, M.E., White III, C.C.: Optimal vehicle routing with real-time traffic information. IEEE Trans. Intell. Transp. Syst. 6(2), 178–188 (2005)

    Article  Google Scholar 

  2. Dong, W., et al.: BBOB-benchmarking: a new evolutionary algorithms inspired by ITO process for noiseless function tested. J. Comput. Inf. Syst. pp. 2195–2203 (2011)

    Google Scholar 

  3. Korenevskiy, N., Al-Kasasbeh, R.T., Ionescouc, F., Alshamasin, M., Alkasasbeh, E., Smith, A.P.: Fuzzy determination of the human’s level of psycho-emotional. In: Toi, V.V., Toan, N.B., Dang Khoa, T.Q., Lien Phuong, T.H. (eds.) 4th International Conference on Biomedical Engineering in Vietnam. IFMBE Proceedings, vol. 40, pp. 213–216. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  4. Hongbin, L.: Study on multi-depots and multi-vehicles vehicle scheduling problem based on improved particle swarm optimization. Comput. Eng. Appl. 50(7), pp. 251−253 (2014) (in Chinese)

    Google Scholar 

  5. Qiuyun, W., Wenbao, J.: Solving algorithm of vehicle routing problem with soft time windows. J. Beijing Inf. Sci. Technol. Univ. 28(4), pp. 57−59 (2013) (in Chinese)

    Google Scholar 

  6. Wen, M., Cordeau, J.-F., Laporte, G., et al.: The Dynamic Multi-PeriodVehicle Routing Problem. DTU Management Engineering, Denmark (2009)

    Google Scholar 

  7. Dong, W.: Time series modeling based on ITO algorithm. In: Proceedings of the International Conference on Natural Computation, pp. 398–402 (2007)

    Google Scholar 

  8. Wenyong, D., Wensheng, Z., Ruiguo, Y.: Convergence and runtime analysis of ITO algorithm for one class of combinatorial optimization. chinese. J. Comput. 34(4), pp. 636–646 (2011) (in Chinese)

    Google Scholar 

  9. Dong, W.: The multi-objective ITO algorithms. In: Proceedings of the International Conference on Intelligence Computation and Application, pp. 21–23 (2007)

    Google Scholar 

  10. Ghannadpour, S.F., Noori, S., Tavakkoli-Moghaddam, R.: Multiobjective dynamic vehicle routing problem with fuzzy travel times and customers’ satisfaction in supply chain management. IEEE Trans. Eng. Manage. 60(4), 777–790 (2013)

    Article  Google Scholar 

  11. MA, H., Jin, P., Yang, S.: Heuristic methods for time-dependent vehicle routing problem. J. Syst. Eng. 27(2), pp. 256−262 (2012) (in Chinese)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Computer and Information Engineering, Hechi University, Yizhou, 546300, China

    Yi Yunfei & Lin Xiaodong

  2. Computer School, Wuhan University, Wuhan, 430079, China

    Yi Yunfei, Sheng Kang & Cai Yongle

  3. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning, 530006, China

    Yi Yunfei

Authors
  1. Yi Yunfei
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  2. Lin Xiaodong
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  3. Sheng Kang
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  4. Cai Yongle
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Corresponding author

Correspondence to Yi Yunfei .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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Yunfei, Y., Xiaodong, L., Kang, S., Yongle, C. (2015). An Improved Ant Colony Algorithm to Solve Vehicle Routing Problem with Time Windows. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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