A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
Y. Tang, Y. Wang, C. Liu, Q. Sui, Y. Liu, K. Huang, and W. Gui, “Data-driven two-stage robust optimization allocation and loading for salt lake chemical enterprise products under demand uncertainty,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 5, pp. 1–15, May 2025. doi: 10.1109/JAS.2025.125204
Citation: Y. Tang, Y. Wang, C. Liu, Q. Sui, Y. Liu, K. Huang, and W. Gui, “Data-driven two-stage robust optimization allocation and loading for salt lake chemical enterprise products under demand uncertainty,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 5, pp. 1–15, May 2025. doi: 10.1109/JAS.2025.125204
shu

Data-Driven Two-Stage Robust Optimization Allocation and Loading for Salt Lake Chemical Enterprise Products Under Demand Uncertainty

doi: 10.1109/JAS.2025.125204
Funds:  This work was supported in part by the National Natural Science Foundation of China (NSFC) (92267205) and the Fundamental Research Funds for the Central Universities of Central South University (2023ZZTS0305)
More Information
    Abstract
  • Most enterprises rely on railway transportation to deliver their products to customers, particularly in the salt lake chemical industry. Notably, allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes. However, due to demand fluctuations from changing product orders and unforeseen railway scheduling delays, manually adjusted allocation and loading may lead to excessive loading and unloading distances and times, ultimately increasing transportation costs for enterprises. To address these issues, this paper proposes a data-driven two-stage robust optimization (TSRO) framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism (GSTAC-AM), which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading. Specifically, GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers. Then, a robust counterpart model is formulated to ensure computational tractability. In addition, a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes. Finally, the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.

     

    • FullText(HTML)
  • loading
    • References(44)
  • [1]
    Q. Ma, P. Jia, and H. Kuang, “Green efficiency changes of comprehensive transportation in China: Technological change or technical efficiency change?” J. Cleaner Prod., vol. 304, p. 127115, Jul. 2021. doi: 10.1016/j.jclepro.2021.127115
    [2]
    H. K. Chan, J. Dai, X. Wang, and E. Lacka, “Logistics and supply chain innovation in the context of the Belt and Road Initiative (BRI),” Transp. Res. Part E: Logist. Transp. Rev., vol. 132, pp. 51–56, Dec. 2019. doi: 10.1016/j.tre.2019.10.009
    [3]
    R. Jia, S. Shao, and L. Yang, “High-speed rail and CO2 emissions in urban China: A spatial difference-in-differences approach,” Energy Econ., vol. 99, p. 105271, Jul. 2021. doi: 10.1016/j.eneco.2021.105271
    [4]
    J. Peng, L. Chen, and B. Zhang, “Transportation planning for sustainable supply chain network using big data technology,” Inf. Sci., vol. 609, pp. 781–798, Sep. 2022. doi: 10.1016/j.ins.2022.07.112
    [5]
    D. Chang, X. Huang, C. Wang, and N. Zhang, “Temperature shocks and low-carbon performance: Evidence from the transportation sector in China,” Transp. Res. Part D: Transport Environ., vol. 133, p. 104282, Aug. 2024. doi: 10.1016/j.trd.2024.104282
    [6]
    H. Song, S. Gao, Y. Li, L. Liu, and H. Dong, “Train-Centric communication based autonomous train control system,” IEEE Trans. Intell. Veh., vol. 8, no. 1, pp. 721–731, Jan. 2023. doi: 10.1109/TIV.2022.3192476
    [7]
    H. Song, L. Li, Y. Li, L. Tan, and H. Dong, “Functional safety and performance analysis of autonomous route management for autonomous train control system,” IEEE Trans. Intell. Transport. Syst., vol. 25, no. 10, pp. 13291–13304, Oct. 2024. doi: 10.1109/TITS.2024.3402435
    [8]
    Y. Wang, S. Li, C. Liu, K. Wang, X. Yuan, C. Yang, and W. Gui, “Multiscale feature fusion and semi-supervised temporal-spatial learning for performance monitoring in the flotation industrial process,” IEEE Trans. Cybern., vol. 54, no. 2, pp. 974–987, Feb. 2024. doi: 10.1109/TCYB.2023.3295852
    [9]
    D. Liu, Y. Wang, C. Liu, X. Yuan, C. Yang, and W. Gui, “Data mode related interpretable transformer network for predictive modeling and key sample analysis in industrial processes,” IEEE Trans. Ind. Inf., vol. 19, no. 9, pp. 9325–9336, Sep. 2023. doi: 10.1109/TII.2022.3227731
    [10]
    J. A. M. Santos, M. R. Lopes, J. L. Viegas, S. M. Vieira, and J. M. C. Sousa, “Internal supply chain digital twin of a pharmaceutical company,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 10797–10802, 2020. doi: 10.1016/j.ifacol.2020.12.2864
    [11]
