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Optimization of regional emergency supplies distribution vehicle route with dynamic real-time demand


  • Received: 14 December 2022 Revised: 06 February 2023 Accepted: 12 February 2023 Published: 16 February 2023
  • Given the particular characteristics of a sudden outbreak of an epidemic on a regional scale and considering the possible existence of a latent period process, this paper takes the distribution of regional emergency supplies as the research object. Form the proposes a dynamic vehicle path problem from the perspective of real-time demand changes. First, when there is a sudden outbreak of a small-scale epidemic, there is uncertainty about demand in the epidemic area. The objective functions of minimizing the vehicle travel route cost of emergency vehicles, the late arrival penalty cost of emergency vehicles, and the fixed cost of emergency vehicles, as well as the objective function of minimizing the total distance traveled by vehicles, are established. Second, a mathematical model of the dynamic real-time demand vehicle route problem is built using the actual vehicle routing problem as a basis. The model is then solved using the SFSSA method. Finally, the computational results demonstrate that the SFSSA algorithm can effectively reduce transportation cost and distance when solving the constructed mathematical model problem, providing a solution to the problem of optimizing the route of emergency material distribution vehicles for a regional scale.

    Citation: Xiangyang Ren, Shuai Chen, Liyuan Ren. Optimization of regional emergency supplies distribution vehicle route with dynamic real-time demand[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 7487-7518. doi: 10.3934/mbe.2023324

    Related Papers:

  • Given the particular characteristics of a sudden outbreak of an epidemic on a regional scale and considering the possible existence of a latent period process, this paper takes the distribution of regional emergency supplies as the research object. Form the proposes a dynamic vehicle path problem from the perspective of real-time demand changes. First, when there is a sudden outbreak of a small-scale epidemic, there is uncertainty about demand in the epidemic area. The objective functions of minimizing the vehicle travel route cost of emergency vehicles, the late arrival penalty cost of emergency vehicles, and the fixed cost of emergency vehicles, as well as the objective function of minimizing the total distance traveled by vehicles, are established. Second, a mathematical model of the dynamic real-time demand vehicle route problem is built using the actual vehicle routing problem as a basis. The model is then solved using the SFSSA method. Finally, the computational results demonstrate that the SFSSA algorithm can effectively reduce transportation cost and distance when solving the constructed mathematical model problem, providing a solution to the problem of optimizing the route of emergency material distribution vehicles for a regional scale.



