Cold Chain Logistics UAV Path Optimization Enlightenment

Authors

  • Hongxia Miao

DOI:

https://doi.org/10.54097/fbem.v4i1.410

Keywords:

Cold chain logistics, Target-task priority, Unbalanced data, UAV.

Abstract

As the market size of cold chain logistics in China grows year by year, consumers have significantly improved their requirements for the quality of cold chain items. Meanwhile, national laws and policies supporting the development of cold chain logistics have been introduced successively, which makes the field related to cold chain logistics develop rapidly. However, there are some problems in the development of the cold chains, such as waste of logistics data supervision and high cost of logistics distribution. Especially in the critical situation of the epidemic, the "last mile" delivery without contact is advocated, and the transportation time of cold-chain vehicles is long and the cost is high, which causes great resistance to solving the "last mile". This paper puts forward the corresponding enlightenment according to the relevant literature.

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References

Mena C, Terry L A, Williams A, et al. Causes of waste across multi-tier supply networks: Cases in the UK food sector[J]. International Journal of Production Economics, 2014, 152: 144-158. DOI: https://doi.org/10.1016/j.ijpe.2014.03.012

Kim K, Kim H, Kim S K, et al. i-RM: An intelligent risk management framework for context-aware ubiquitous cold chain logistics[J]. Expert Systems with Applications, 2016, 46: 463-473. DOI: https://doi.org/10.1016/j.eswa.2015.11.005

Wang L, Kwok S K, Ip W H. A radio frequency identification and sensor-based system for the transportation of food[J]. Journal of Food Engineering, 2010, 101(1): 120-129. DOI: https://doi.org/10.1016/j.jfoodeng.2010.06.020

Albrecht A, Ibald R, Raab V, et al. Implementation of time temperature indicators to improve temperature monitoring and support dynamic shelf life in meat supply chains[J]. Journal of Packaging Technology and Research, 2020, 4(1): 23-32. DOI: https://doi.org/10.1007/s41783-019-00080-x

Kramar V, Määttä H, Hinkula H, et al. Smart-fish system for fresh fish cold chain transportation—Overall approach and selection of sensor materials[C]//2017 21st Conference of Open Innovations Association (FRUCT). IEEE, 2017: 202-209. DOI: https://doi.org/10.23919/FRUCT.2017.8250183

Blackburn J, Scudder G. Supply chain strategies for perishable products: the case of fresh produce[J]. Production and Operations Management, 2009, 18(2): 129-137. DOI: https://doi.org/10.1111/j.1937-5956.2009.01016.x

Giannakourou M C, Taoukis P S. Application of a TTI‐based distribution management system for quality optimization of frozen vegetables at the consumer end[J]. Journal of food science, 2003, 68(1): 201-209. DOI: https://doi.org/10.1111/j.1365-2621.2003.tb14140.x

Zhao Y, Zhang X, Xu X. Application and research progress of cold storage technology in cold chain transportation and distribution[J]. Journal of Thermal Analysis and Calorimetry, 2020, 139(2): 1419-1434. DOI: https://doi.org/10.1007/s10973-019-08400-8

Qi L, Xu M, Fu Z, et al. C2SLDS: A WSN-based perishable food shelf-life prediction and LSFO strategy decision support system in cold chain logistics[J]. Food Control, 2014, 38: 19-29. DOI: https://doi.org/10.1016/j.foodcont.2013.09.023

Chen Y, Wu Q, Shao L. Urban cold-chain logistics demand predicting model based on improved neural network model[J]. International Journal of Metrology and Quality Engineering, 2020, 11: 5. DOI: https://doi.org/10.1051/ijmqe/2020003

Wang J, Wang H, He J, et al. Wireless sensor network for real-time perishable food supply chain management[J]. Computers and Electronics in Agriculture, 2015, 110: 196-207. DOI: https://doi.org/10.1016/j.compag.2014.11.009

Shavarani S M, Nejad M G, Rismanchian F, et al. Application of hierarchical facility location problem for optimization of a drone delivery system: a case study of Amazon prime air in the city of San Francisco[J]. The International Journal of Advanced Manufacturing Technology, 2018, 95(9): 3141-3153. DOI: https://doi.org/10.1007/s00170-017-1363-1

Sathyan A, Ernest N D, Cohen K. An efficient genetic fuzzy approach to UAV swarm routing[J]. Unmanned Systems, 2016, 4(02): 117-127. DOI: https://doi.org/10.1142/S2301385016500011

Sathyan A, Boone N, Cohen K. Comparison of approximate approaches to solving the travelling salesman problem and its application to UAV swarming[J]. International Journal of Unmanned Systems Engineering, 2015, 3(1): 1. DOI: https://doi.org/10.14323/ijuseng.2015.1

Singireddy S R, Daim T U. Technology roadmap: Drone delivery–amazon prime air[M]. Infrastructure and Technology Management. Cham: Springer, 2018: 387-412. DOI: https://doi.org/10.1007/978-3-319-68987-6_13

Wang D, Hu P, Du J, et al. Routing and scheduling for hybrid truck-drone collaborative parcel delivery with independent and truck-carried drones[J]. IEEE Internet of Things Journal, 2019, 6(6): 10483-10495. DOI: https://doi.org/10.1109/JIOT.2019.2939397

Rash W. UPS Tests Delivery Trucks Equipped With Drones as Efficiency Booster [M]. Retrieved from eWeek: http://www. eweek. com/mobile/ups-testsdelivery-trucks …. 2017.