    P. Kall and S. W. Wallace, Stochastic Programming. New York, USA: John Wiley & Sons Inc, 1994.
    [12]
    A. Ben-Tal and A. Nemirovski, “Robust solutions of Linear Programming problems contaminated with uncertain data,” Math. Program., vol. 88, no. 3, pp. 411–424, Sep. 2000. doi: 10.1007/PL00011380
    [13]
    P. Li, H. Arellano-Garcia, and G. Wozny, “Chance constrained programming approach to process optimization under uncertainty,” Comput. Chem. Eng., vol. 32, no. 1–2, pp. 25–45, Jan. , 2008. doi: 10.1016/j.compchemeng.2007.05.009
    [14]
    E. Kurtuluş, “Optimizing inland container logistics through dry ports: A two-stage stochastic mixed-integer programming approach considering volume discounts and consolidation in rail transport,” Comput. Ind. Eng., vol. 174, p. 108768, Dec. 2022. doi: 10.1016/j.cie.2022.108768
    [15]
    Y. Xu, M. Wang, K. K. Lai, and B. Ram, “A stochastic model for shipping container terminal storage management,” J. Mar. Sci. Eng., vol. 10, no. 10, p. 1429, Oct. 2022. doi: 10.3390/jmse10101429
    [16]
    Y. Karmanesh, M. Bagheri, R. Mohammad Hasany, and M. Saman Pishvaee, “Two-stage stochastic programming approach for fleet sizing and allocating rail wagon under uncertain demand,” Comput. Ind. Eng., vol. 188, p. 109878, Feb. 2024. doi: 10.1016/j.cie.2023.109878
    [17]
    A. L. Soyster, “Convex programming with set-inclusive constraints and applications to inexact linear programming,” Oper. Res., vol. 21, no. 5, pp. 1154–1157, Sep.-Oct. 1973. doi: 10.1287/opre.21.5.1154
    [18]
    M. Laguna, “A review of: “Robust discrete optimization and its applications” P. Kouvelis and G. Yu Kluwer Academic Publishers, 1997, 356 pp., ISBN 0-7923-4291- 7,” IIE Trans., vol. 32, no. 3, pp. 280–281, 2000. doi: 10.1080/07408170008963903
    [19]
    A. Ben-Tal, L. El Ghaoui, and A. Nemirovski, Robust Optimization. Princeton, USA: Princeton University Press, 2009, pp. 35–53.
    [20]
    H. Aissi, C. Bazgan, and D. Vanderpooten, “Complexity of the min-max and min-max regret assignment problems,” Oper. Res. Lett., vol. 33, no. 6, pp. 634–640, Nov. , 2005. doi: 10.1016/j.orl.2004.12.002
    [21]
    Q. Sui, Y. Wang, C. Liu, K. Huang, and C. Yang, “Interpretable switching deep Markov model for industrial process monitoring via cloud-edge collaborative framework,” IEEE Trans. Instrum. Meas., vol. 74, p. 3501614, 2025.
    [22]
    L. El Ghaoui, F. Oustry, and H. Lebret, “Robust solutions to uncertain semidefinite programs,” SIAM J. Optim., vol. 9, no. 1, pp. 33–52, Jan. 1998. doi: 10.1137/S1052623496305717
    [23]
    L. El Ghaoui and H. Lebret, “Robust solutions to least-squares problems with uncertain data,” SIAM J. Matrix Anal. Appl., vol. 18, no. 4, Oct. 1997.
