Abstract
Cargo distribution is one of most critical issues for steel logistics industry, whose core task is to determine cargo loading plan for each truck. Due to cargos far outnumber available transport capacity in steel logistics industry, traditional policies treat all cargos equally and distribute them to each arrived trucks with the aim of maximizing the load for each truck. However, they ignore timely delivering high-priority cargos, which causes a great loss to the profit of the steel enterprise. In this paper, we first bring forward a data-driven cargo loading plan decision framework based on the target of high-priority cargo delivery maximization, called as ALPD. To be specific, through analyzing historical steel logistics data, some significant limiting rules related to loading plan decision process are extracted. Then a two-step online decision mechanism is designed to achieve optimal cargo loading plan decision in each time period. It consists of genetic algorithm-based loading plan generation and breadth first traversal-based loading plan path searching. Furthermore, adaptive time window based solution is introduced to address the issue of low decision efficiency brought by uneven distribution of number of arrived trucks within different time periods. Extensive experimental results on real steel logistics data generated from Rizhao Steel’s logistics platform validate the effectiveness and practicality of our proposal.
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References
Besse, P.C., Guillouet, B., Loubes, J., Royer, F.: Destination prediction by trajectory distribution-based model. Trans. Intell. Transp. Syst. 19(8), 2470–2481 (2018)
Chen, Y., Lv, P., Guo, D., Zhou, T., Xu, M.: A survey on task and participant matching in mobile crowd sensing. J. Comput. Sci. Technol. 33(4), 768–791 (2018)
Chen, Y., et al.: Can sophisticated dispatching strategy acquired by reinforcement learning? In: AAMAS, pp. 1395–1403 (2019)
Epstein, R., et al.: A strategic empty container logistics optimization in a major shipping company. Interfaces 42(1), 5–16 (2012)
Geng, X., et al.: Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In: AAAI, pp. 3656–3663 (2019)
Kuhn, H.W.: The hungarian method for the assignment problem. Naval Res. Logist. Q. 2(1–2), 83–97 (1955)
Li, M., Fan, S., Luo, A.: A partheno-genetic algorithm for combinatorial optimization. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 224–229. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30499-9_33
Li, X., Zhang, J., Bian, J., Tong, Y., Liu, T.: A cooperative multi-agent reinforcement learning framework for resource balancing in complex logistics network. In: AAMAS, pp. 980–988 (2019)
Lin, K., Zhao, R., Xu, Z., Zhou, J.: Efficient large-scale fleet management via multi-agent deep reinforcement learning. In: SIGKDD, pp. 1774–1783 (2018)
Tong, Y., et al.: The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms. In: SIGKDD, pp. 1653–1662 (2017)
Tong, Y., She, J., Ding, B., Chen, L., Wo, T., Xu, K.: Online minimum matching in real-time spatial data: experiments and analysis. PVLDB 9(12), 1053–1064 (2016)
Tong, Y., She, J., Ding, B., Wang, L., Chen, L.: Online mobile micro-task allocation in spatial crowdsourcing. In: ICDE, pp. 49–60 (2016)
Tong, Y., et al.: Flexible online task assignment in real-time spatial data. PVLDB 10(11), 1334–1345 (2017)
Wang, Y., Tong, Y., Long, C., Xu, P., Xu, K., Lv, W.: Adaptive dynamic bipartite graph matching: a reinforcement learning approach. In: ICDE, pp. 1478–1489 (2019)
Wang, Z., Qin, Z., Tang, X., Ye, J., Zhu, H.: Deep reinforcement learning with knowledge transfer for online rides order dispatching. In: ICDM, pp. 617–626 (2018)
Xu, X., Hao, J., Yu, L., Deng, Y.: Fuzzy optimal allocation model for task-resource assignment problem in a collaborative logistics network. IEEE Trans. Fuzzy Syst. 27(5), 1112–1125 (2019)
Xu, Z., et al.: Large-scale order dispatch in on-demand ride-hailing platforms: a learning and planning approach. In: SIGKDD, pp. 905–913 (2018)
Yuan, W., Chen, J., Cao, J., Jin, Z.: Forecast of logistics demand based on grey deep neural network model. In: ICMLC, pp. 251–256 (2018)
Zhang, L., et al.: A taxi order dispatch model based on combinatorial optimization. In: SIGKDD, pp. 2151–2159 (2017)
Zhou, C., Li, H., Liu, W., Stephen, A., Lee, L.H., Chew, E.P.: Challenges and opportunities in integration of simulation and optimization in maritime logistics. In: WSC, pp. 2897–2908 (2018)
Acknowledgements
The authors are very grateful to the editors and reviewers for their valuable comments and suggestions. This research was supported by the National Natural Science Foundation of China (NSFC) (Nos.U1911203, U1811264 and 61702423).
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Liu, J. et al. (2020). Adaptive Loading Plan Decision Based upon Limited Transport Capacity. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_42
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DOI: https://doi.org/10.1007/978-3-030-59419-0_42
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