(45-1) 18 * << * >> * Русский * English * Содержание * Все выпуски

Analysis of logistics distribution path optimization planning based on traffic network data
H.H. Li 1, H.R. Fu 2, W.H. Li 3

Anyang University, Anyang, Henan 455000, China,

Faculty of Computer Science, Vietnam-Korea University of Information and Communication Technology – The Uni-versity of Danang, Vietnam,

School of Transportation, Southeast University, Nanjing, Jiangsu 211189, China

 PDF, 1128 kB

DOI: 10.18287/2412-6179-CO-732

Страницы: 154-160.

Язык статьи: English

Аннотация:
With the development of economy, the distribution problem of logistics becomes more and more complex. Based on the traffic network data, this study analyzed the vehicle routing problem (VRP), designed a dynamic vehicle routing problem with time window (DVRPTW) model, and solved it with genetic algorithm (GA). In order to improve the performance of the algorithm, the genetic operation was improved, and the output solution was further optimized by hill climbing algorithm. The analysis of example showed that the improved GA algorithm had better performance in path optimization planning, the total cost of planning results was 31.44 % less than that of GA algorithm, and the total cost of planning results increased by 11.48 % considering the traffic network data. The experimental results show that the improved GA algorithm has good performance and can significantly reduce the cost of distribution and that research on VRP based on the traffic network data is more in line with the actual situation of logistics distribution, which is conducive to the further application of the improved GA algorithm in VRP.

Ключевые слова:
Traffic network data, logistics distribution, path optimization, genetic algorithm, time window.

Citation:
Li HH, Fu HR, Li WH. Analysis of logistics distribution path optimization planning based on traffic network data. Computer Optics 2021; 45(1): 154-160. DOI: 10.18287/2412-6179-CO-732.

Литература:

  1. Braekers K, Ramaekers K, Nieuwenhuyse IV. The vehicle routing problem: State of the art classification and review. Comput Ind Eng 2015; 99: 300-313.
  2. Xia C, Sheng Y, Jiang ZZ, Tan CQ, Huang M, He Y. A novel discrete differential evolution algorithm for the vehicle routing problem in B2C e-commerce. Int J Bifurcat Chaos 2015; 25(14): 1540033.
  3. Norouzi N, Sadegh-Amalnick M, Alinaghiyan M. Evaluating of the particle swarm optimization in a periodic vehicle routing problem. Measurement 2015; 62: 162-169.
  4. Yahyaoui H, Kaabachi I, Krichen S, Dekdouk A. Two metaheuristic approaches for solving the multi-compartment vehicle routing problem. Oper Res 2018(1): 1-24.
  5. Ezugwu AE, Akutsah F, Olusanya MO, Adewumi AO. Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem. PLOS ONE 2018; 13(3): e0193751.
  6. Qiu Y, Shi X. A System Dynamics Modeling Framework for Urban Logistics Demand System With a View to Society, Economy and Environment. LISS 2014: Proceedings of 4th International Conference on Logistics, Informatics and Service Science 2015: 299-303.
  7. Rose WJ, Mollenkopf DA, Autry CW, Bell J. Exploring urban institutional pressures on logistics service providers. Int J Phys Distrib Logist Manag 2016; 46(2): 153-176.
  8. Penna PHV, Afsar HM, Prins C, Prodhon C. A hybrid iterative local search algorithm for the electric fleet size and mix vehicle routing problem with time windows and recharging stations. IFAC PapersOnLine 2016; 49(12): 955-960.
  9. Papoutsis K, Nathanail E. Facilitating the selection of city logistics measures through a concrete measures package: A generic approach. Transportation Research Procedia 2016; 12(2016): 679-691.
  10. Qi MY, Wu T, Zhang X. Vehicle routing problem: From a perspective of time geography. Journal of Geo-Information Science 2015; 17(1): 22-30.
  11. Wang J, Weng T, Zhang Q. A two-stage multiobjective evolutionary algorithm for multiobjective multidepot vehicle routing problem with time windows. IEEE Trans Cybern 2019; 49(7): 2467-2478.
  12. Lei S, Liu W, Cai YH. A differential evolution algorithm based on self-adapting mountain-climbing operator. Appl Mech Mater 2012; 263-266: 2332-2338.
  13. Cherrett T, Dickinson J, McLeod F, Sit J, Bailey G, Whittle G. Logistics impacts of student online shopping – evaluating delivery consolidation to halls of residence. Transp Res Part C Emerg Technol 2017; 78: 111-128.
  14. Yue YX, Zhang T, Yue QX. Improved fractal space filling curves hybrid optimization algorithm for vehicle routing problem. Comput Intell Neurosci 2015; 2015(8): 375163.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: journal@computeroptics.ru ; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20