    [24]
    D. Bertsimas and M. Sim, “The price of robustness,” Oper. Res., vol. 52, no. 1, pp. 35–53, Feb. 2004. doi: 10.1287/opre.1030.0065
    [25]
    Z. Li, R. Ding, and C. A. Floudas, “A comparative theoretical and computational study on robust counterpart optimization: I. Robust linear optimization and robust mixed integer linear optimization,” Ind. Eng. Chem. Res., vol. 50, no. 18, pp. 10567–10603, Sep. 2011. doi: 10.1021/ie200150p
    [26]
    T. Dokka, M. Goerigk, and R. Roy, “Mixed uncertainty sets for robust combinatorial optimization,” Optim. Lett., vol. 14, no. 6, pp. 1323–1337, Sep. 2020. doi: 10.1007/s11590-019-01456-3
    [27]
    C. Buchheim and J. Kurtz, “Robust combinatorial optimization under convex and discrete cost uncertainty,” EURO J. Comput. Optim., vol. 6, no. 3, pp. 211–238, Sep. 2018. doi: 10.1007/s13675-018-0103-0
    [28]
    N. Pourmohammad-Zia, F. Schulte, R. G. González-Ramírez, S. Voß, and R. R. Negenborn, “A robust optimization approach for platooning of automated ground vehicles in port hinterland corridors,” Comput. Ind. Eng., vol. 177, p. 109046, Mar. 2023. doi: 10.1016/j.cie.2023.109046
    [29]
    Z. Ziaei and A. Jabbarzadeh, “A multi-objective robust optimization approach for green location-routing planning of multi-modal transportation systems under uncertainty,” J. Cleaner Prod., vol. 291, p. 125293, Apr. 2021. doi: 10.1016/j.jclepro.2020.125293
    [30]
    A. Abbassi, A. E. H. Alaoui, and J. Boukachour, “Robust optimisation of the intermodal freight transport problem: Modeling and solving with an efficient hybrid approach,” J. Comput. Sci., vol. 30, pp. 127–142, Jan. 2019. doi: 10.1016/j.jocs.2018.12.001
    [31]
    S. Mohseni and M. S. Pishvaee, “Data-driven robust optimization for wastewater sludge-to-biodiesel supply chain design,” Comput. Ind. Eng., vol. 139, p. 105944, Jan. 2020. doi: 10.1016/j.cie.2019.07.001
    [32]
    Y. Cao, X. Zhu, and H. Yan, “Data-driven Wasserstein distributionally robust mitigation and recovery against random supply chain disruption,” Trans. Res. Part E: Logist. Transp. Rev., vol. 163, p. 102751, Jul. 2022. doi: 10.1016/j.tre.2022.102751
    [33]
    C. Ning and F. You, “Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods,” Comput. Chem. Eng., vol. 112, pp. 190–210, Apr. 2018. doi: 10.1016/j.compchemeng.2018.02.007
    [34]
    Q. Sui, Y. Wang, C. Liu, M. Han, and C. Yang, “Koopman-constrained hierarchical deep state space model for industrial quality prediction via cloud-edge collaborative framework,” IEEE Trans. Syst., Man, Cybern.: Syst., vol. 55, no. 2, pp. 1137–1150, Feb. 2025. doi: 10.1109/TSMC.2024.3495020
    [35]
    C. Liu, Y. Wang, Y. Fang, C. Yang, and W. Gui, “Operating condition recognition of industrial flotation processes using visual and acoustic bimodal autoencoder with manifold learning,” IEEE Trans. Ind. Inf., vol. 20, no. 5, pp. 7428–7439, May 2024. doi: 10.1109/TII.2024.3359416
    [36]
    F. Shen, L. Zhao, M. Wang, W. Du, and F. Qian, “Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty,” Appl. Energy, vol. 307, p. 118148, Feb. 2022. doi: 10.1016/j.apenergy.2021.118148
    [37]
    Y. Wang, Y. Tang, C. Liu, G. Wu, and W. Gui, “Two-stage hierarchical decision-making strategy for railway transportation freight stowage based on cooperative embedding optimization,” IEEE Trans. Intell. Veh., 2024, doi: 10.1109/TIV.2024.3446536.
    [38]
    X. Yang, Y. Li, Y. Zhao, Y. Yu, Y. Lian, G. Hao, and L. Jiang, “Data-driven nested robust optimization for generation maintenance scheduling considering temporal correlation,” Energy, vol. 278, p. 127499, Sep. 2023. doi: 10.1016/j.energy.2023.127499
    [39]
    X. Chen, C. Liu, B. Lin, J. Lai, and D. Miao, “AHA-3WKM: The optimization of K-means with three-way clustering and artificial hummingbird algorithm,” Inf. Sci., vol. 672, p. 120661, Jun. 2024. doi: 10.1016/j.ins.2024.120661
    [40]
    S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997. doi: 10.1162/neco.1997.9.8.1735
    [41]
    X. Yuan, B. Huang, Y. Wang, C. Yang, and W. Gui, “Deep learning-based feature representation and its application for soft sensor modeling with variable-wise weighted SAE,” IEEE Trans. Ind. Inf., vol. 14, no. 7, pp. 3235–3243, Jul. 2018. doi: 10.1109/TII.2018.2809730
    [42]
    A. S. Musleh, G. Chen, Z. Y. Dong, C. Wang, and S. Chen, “Attack detection in automatic generation control systems using LSTM-based stacked autoencoders,” IEEE Trans. Ind. Inf., vol. 19, no. 1, pp. 153–165, Jan. 2023. doi: 10.1109/TII.2022.3178418
    [43]
    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, “Attention is all you need,” in Proc. 31st Int. Conf. Neural Information Processing Systems, Long Beach, USA, 2017, pp. 6000–6010.
    [44]
    A. Ben-Tal, R. Brekelmans, D. Den Hertog, and J. P. Vial, “Globalized robust optimization for nonlinear uncertain inequalities,” INFORMS J. Comput., vol. 29, no. 2, pp. 350–366, Apr. 2017. doi: 10.1287/ijoc.2016.0735
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(11)  / Tables(6)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (13) PDF downloads(2) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return