Research of the Application of Cargo Volume Forecasting and Simulated Annealing Scheduling Optimization Based on Multi-model Integration in Logistics Centers

Xintao Xu; Kuiyan Men; Yingqiao Li; Chuansen Zhang; Hang Su

doi:10.62051/sp7das96

Authors

Xintao Xu
Kuiyan Men
Yingqiao Li
Chuansen Zhang
Hang Su

DOI:

https://doi.org/10.62051/sp7das96

Keywords:

the SARIMA model; XGBoost model; Simulated Annealing algorithm (SA); optimize resource allocation

Abstract

SARIMA model and the XGBoost model are both classical time series forecasting tools, while the simulated annealing algorithm (SA) is an excellent multi-objective planning method. By combining these algorithms, this study aims to forecast the cargo volume of a logistics center and arrange the scheduling of the logistics center accordingly. First, the SARIMA forecasting, and XGBoost hybrid correction model can more accurately predict the cargo volume of this logistics center for the next 30 days. Subsequently, the study will analyze and determine the decision variables and constraints in scheduling planning. Finally, the planning problem will be solved by SA, which enables the minimum number of personnel to meet cargo volume requirements, scheduling constraints, and other constraints. The results of this study have significant practical value, providing a replicable forecasting and planning framework for the dynamically changing market environment, which effectively helps enterprises to anticipate market changes and optimize resource allocation to enhance market competitiveness.

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References

[1] Song Ying-Hua,Xu Ya-An,Zhang Yuan-Jin. Research on seasonal PM2.5 prediction based on SARIMA-SVM model[J/OL]. Computer Engineering:1-11[2024-04-13]. https://doi.org/10.19678/j.issn.1000-3428.0068372.

[2] Xing Zhi-Wei, Li Xue-Zhe, Luo Qian, et al. Airport cargo volume prediction based on SARIMA and RBF neural network[J]. Journal of Civil Aviation University of China,2016,34(05):51-55.

[3] Wang Ying, Han Bao-Ming, Zhang Qi, et al. Beijing subway inbound passenger flow prediction based on SARIMA model[J]. Transportation Systems Engineering and Information,2015,15(06):205-211.DOI:10.16097/j.cnki.1009-6744.2015.06.031.

[4] Zheng-G. Container cargo volume prediction based on gray model[J]. China Navigation,2014,37(02):118-121.

[5] Li Zhan-Shan, Liu Zhao-Gang. Feature selection algorithm based on XGBoost[J]. Journal of Communication,2019,40(10):101-108.

[6] Ye Qian-Yi. Research on physical retail sales forecasting based on Xgboost method[D]. Nanchang University,2016.

[7] Chen Xiao-Hong, Hú Wen-Hua, Cao Yu,et al. Application of hierarchical multi-objective linear programming model based on trapezoidal fuzzy number in multi-attribute uncertain decision-making problems[J]. Journal of Management Engineering, 2012, 26(4):7. DOI:10.3969/j.issn.1004-6062.2012.04.026.

[8] Luo Pei-Wen, Xiong Xiao-Yu, Liu Bing-Lin. Forest carbon sequestration analysis and multi-objective planning management strategy based on machine learning and growth prediction[J]. Science and Technology Wind,2023,(19):156-159.DOI:10.19392/j.cnki.1671-7341.202319052.

[9] Zhao D. Research on site selection of highway emergency logistics center based on network robustness and multi-objective optimization model [D]. Shijiazhuang Railway University,2024.DOI:10.27334/d.cnki.gstdy.2024.000022.

[10] Tao Sha, Liu Yang, Zhou Jing. Integrated optimization of project scheduling and staffing considering quality transfer[J]. Journal of Systems Management,2024,33(02):356-367.

[11] Wu Yao-Hua, Wang Sheng-Li, Wang Chang-Xiang. Application of queuing theory in logistics planning[J]. Logistics Science and Technology,2004, (05):53-55.

[12] Yi H. Research on enterprise logistics cost control based on competitive strategy [D]. Beijing Jiaotong University,2012.