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Improved Ant Colony Algorithm for Logistics Vehicle Routing Problem with Time Window

  • Conference paper
Emerging Research in Artificial Intelligence and Computational Intelligence (AICI 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 315))

Abstract

For the current ant colony algorithm(ACO) cannot take the real-time road condition into consideration, a improved ACO was proposed which covers there aspects influence of people, vehicle and road. Mathematical model of driver effect factor was put forward. Define the road network division algorithm. Consider the vehicle’s condition. To avoid the ACO slide into local optima, negative feedback strategy was introduced when updating the global pheromone. Time window impact factor was added into the logistics vehicle’s transition probability algorithm to make sure that the rush order have a relatively higher priority to be processed. The improved ACO aimed at solving vehicle routing problem(VRP) was accomplished by the computer. Test results show that the improved ACO has better optimization efficiency.

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References

  1. Laporte, G.: Fifty years of vehicle routing, Canada, pp. 1–23. HEC MONTREAL (2009)

    Google Scholar 

  2. An, H.C., Kleinberg, R., Shmoys, D.B.: Improving Christofides’ Algorithm for the s-t Path TSP. eprint arXiv:1110.4604, 1–31 (2011)

    Google Scholar 

  3. Matthew, D., Kym, P., Johan, W.: Randomness, and Regularity: Spatial Distributions and Human Performance on the Traveling Salesperson Problem and Minimum Spanning Tree Problem. The Journal of Problem Solving 4(1), 1–17 (2012)

    Google Scholar 

  4. Huang, D., Yan, X., Chu, X., Mao, Z.: An Adaptive Algorithm for Dynamic Vehicle Routing Problem Based on Real Time Traffic Information. In: ICTIS 2011: Multimodal Approach to Sustained Transportation System Development—Information, Technology, Implementation Proceedings of the 1st International Conference on Transportation Information and Safety (2011)

    Google Scholar 

  5. Jepsen, M.K.: Branch-and-cut and Branch-and-Cut-and-Price Algorithms for Solving. University of Copenhagen (2011)

    Google Scholar 

  6. Gunther: Two heuristic solution concepts for the vehicle selection problem in line haul transports. European Journal of Operational Research 217(2), 448–458 (2011)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Transportation Science and Engineering Department, Beihang University, 37# Xueyuan Rd, Haidian District, Beijing, China

    Jian Wang, Yanyan Wang & Hongyun Li

Authors
  1. Jian Wang
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  2. Yanyan Wang
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  3. Hongyun Li
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Editor information

Editors and Affiliations

  1. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  2. Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

  3. School of Business Information Technology, RMIT University, City Campus, 124 La Trobe Street, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  4. School of Electronics and Information, Tongji University, China, 201804, Shanghai, China

    Duoqian Miao

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© 2012 Springer-Verlag Berlin Heidelberg

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Wang, J., Wang, Y., Li, H. (2012). Improved Ant Colony Algorithm for Logistics Vehicle Routing Problem with Time Window. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Emerging Research in Artificial Intelligence and Computational Intelligence. AICI 2012. Communications in Computer and Information Science, vol 315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34240-0_6

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  • DOI: https://doi.org/10.1007/978-3-642-34240-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34239-4

  • Online ISBN: 978-3-642-34240-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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