The ongoing emergence of COVID-19 and the maturation of cold chain technology, have aided in the rapid development of the fresh produce e-commerce industry. Taking into account the characteristics of consumers' demand for fresh products, this paper constructs a location allocation model of a front warehouse for fresh e-commerce with the objective of minimizing the total cost. An improved immune optimization algorithm is proposed in this paper, and the effectiveness of the proposed algorithm is demonstrated by a real case study. The results show that the improved immune optimization algorithm outperforms the traditional genetic algorithm in terms of solution accuracy; the proposed location model can effectively help fresh produce e-commerce enterprises open new front-end warehouses when demand is increasing, as well as provide optimal economic decision-making for front warehouse layout.
Citation: Dezheng Zhang, Shuai Chen, Na Zhou, Pu Shi. Location optimization of fresh food e-commerce front warehouse[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14899-14919. doi: 10.3934/mbe.2023667
[1] | Hamed Karami, Pejman Sanaei, Alexandra Smirnova . Balancing mitigation strategies for viral outbreaks. Mathematical Biosciences and Engineering, 2024, 21(12): 7650-7687. doi: 10.3934/mbe.2024337 |
[2] | Pannathon Kreabkhontho, Watchara Teparos, Thitiya Theparod . Potential for eliminating COVID-19 in Thailand through third-dose vaccination: A modeling approach. Mathematical Biosciences and Engineering, 2024, 21(8): 6807-6828. doi: 10.3934/mbe.2024298 |
[3] | Holly Gaff, Elsa Schaefer . Optimal control applied to vaccination and treatment strategies for various epidemiological models. Mathematical Biosciences and Engineering, 2009, 6(3): 469-492. doi: 10.3934/mbe.2009.6.469 |
[4] | Stefanie Fuderer, Christina Kuttler, Michael Hoelscher, Ludwig Christian Hinske, Noemi Castelletti . Data suggested hospitalization as critical indicator of the severity of the COVID-19 pandemic, even at its early stages. Mathematical Biosciences and Engineering, 2023, 20(6): 10304-10338. doi: 10.3934/mbe.2023452 |
[5] | Vinicius Piccirillo . COVID-19 pandemic control using restrictions and vaccination. Mathematical Biosciences and Engineering, 2022, 19(2): 1355-1372. doi: 10.3934/mbe.2022062 |
[6] | ZongWang, Qimin Zhang, Xining Li . Markovian switching for near-optimal control of a stochastic SIV epidemic model. Mathematical Biosciences and Engineering, 2019, 16(3): 1348-1375. doi: 10.3934/mbe.2019066 |
[7] | Zi Sang, Zhipeng Qiu, Xiefei Yan, Yun Zou . Assessing the effect of non-pharmaceutical interventions on containing an emerging disease. Mathematical Biosciences and Engineering, 2012, 9(1): 147-164. doi: 10.3934/mbe.2012.9.147 |
[8] | Majid Jaberi-Douraki, Seyed M. Moghadas . Optimal control of vaccination dynamics during an influenza epidemic. Mathematical Biosciences and Engineering, 2014, 11(5): 1045-1063. doi: 10.3934/mbe.2014.11.1045 |
[9] | Lili Liu, Xi Wang, Yazhi Li . Mathematical analysis and optimal control of an epidemic model with vaccination and different infectivity. Mathematical Biosciences and Engineering, 2023, 20(12): 20914-20938. doi: 10.3934/mbe.2023925 |
[10] | Eunha Shim . Optimal strategies of social distancing and vaccination against seasonal influenza. Mathematical Biosciences and Engineering, 2013, 10(5&6): 1615-1634. doi: 10.3934/mbe.2013.10.1615 |
The ongoing emergence of COVID-19 and the maturation of cold chain technology, have aided in the rapid development of the fresh produce e-commerce industry. Taking into account the characteristics of consumers' demand for fresh products, this paper constructs a location allocation model of a front warehouse for fresh e-commerce with the objective of minimizing the total cost. An improved immune optimization algorithm is proposed in this paper, and the effectiveness of the proposed algorithm is demonstrated by a real case study. The results show that the improved immune optimization algorithm outperforms the traditional genetic algorithm in terms of solution accuracy; the proposed location model can effectively help fresh produce e-commerce enterprises open new front-end warehouses when demand is increasing, as well as provide optimal economic decision-making for front warehouse layout.
