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Study of delivery path optimization solution based on improved ant colony model

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Abstract

As a bionic optimization algorithm, ant colony algorithm has the advantages of robustness, parallel computation and easy combination and so on, which can solve complicated combinatorial optimization problems. However, the selection strategy of traditional algorithm is more random, which leads to the slow evolution speed. Therefore, an improved ant colony algorithm is proposed, which uses density peak clustering algorithm to classify sites and local optimization strategy. The simulating results of multiple TSP problems demonstrate that the improved algorithm has good optimization ability, greatly improves the quality of the solution and the speed of optimization, and overcomes the slowness and tendency of the algorithm.

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References

  1. Baldacci R, Bodin L, Mingozzi A (2006) The multiple disposal facilities and multiple inventory locations rollon-rolloff vehicle routing problem[M]. Elsevier Science Ltd

  2. Belhaiza S (2018) A game theoretic approach for the real-life multiple-criterion vehicle routing problem with multiple time windows[J]. IEEE Syst J 12(2):1251–1262

    Article  MathSciNet  Google Scholar 

  3. Ben Ticha H, Absi N, Feillet D, Quilliot A (2017) Empirical analysis for the VRPTW with a multigraph representation for the road network[J]. Comput Oper Res 88:103–116

    Article  MathSciNet  Google Scholar 

  4. Christos DT, Stavropoulou F, Panagiotis PR (2012) A template-based Tabu search algorithm for the consistent vehicle routing problem. Expert Syst Appl 39:4233–4239

    Article  Google Scholar 

  5. Humberto CBO, Germano CV (2010) A hybrid search method for the vehicle routing problem with time windows. Ann Oper Res 180:125–144

    Article  Google Scholar 

  6. Jiang C, Li R, Chen T, Xu C, Li L, Li S (2020) A two-lane mixed traffic flow model with drivers' intention to change lane based on cellular automata[J].INT J BIO-INSPIR COM ‏ 16: 229-240

  7. Lai DSW, Demirag OC, Leung JMY (2016) A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph[J]. Transp Res E Logist Transp Rev 86:32–52

    Article  Google Scholar 

  8. Li H, Lei W, Hei X et al (2018) A decomposition-based chemical reaction optimization for multi-objective vehicle routing problem for simultaneous delivery and pickup with time windows[J]. Memet Comput 10(1):103–120

    Article  Google Scholar 

  9. Meng XP, Pian ZY, Shen ZY et al (2013) Ant algorithm based on direction -coordinating[J]. Control Decis 28(5):782–786

    Google Scholar 

  10. Pradhananga R, Taniguchi E, Yamada T, Qureshi AG (2014) Bi-objective decision support system for routing and scheduling of hazardous materials[J]. Socio Econ Plan Sci 48(2):135–148

    Article  Google Scholar 

  11. Sánchez-Oro J, López-Sánchez AD, Colmenar JM (2017) A general variable neighborhood search for solving the multi-objective open vehicle routing problem[J]. J Heuristics 2:1–30

    Google Scholar 

  12. Wang J, Zhou Y, Wang Y et al (2017) Multiobjective vehicle routing problems with simultaneous delivery and pickup and time windows: formulation, instances, and algorithms[J]. IEEE Trans Cybern 46(3):582–594

    Article  Google Scholar 

  13. Wang J, Weng T, Zhang Q (2018) A two-stage multiobjective evolutionary algorithm for MultiobjectiveMultidepot vehicle routing problem with time windows[J]. IEEE Trans Cybern 99:1–12

    Google Scholar 

  14. Ying Z, Wang J (2017) A local search-based multiobjective optimization algorithm for multiobjective vehicle routing problem with time windows[J]. IEEE Syst J 9(3):1100–1113

    Google Scholar 

  15. Yu B, Yang ZZ, Yao B (2009) An improved ant colony optimization for vehicle routing problem. Eur J Oper Res 196:171–176

    Article  Google Scholar 

  16. Yucenur GN, Nihan CD (2011) A new geometric shap-based genetic clustering algorithm for the multi-depot vehicle routing problem. Expert Syst Appl 38:11859–11865

    Article  Google Scholar 

  17. Zhang Z, Sun Y, Hong X et al (2018) GMMA: GPU-based multiobjective memetic algorithms for vehicle routing problem with route balancing[J]. Appl Intell 49(4):63–78

    Google Scholar 

Download references

Acknowledgments

Scientific Research Project of National Natural Science Foundation of China (No. U1709212), Scientific Research Project of Zhejiang Province (Grant No. 2019C02075, LGG18F030012, LGG19F010012), Natural Science Foundation of Zhejiang Province (Grant No. LY19F030023, LY19F020048). and College Student Research Programme of Zhejiang A&F University.

Funding

Scientific Research Project of National Natural Science Foundation of China (No. U1709212), Scientific Research Project of Zhejiang Province (Grant No. 2019C02075, LGG18F030012, LGG19F010012), Natural Science Foundation of Zhejiang Province (Grant No. LY19F030023, LY19F020048). and College Student Research Programme of Zhejiang A&F University.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Key Laboratory of Forestry Sensing Technology and Intelligent Equipment of Department of Forestry, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Hangzhou, 311300, China

    Ruixiao Huang, Jingyuan Ning, Zhenghao Mei, Xudong Fang, Xiaomei Yi, Yuanyuan Gao & Guohua Hui

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  1. Ruixiao Huang
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  2. Jingyuan Ning
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  3. Zhenghao Mei
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  4. Xudong Fang
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  5. Xiaomei Yi
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  6. Yuanyuan Gao
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  7. Guohua Hui
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Corresponding author

Correspondence to Guohua Hui.

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All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

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Conflict of interest

Author Xiaomei Yi has received research grant from Scientific Research Project of Zhejiang Province (LGG19F010012). Yuanyuan Gao has received research grant from Scientific Research Project of Zhejiang Province (LY19F030023). Guohua Hui has received grant from Scientific Research Project of National Natural Science Foundation of China (No. U1709212), Scientific Research Project of Zhejiang Province (Grant No. 2019C02075). Ruixiao Huang declares that he has no conflicts of interest. Jingyuan Ning declares that he has no conflicts of interest. Zhenghao Mei declares that he has no conflicts of interest. Xudong Fang declares that he has no conflicts of interest. Xiaomei Yi declares that she has no conflicts of interest. Yuanyuan Gao declares that she has no conflicts of interest. Guohua Hui declares that he has no conflicts of interest.

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Huang, R., Ning, J., Mei, Z. et al. Study of delivery path optimization solution based on improved ant colony model. Multimed Tools Appl 80, 28975–28987 (2021). https://doi.org/10.1007/s11042-021-11142-1

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  • DOI: https://doi.org/10.1007/s11042-021-11142-1

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