Abstract
Proactive service is a kind of service that can be automatically provided based on logical judgment without human intervention. The traditional approach to evaluating proactive services involves conducting quantitative analyses through public datasets. However, this method can only evaluate the effectiveness of the specific algorithms and has limitations in evaluating the effectiveness of real-world applications as a whole. Determining a more appropriate evaluation approach for proactive services is a challenging problem. To address this issue, this paper proposes a method to evaluate the effectiveness of proactive services from a simulation perspective with a case of LNG (Liquefied Natural Gas) ship** as an example. Using the multi-agent simulation method, we design and implement a simulation model for LNG ships navigating at sea while avoiding dangerous areas by AnyLogic, which is capable of interacting with the deep learning model for predicting ships’ locations. Our simulation model also defines a set of metrics for evaluating the effectiveness of proactive services in the application case. We present a simulation demonstration on evading dangerous areas for LNG ships and conduct simulation experiments. The experimental results show that the simulation model can serve as an effective evaluation method for proactive services. Additionally, the simulation model can also be used to analyze differences in the application effects of proactive services implemented under different prediction algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ekyalimpa, R., Werner, M., Hague, S., AbouRizk, S., Porter, N.: A combined discrete-continuous simulation model for analyzing train-pedestrian interactions. In: 2016 Winter Simulation Conference (WSC), pp. 1583–1594. IEEE (2016)
Han, Y., Liu, C., Su, S., Zhu, M., Zhang, Z., Zhang, S.: A proactive service model facilitating stream data fusion and correlation. Int. J. Web Serv. Res. (IJWSR) 14(3), 1–16 (2017)
He, R., Zhao, M., **ang, W.: System Modeling and Simulation With Anylogic. Chemical Industry Press, Bei**g (2020). (In Chinese)
Hübner, M., Feierle, A., Rettenmaier, M., Bengler, K.: External communication of automated vehicles in mixed traffic: addressing the right human interaction partner in multi-agent simulation. Transport. Res. F: Traffic Psychol. Behav. 87, 365–378 (2022)
Jiao, L., Peng, Z., **, L., Ding, S., Cui, J.: Multi-agent coverage path planning via proximity interaction and cooperation. IEEE Sens. J. 22(6), 6196–6207 (2022)
Karanjkar, N., Joshi, S.M.: Mixed discrete-continuous simulation for digital twins. In: SIMULTECH, pp. 422–429 (2021)
Kim, J., Lee, S., Lee, S.: An evacuation route choice model based on multi-agent simulation in order to prepare tsunami disasters. Transportmetrica B: Transp. Dyn. 5(4), 385–401 (2017)
Li, L., Fan, S., Yang, G., Chen, Q., Zhao, J., Wei, N., Meng, W., Fan, J., Yang, H.: Continuous simulation of the separation process of co2/h2 by forming hydrate. Chem. Eng. Sci. X 7, 100067 (2020)
Li, Y., She, C.: Discrete simulation of vibratory roller compaction of field rockfills. Shock. Vib. 2021, 1–15 (2021)
Liu, J., Liu, Y., Qi, L.: Modelling liquefied natural gas ship traffic in port based on cellular automaton and multi-agent system. J. Navig. 74(3), 533–548 (2021)
Moustafa, A., Zhang, M.: Towards proactive web service adaptation. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds.) CAiSE 2012. LNCS, vol. 7328, pp. 473–485. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31095-9_31
Nie, X., Liu, Q., Li, G., Dai, R.: Current status and progress of LNG ship maritime transport safety research. China Saf. Sci. J. 27(5), 87 (2017). (In Chinese)
Nkiaka, E., Shrestha, N., Leta, O., Bauwens, W.: Use of continuous simulation model (cosimat) as a complementary tool to model sewer systems: a case study on the Paruck collector, brussels, Belgium. Water Environ. J. 30(3–4), 310–320 (2016)
Rotunno, G., Zupone, G.L., Fanti, M.P., Carnimeo, L.: Discrete event simulation as decision tool for sustainable development in smart cities. In: 2022 IEEE International Smart Cities Conference (ISC2), pp. 1–6. IEEE (2022)
Wang, H., Wang, L., Yu, Q., Zheng, Z., Yang, Z.: A proactive approach based on online reliability prediction for adaptation of service-oriented systems. J. Parallel Distrib. Comput. 114, 70–84 (2018)
Wang, S., Wang, Y., Yue, S., Wang, J., Wang, Z., Zhao, J.: Digitally twinned simulation model of self-propelled artillery maintenance and support system. J. Artillery Launch Control, 1–6 (2022). (In Chinese)
Acknowledgements
This work is supported by the Key Program of National Natural Science Foundation of China (No.61832004) and International Cooperation and Exchange Program of National Natural Science Foundation of China (No.62061136006).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shi, Y., Wang, G., Zhang, J., Li, Z., Yu, J. (2023). A Simulation Model for Proactive Services: A Case Study on Evading Dangerous Areas for LNG Ships. In: Wang, Z., Wang, S., Xu, H. (eds) Service Science. ICSS 2023. Communications in Computer and Information Science, vol 1844. Springer, Singapore. https://doi.org/10.1007/978-981-99-4402-6_31
Download citation
DOI: https://doi.org/10.1007/978-981-99-4402-6_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4401-9
Online ISBN: 978-981-99-4402-6
eBook Packages: Computer ScienceComputer Science (R0)