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    [1] Q. Jia, Y. Guo, G. L. Wang, S. J. Barnes, Big data analytics in the fight against major public health incidents (Including COVID-19): a conceptual framework, Int. J. Environ. Res. Public Health, 17 (2020), 6161. doi: 10.3390/ijerph17176161 doi: 10.3390/ijerph17176161
    [2] Q. G. Zhu, Impact of the COVID-19 pandemic on major world economies and China's countermeasures, J. Shanghai Jiaotong Univ., 28 (2020), 87–99. doi: 10.13806/j.cnki.issn1008-7095.2020.05.009 doi: 10.13806/j.cnki.issn1008-7095.2020.05.009
    [3] J. C. Jiang, Q. Q. Li, L. X. Wu, W. Tu, Multi-objective emergency material vehicle dispatching and routing under dynamic constraints in an earthquake disaster environment, ISPRS Int. J. Geo-Inf., 6 (2017), 142. doi: 10.3390/ijgi6050142 doi: 10.3390/ijgi6050142
    [4] X. Y. Ren, S. Chen, K. Y. Wang, J. Tan, Design and application of improved sparrow search algorithm based on sine cosine and firefly perturbation, Math. Biosci. Eng., 19 (2022) 11422–11452. doi: 10.3934/mbe.2022533 doi: 10.3934/mbe.2022533
    [5] G. B. Dantzig, J. H. Ramser, The truck dispatching problem, Manage. Sci., 6 (1959), 80–91. doi: 10.1287/mnsc.6.1.80 doi: 10.1287/mnsc.6.1.80
    [6] M. Alweshah, M. Almiani, N. Almansour, S. A. Khalaileh, H. Aldabbas, W. Alomoush, et al., Vehicle routing problems based on harris hawks optimization, J. Big Data, 9 (2022). doi: 10.1186/s40537-022-00593-4 doi: 10.1186/s40537-022-00593-4
    [7] X. N. Zhang, L. Jiang, C. Y. Liang, J. F. Dong, W. X. Lu, N. Mladenovic, Optimization approaches for the urban delivery problem with trucks and drones, Swarm Evol. Comput., 75 (2022), 101147. doi: 10.1016/j.swevo.2022.101147 doi: 10.1016/j.swevo.2022.101147
    [8] I. Kucukoglu, R. Dewil, D. Cattrysse, The electric vehicle routing problem and its variations: A literature review, Comput. Ind. Eng., 161 (2021), 107650. doi: 10.1016/j.cie.2021.107650 doi: 10.1016/j.cie.2021.107650
    [9] R. Moghdani, K. Salimifard, E. Demir, A. Benyettou, The green vehicle routing problem: A systematic literature review, J. Cleaner Prod., 279 (2020), 123691. doi: 10.1016/j.jclepro.2020.123691 doi: 10.1016/j.jclepro.2020.123691
    [10] M. A. Nguyen, G. T. H. Dang, M. H. Hà, M. T. Pham, The min-cost parallel drone scheduling vehicle routing problem, Eur. J. Oper. Res., 299 (2022), 910–930. doi: 10.1016/j.ejor.2021.07.008 doi: 10.1016/j.ejor.2021.07.008
    [11] R. P. Hornstra, A. Silva, K. J. Roodbergen, L. C. Coelho, The vehicle routing problem with simultaneous pickup and delivery and handling costs, Comput. Oper. Res., 115 (2020), 104858. doi: 10.1016/j.cor.2019.104858 doi: 10.1016/j.cor.2019.104858
    [12] Y. K. Xia, Z. Fu, L. J. Pan, F. H. Duan, Tabu search algorithm for the distance-constrained vehicle routing problem with split deliveries by order, PLOS ONE, 13 (2018), e0195457. doi: 10.1371/journal.pone.0195457 doi: 10.1371/journal.pone.0195457
    [13] H. S. Liu, Y. X. Sun, N. Pan, Y. Li, Y. Q. An, D. L. Pan, Study on the optimization of urban emergency supplies distribution paths for epidemic outbreaks, Comput. Oper. Res., 146 (2022), 105912. doi: 10.1016/j.cor.2022.105912 doi: 10.1016/j.cor.2022.105912
    [14] B. H. O. Rios, E. C. Xavier, F. K. Miyazawa, P. Amorim, E. Curcio, M. J. Santos, Recent dynamic vehicle routing problems: A survey, Comput. Ind. Eng., 160 (2021), 107604. doi: 10.1016/j.cie.2021.107604 doi: 10.1016/j.cie.2021.107604
    [15] N. R. Sabar, S. L. Goh, A. Turky, G. Kendall, Population-based iterated local search approach for dynamic vehicle routing problems, IEEE Trans. Autom. Sci. Eng., 30 (2021). doi: 10.1109/tase.2021.3097778
    [16] Y. Li, H. M. Fan, X. N. Zhang, A periodic optimization model and solution for capacitated vehicle routing problems with dynamic requests, Chin. J. Manage. Sci., 30 (2022), 254–266. doi: 10.16381/j.cnki.issn1003-207x.2019.1495 doi: 10.16381/j.cnki.issn1003-207x.2019.1495
    [17] X. S. Xiang, J. F. Qiu, J. H. Xiao, X. Y. Zhang, Demand coverage diversity based ant colony optimization for dynamic vehicle routing problems, Eng. Appl. Artif. Intell., 91 (2020), 103582. doi: 10.1016/j.engappai.2020.103582 doi: 10.1016/j.engappai.2020.103582
    [18] R. RamachandranPillai, M. Arock, Spiking neural firefly optimization scheme for the capacitated dynamic vehicle routing problem with time windows, Neural Comput. Appl., 33 (2021), 409–432. doi: 10.1007/s00521-020-04983-8 doi: 10.1007/s00521-020-04983-8