[1] |
J. Li, X. Xiang, X. Tao, Research of the operational mechanism of the fresh food e-commerce supply chain and "aricultural and supermarket docking" mode in China, Sci. Res., 4 (2016), 55. https://doi.org/10.11648/j.sr.20160402.16 doi: 10.11648/j.sr.20160402.16
![]() |
[2] |
B. Meng, X. Zhang, W. Hua, L. Liu, K. Ma, Development and application of phase change material in fresh e-commerce cold chain logistics: A review, J. Energy Storage, 55 (2022). https://doi.org/10.1016/J.EST.2022.105373 doi: 10.1016/J.EST.2022.105373
![]() |
[3] |
M. Yu, A. Nagurney, Competitive food supply chain networks with application to fresh produce, Eur. J. Oper. Res., 224 (2013), 273–282. https://doi.org/10.1016/j.ejor.2012.07.033 doi: 10.1016/j.ejor.2012.07.033
![]() |
[4] | T. Zheng, F. Li, Research on the development of store-warehouse integration, front-warehouse and community-group mode under the new retail situation, in Proceedings of 2020 International Conference on World Economy and Project Management (WEPM 2020), (2020), 4. https://doi.org/10.26914/c.cnkihy.2020.004796 |
[5] |
Q. Wu, Research on the influence of logistics service quality on consumers' repeated purchase willingness under the group-buying mode of agricultural products community, Acad. J. Bus. Manage., 4 (2022), 40–49. https://doi.org/10.25236/AJBM.2022.041906 doi: 10.25236/AJBM.2022.041906
![]() |
[6] |
Y. Jiang, P. Lai, C. H. Chang, K. F. Yuen, S. Li, X. Wang, Sustainable management for fresh food e-commerce logistics services, Sustainability, 13 (2021), 3456. https://doi.org/10.3390/SU13063456 doi: 10.3390/SU13063456
![]() |
[7] | F. Wan, J. Qin, X. Wang, Location selection of fresh e-commerce's front warehouse under new retail model, in 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022) 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022), 2022. https://doi.org/10.2991/978-94-6463-010-7_83 |
[8] |
M. Nekutova, L. Svadlenka, N. Kudlackova, Warehouse location problem as a strategical and operative logistic decision, Appl. Mech. Materials, 803 (2015), 40–46. https://doi.org/10.4028/www.scientific.net/AMM.803.40 doi: 10.4028/www.scientific.net/AMM.803.40
![]() |
[9] |
M. Momenitabar, Z. D. Ebrahimi, M. Arani, J. Mattson, P. Ghasemi, Designing a sustainable closed-loop supply chain network considering lateral resupply and backup suppliers using fuzzy inference system, Environ. Dev. Sustainability, 2022 (2022), 1–34. https://doi.org/10.1007/S10668-022-02332-4 doi: 10.1007/S10668-022-02332-4
![]() |
[10] |
G. Fariba, K. Vikas, G. Peiman, Investigating a citrus fruit supply chain network considering CO2 emissions using meta-heuristic algorithms, Ann. Oper. Res., 2022 (2022), 1–55. https://doi.org/10.1007/s10479-022-05005-7 doi: 10.1007/s10479-022-05005-7
![]() |
[11] |
M. Mohsen, D. E. Zhila, G. Peiman, Designing a sustainable bioethanol supply chain network: A combination of machine learning and meta-heuristic algorithms, Ind. Crops Products, 189 (2022), 115848. https://doi.org/10.1016/J.INDCROP.2022.115848 doi: 10.1016/J.INDCROP.2022.115848
![]() |
[12] |
Y. Wang, J. Zhang, X. Guan, M. Xu, Z. Wang, H. Wang, Collaborative multiple centers fresh logistics distribution network optimization with resource sharing and temperature control constraints, Expert Syst. Appl., 165 (2021), 113838. https://doi.org/10.1016/j.eswa.2020.113838 doi: 10.1016/j.eswa.2020.113838
![]() |
[13] |
K. Govindan, A. Jafarian, R. Khodaverdi, K. Devika, Two-echelon multiple-vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food, Int. J. Prod. Econ., 152 (2014), 9–28. https://doi.org/10.1016/j.ijpe.2013.12.028 doi: 10.1016/j.ijpe.2013.12.028
![]() |
[14] |
S. Wang, F. Tao, Y. Shi, Optimization of location–routing problem for cold chain logistics considering carbon footprint, Int. J. Environ. Res. Public Health, 15 (2018), 86. https://doi.org/10.3390/ijerph15010086 doi: 10.3390/ijerph15010086
![]() |
[15] |