Han H, Wang W Y, Mao B H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning[C]//International conference on intelligent computing. Springer, Berlin, Heidelberg, 2005: 878-887. DOI: https://doi.org/10.1007/11538059_91

Chen Y, Zhang R. Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network[J]. Complexity, 2021, 2021. DOI: https://doi.org/10.1155/2021/6618841

Sun J, Li H, Fujita H, et al. Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting[J]. Information Fusion, 2020, 54: 128-144. DOI: https://doi.org/10.1016/j.inffus.2019.07.006

Raghuwanshi B S, Shukia S. SMOTE based class-specific extreme learning machine for imbalanced learning[J]. Knowledge-Based Systems, 2020, 187: 104814. DOI: https://doi.org/10.1016/j.knosys.2019.06.022

Hyun C M, Kim H P, Lee S M, et al. Deep learning for undersampled MRI reconstruction[J]. Physics in Medicine & Biology, 2018, 63(13): 135007. DOI: https://doi.org/10.1088/1361-6560/aac71a

Erba V, Gherardi M, Rotondo P. Intrinsic dimension estimation for locally undersampled data[J]. Scientific reports, 2019, 9(1): 1-9. DOI: https://doi.org/10.1038/s41598-019-53549-9

Wang L, Han M, Li X, et al. Review of classification methods on unbalanced data sets[J]. IEEE Access, 2021, 9: 64606-64628. DOI: https://doi.org/10.1109/ACCESS.2021.3074243

Liang X, Jiang A, Li T, et al. LR-SMOTE—An improved unbalanced data set oversampling based on K-means and SVM[J]. Knowledge-Based Systems, 2020, 196: 105845. DOI: https://doi.org/10.1016/j.knosys.2020.105845

Zhang Z, Romero A, Muckley M, et al. Reducing uncertainty in undersampled MRI reconstruction with active acquisition[C]// Reducing uncertainty in undersampled MRI reconstruction with active acquisition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 2049-2058. DOI: https://doi.org/10.1109/CVPR.2019.00215

Gao X, Ren B, Zhang H, et al. An ensemble imbalanced classification method based on model dynamic selection driven by data partition hybrid sampling[J]. Expert Systems with Applications, 2020, 160: 113660. DOI: https://doi.org/10.1016/j.eswa.2020.113660

Liu C L, Chang Y H, Man,, Systems C. Learning From Imbalanced Data With Deep Density Hybrid Sampling[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022. DOI: https://doi.org/10.1109/TSMC.2022.3151394

Ahmed F, Kilic K. Fuzzy Analytic Hierarchy Process: A performance analysis of various algorithms[J]. Fuzzy Sets and Systems, 2019, 362: 110-128. DOI: https://doi.org/10.1016/j.fss.2018.08.009

Ahmed Z, Le Roux N, Norouzi M, et al. Understanding the impact of entropy on policy optimization[C]//International conference on machine learning. PMLR, 2019: 151-160.

Wu S X, Wai H T, Li L, et al. A review of distributed algorithms for principal component analysis[J]. Proceedings of the IEEE, 2018, 106(8): 1321-1340. DOI: https://doi.org/10.1109/JPROC.2018.2846568

Schreiber J B. Issues and recommendations for exploratory factor analysis and principal component analysis[J]. Research in Social and Administrative Pharmacy, 2021, 17(5): 1004-1011. DOI: https://doi.org/10.1016/j.sapharm.2020.07.027

Wang F, Lu Y, Li J, et al. Evaluating environmentally sustainable development based on the PSR framework and variable weigh analytic hierarchy process[J]. International Journal of Environmental Research and Public Health, 2021, 18(6): 2836. DOI: https://doi.org/10.3390/ijerph18062836

Zhu Y, Tian D, Yan F. Effectiveness of entropy weight method in decision-making[J]. Mathematical Problems in Engineering, 2020, 2020. DOI: https://doi.org/10.1155/2020/3564835

Gewers F L, Ferreira G R, Arruda H F D, et al. Principal component analysis: A natural approach to data exploration[J]. ACM Computing Surveys (CSUR), 2021, 54(4): 1-34. DOI: https://doi.org/10.1145/3447755

Shrestha N. Factor analysis as a tool for survey analysis[J]. American Journal of Applied Mathematics and Statistics, 2021, 9(1): 4-11. DOI: https://doi.org/10.12691/ajams-9-1-2

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Published

31-05-2022

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