    [19] H. W. Jiang, T. Guo, Z. Yang, L. K. Zhao, Deep reinforcement learning algorithm for solving material emergency dispatching problem, Math. Biosci. Eng., 19 (2022) 10864–10881. doi: 10.3934/mbe.2022508
    [20] J. Q. Fang, H. P. Hou, C. X. Lu, H. Y. Pang, Q. S. Deng, Y. Ye, et al., A new scheduling method based on sequential time windows developed to distribute first-aid medicine for emergency logistics following an earthquake, PLOS ONE, 16 (2021), e0247556. doi: 10.1371/journal.pone.0247566 doi: 10.1371/journal.pone.0247566
    [21] L. J. Du, X. H. Li, Y. Gan, K. J. Leng, Optimal model and algorithm of medical materials delivery drone routing problem under major public health emergencies, Sustainability, 14 (2022), 4651. doi: 10.3390/su14084651 doi: 10.3390/su14084651
    [22] J. A. Espejo-Díaz, W. J. Guerrero, A multiagent approach to solving the dynamic postdisaster relief distribution problem, Oper. Manage. Res., 14 (2021), 177–193. doi: 10.1007/s12063-021-00192-1 doi: 10.1007/s12063-021-00192-1
    [23] Y. Suzuki, Impact of material convergence on last-mile distribution in humanitarian logistics, Int. J. Prod. Econ., 223 (2020), 107515. doi: 10.1016/j.ijpe.2019.107515 doi: 10.1016/j.ijpe.2019.107515
    [24] B. K. Mishra, K. Dahal, Z. Pervez, Dynamic relief items distribution model with sliding time window in the post-disaster environment, Appl. Sci., 12 (2022), 8358. doi: 10.3390/app12168358 doi: 10.3390/app12168358
    [25] J. Ochelska-Mierzejewska, A. Poniszewska-Marańda, W. Marańda, Selected genetic algorithms for vehicle routing problem solving, Electronics, 10 (2021), 3147. doi: 10.3390/electronics10243147 doi: 10.3390/electronics10243147
    [26] Y. B. Li, H. Soleimani, M. Zohal, An improved ant colony optimization algorithm for the multi-depot green vehicle routing problem with multiple objectives, J. Cleaner Prod., 227 (2019), 1161–1172. doi: 10.1016/j.asoc.2021.107655 doi: 10.1016/j.asoc.2021.107655
    [27] M. A. Islam, Y. Gajpal, T. Y. ElMekkawy, Hybrid particle swarm optimization algorithm for solving the clustered vehicle routing problem, Appl. Soft Comput., 110 (2021), 107655. doi: 10.1016/j.asoc.2021.107655 doi: 10.1016/j.asoc.2021.107655
    [28] J. K. Xue, B. Shen, A novel swarm intelligence optimization approach: sparrow search algorithm, Syst. Sci. Control Eng., 8 (2020), 22–34. doi: 10.1080/21642583.2019.1708830 doi: 10.1080/21642583.2019.1708830
    [29] Y. X. Duan, C. Y. Liu, Sparrow search algorithm based on Sobol sequence and crisscross strategy, J. Comput. Appl., 42 (2022), 36–43. doi: 10.11772/j.issn.1001-9081.2021010187 doi: 10.11772/j.issn.1001-9081.2021010187
    [30] L. F. Yue, R. N. Yang, Y. J. Zhang, Y. Yu, Z. X. Zhang, Tent chaos and simulated annealing improved moth-flame optimization algorithm, J. Harbin Inst. Technol., 51 (2019), 146–154. doi: 10.11918/j.issn.0367-6234.201811027 doi: 10.11918/j.issn.0367-6234.201811027
    [31] L. Abualigah, A. Diabat, Advances in sine cosine algorithm: A comprehensive survey, Artif. Intell. Rev., 54 (2021), 2567–2608. doi: 10.1007/s10462-020-09909-3 doi: 10.1007/s10462-020-09909-3
    [32] C. Gan, W. H. Cao, M. Wu, X. Chen, A new bat algorithm based on iterative local search and stochastic inertia weight, Expert Syst. Appl., 104 (2018), 202–212. doi: 10.1016/j.eswa.2018.03.015 doi: 10.1016/j.eswa.2018.03.015
    [33] J. Q. Wang, M. X. Zhang, H. H. Song, Z. W. Cheng, T. Z. Chang, Y. S. Bi, et al., Improvement and application of hybrid firefly algorithm, IEEE Access, 7 (2019), 165458–165477. doi: 10.1109/access.2019.2952468 doi: 10.1109/access.2019.2952468
    [34] X. Lv, X. D. Mu, J. Zhang, Z. Wang, Chaos sparrow search optimization algorithm, J. Beijing Univ. Aeronau. Astronaut., 47 (2020), 1–10. doi: 10.13700/j.bh.1001-5965.2020.0298 doi: 10.13700/j.bh.1001-5965.2020.0298
    [35] C. L. Zhang, S. F. Ding, A stochastic configuration network based on chaotic sparrow search algorithm, Knowl. Based Syst., 220 (2021), 106924. doi: 10.1016/j.knosys.2021.106924 doi: 10.1016/j.knosys.2021.106924
    [36] G. Yu, H. Wang, H. Z. Zhou, S. S. Zhao, Y. Wang, An efficient firefly algorithm based on modified search strategy and neighborhood attraction, Int. J. Intell. Syst., 36 (2021), 4346–4363. doi: 10.1002/int.22462 doi: 10.1002/int.22462
    [37] M. M. Solomon, Algorithms for the vehicle routing and scheduling problems with time window constraints, Oper. Res., 35 (1987), 254–265. doi:10.1287/opre.35.2.254 doi: 10.1287/opre.35.2.254
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