H. Wang, H. Ran, X. Dang, Location optimization of fresh agricultural products cold chain distribution center under carbon emission constraints, Sustainability, 14 (2022), 6726. https://doi.org/10.3390/SU14116726 doi: 10.3390/SU14116726
![]() |
[16] |
S. Liu, Multimodal transportation route optimization of cold chain container in time-varying network considering carbon emissions, Sustainability, 15 (2023), 4435. https://doi.org/10.3390/su15054435 doi: 10.3390/su15054435
![]() |
[17] |
N. M. M. Torre, V. A. P. Salomon, E. Loche, S. A. Gazale, V. M. Palermo, Warehouse location for product distribution by e-commerce in Brazil: Comparing symmetrical MCDM applications, Symmetry, 14 (2022), 1987. https://doi.org/10.3390/SYM14101987 doi: 10.3390/SYM14101987
![]() |
[18] |
X. Wang, Location selection of marine product E-commerce distribution centers based on effective covering model, J. Coastal Res., 110 (2020), 15–19. https://doi.org/10.2112/JCR-SI110-004.1 doi: 10.2112/JCR-SI110-004.1
![]() |
[19] |
A. Ahmadi-Javid, E. Amiri, M. Meskar, A profit-maximization location-routing-pricing problem: A branch-and-price algorithm, Eur. J. Oper. Res., 271 (2018), 866–881. https://doi.org/10.1016/j.ejor.2018.02.020 doi: 10.1016/j.ejor.2018.02.020
![]() |
[20] |
R. Macedo, C. Alves, S. Hanafi, B. Jarboui, N. Mladenović, B. Ramos, et al., Skewed general variable neighborhood search for the location routing scheduling problem, Comput. Oper. Res., 61 (2015), 143–152. https://doi.org/10.1016/j.cor.2015.03.011 doi: 10.1016/j.cor.2015.03.011
![]() |
[21] |
Y. Zhang, Logistics distribution scheduling model of supply chain based on genetic algorithm, J. Ind. Prod. Eng., 39 (2022), 83–88. https://doi.org/10.1080/21681015.2021.1958938 doi: 10.1080/21681015.2021.1958938
![]() |
[22] |
S. A. Torabi, I. Shokr, S. Tofighi, J. Heydari, Integrated relief pre-positioning and procurement planning in humanitarian supply chains, Transp. Res. Part E, 113 (2018), 123–146. https://doi.org/10.1016/j.tre.2018.03.012 doi: 10.1016/j.tre.2018.03.012
![]() |
[23] |
A. Pagès‐Bernaus, H. Ramalhinho, A. A. Juan, L. Calvet, Designing e-commerce supply chains: A stochastic facility-location approach, Int. Tran. Oper. Res., 26 (2019), 507–528. https://doi.org/10.1111/itor.12433 doi: 10.1111/itor.12433
![]() |
[24] |
C. Erdin, H. E. Akbaş, A comparative analysis of fuzzy TOPSIS and geographic information systems (GIS) for the location selection of shopping malls: A case study from Turkey, Sustainability, 11 (2019), 3837. https://doi.org/10.3390/su11143837 doi: 10.3390/su11143837
![]() |
[25] |
A. Silva, D. Aloise, L. C. Coelho, C. Rocha, Heuristics for the dynamic facility location problem with modular capacities, Eur. J. Oper. Res., 290 (2020), 435–452. https://doi.org/10.1016/j.ejor.2020.08.018 doi: 10.1016/j.ejor.2020.08.018
![]() |
[26] |
H. Zhang, K. Zhang, Y. Zhou, L. Ma, Z. Zhang, An immune algorithm for solving the optimization problem of locating the battery swapping stations, Knowl.-Based Syst., 248 (2022). https://doi.org/10.1016/J.KNOSYS.2022.108883 doi: 10.1016/J.KNOSYS.2022.108883
![]() |
[27] |
R. Shang, W. Zhang, F. Li, L. Jiao, R. Stolkin, Multi-objective artificial immune algorithm for fuzzy clustering based on multiple kernels, Swarm Evol. Comput., 50 (2019). https://doi.org/10.1016/j.swevo.2019.01.001 doi: 10.1016/j.swevo.2019.01.001
![]() |
1. | Inkyung Ahn, Seongman Heo, Seunghyun Ji, Kyung Hyun Kim, Taehwan Kim, Eun Joo Lee, Jooyoung Park, Keehoon Sung, Investigation of nonlinear epidemiological models for analyzing and controlling the MERS outbreak in Korea, 2018, 437, 00225193, 17, 10.1016/j.jtbi.2017.10.004 | |
2. | Carl-Etienne Juneau, Tomas Pueyo, Matt Bell, Genevieve Gee, Pablo Collazzo, Louise Potvin, Lessons from past pandemics: a systematic review of evidence-based, cost-effective interventions to suppress COVID-19, 2022, 11, 2046-4053, 10.1186/s13643-022-01958-9 |