Maritime transportation plays a significant role in international trade and global supply chains. Ship navigation safety is the foundation of operating maritime business smoothly. Recently, more and more attention has been paid to marine environmental protection. To enhance maritime safety and reduce pollution in the marine environment, various regulations and conventions are proposed by international organizations and local governments. One of the most efficient ways of ensuring that the related requirements are complied with by ships is ship inspection by port state control (PSC). In the procedure of ship inspection, a critical issue for the port state is how to select ships of higher risk for inspection and how to optimally allocate the limited inspection resources to these ships. In this study, we adopt prediction and optimization approaches to address the above issues. We first predict the number of ship deficiencies based on a k nearest neighbor (kNN) model. Then, we propose three optimization models which aim for a trade-off between the reward for detected deficiencies and the human resource cost of ship inspection. Specifically, we first follow the predict-then-optimize framework and develop a deterministic optimization model. We also establish two stochastic optimization models where the distribution of ship deficiency number is estimated by the predictive prescription method and the global prescriptive analysis method, respectively. Furthermore, we conduct a case study using inspection data at the Hong Kong port to compare the performances of the three optimization models, from which we conclude that the predictive prescription model is more efficient and effective for this problem.
Citation: Ran Yan, Ying Yang, Yuquan Du. Stochastic optimization model for ship inspection planning under uncertainty in maritime transportation[J]. Electronic Research Archive, 2023, 31(1): 103-122. doi: 10.3934/era.2023006
Maritime transportation plays a significant role in international trade and global supply chains. Ship navigation safety is the foundation of operating maritime business smoothly. Recently, more and more attention has been paid to marine environmental protection. To enhance maritime safety and reduce pollution in the marine environment, various regulations and conventions are proposed by international organizations and local governments. One of the most efficient ways of ensuring that the related requirements are complied with by ships is ship inspection by port state control (PSC). In the procedure of ship inspection, a critical issue for the port state is how to select ships of higher risk for inspection and how to optimally allocate the limited inspection resources to these ships. In this study, we adopt prediction and optimization approaches to address the above issues. We first predict the number of ship deficiencies based on a k nearest neighbor (kNN) model. Then, we propose three optimization models which aim for a trade-off between the reward for detected deficiencies and the human resource cost of ship inspection. Specifically, we first follow the predict-then-optimize framework and develop a deterministic optimization model. We also establish two stochastic optimization models where the distribution of ship deficiency number is estimated by the predictive prescription method and the global prescriptive analysis method, respectively. Furthermore, we conduct a case study using inspection data at the Hong Kong port to compare the performances of the three optimization models, from which we conclude that the predictive prescription model is more efficient and effective for this problem.
| [1] |
C. Li, Y. Xie, G. Wang, X. Zeng, H. Jing, Lateral stability regulation of intelligent electric vehicle based on model predictive control, J. Intell. Connected Veh., 4 (2021), 104–114. https://doi.org/10.1108/JICV-03-2021-0005 doi: 10.1108/JICV-03-2021-0005
|
| [2] |
S. Wang, D. Zhuge, L. Zhen, C. Y. Lee, Liner shipping service planning under sulfur emission regulations, Transp. Sci., 55 (2021), 491–509. https://doi.org/10.1287/trsc.2020.1010 doi: 10.1287/trsc.2020.1010
|
| [3] |
L. Zhen, Y. Hu, S. Wang, G. Laporte, Y. Wu, Fleet deployment and demand fulfillment for container shipping liners, Transp. Res. Part B Methodol., 120 (2019), 15–32. https://doi.org/10.1016/j.trb.2018.11.011 doi: 10.1016/j.trb.2018.11.011
|
| [4] |
K. Wang, S. Wang, L. Zhen, X. Qu, Cruise service planning considering berth availability and decreasing marginal profit, Transp. Res. Part B Methodol., 95 (2017), 1–18. https://doi.org/10.1016/j.trb.2016.10.020 doi: 10.1016/j.trb.2016.10.020
|
| [5] | S. N. Sirimanne, J. Hoffman, W. Juan, R. Asariotis, M. Assaf, G. Ayala, et al., Review of Maritime Transport 2019, UNCTAD, New York, USA, 2019. |
| [6] |
N. Lyu, Y. Wang, C. Wu, L. Peng, A. F. Thomas, Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions, J. Intell. Connected Veh., 5 (2021), 17–35. https://doi.org/10.1108/JICV-07-2021-0008 doi: 10.1108/JICV-07-2021-0008
|
| [7] |
W. Yi, L. Zhen, Y. Jin, Stackelberg game analysis of government subsidy on sustainable off-site construction and low-carbon logistics, Cleaner Logist. Supply Chain, 2 (2021), 100013. https://doi.org/10.1016/j.clscn.2021.100013 doi: 10.1016/j.clscn.2021.100013
|
| [8] |
D. Huang, S. Wang, A two-stage stochastic programming model of coordinated electric bus charging scheduling for a hybrid charging scheme, Multimodal Transp., 1 (2022), 100006. https://doi.org/10.1016/j.multra.2022.100006 doi: 10.1016/j.multra.2022.100006
|
| [9] | IMO, Maritime Safety, 2019. Available from: https://www.imo.org/en/OurWork/Safety/Pages/default.aspx. |
| [10] |
L. Wu, Y. Adulyasak, J. F. Cordeau, S. Wang, Vessel service planning in seaports, Oper. Res., 70 (2022), 2032–2053. https://doi.org/10.1287/opre.2021.2228 doi: 10.1287/opre.2021.2228
|
| [11] |
L. Zhen, Y. Wu, S. Wang, G. Laporte, Green technology adoption for fleet deployment in a shipping network, Transp. Res. Part B Methodol., 139 (2020), 388–410. https://doi.org/10.1016/j.trb.2020.06.004 doi: 10.1016/j.trb.2020.06.004
|
| [12] |
S. Wang, R. Yan, A global method from predictive to prescriptive analytics considering prediction error for "predict, then optimize" with an example of low-carbon logistics, Cleaner Logist. Supply Chain, 4 (2022), 100062. https://doi.org/10.1016/j.clscn.2022.100062 doi: 10.1016/j.clscn.2022.100062
|
| [13] |
J. Qi, S. Wang, H. Psaraftis, Bi-level optimization model applications in managing air emissions from ships: A review, Commun. Transp. Res., 1 (2021), 100020. https://doi.org/10.1016/j.commtr.2021.100020 doi: 10.1016/j.commtr.2021.100020
|
| [14] | IMO, Initial IMO GHG Strategy, 2019. Available from: https://www.imo.org/en/MediaCentre/HotTopics/Pages/Reducing-greenhouse-gas-emissions-from-ships.aspx. |
| [15] |
R. Yan, S. Wang, Integrating prediction with optimization: Models and applications in transportation management, Multimodal Transp., 1 (2022), 100018. https://doi.org/10.1016/j.multra.2022.100018 doi: 10.1016/j.multra.2022.100018
|
| [16] |
L. Zhen, Y. Wu, S. Wang, Y. Hu, W. Yi, Capacitated closed-loop supply chain network design under uncertainty, Adv. Eng. Inf., 38 (2018), 306–315. https://doi.org/10.1016/j.aei.2018.07.007 doi: 10.1016/j.aei.2018.07.007
|
| [17] | IMO, Resolution A. 1155(32) Adopted on 15 December 2021 (Agenda items 12 and 14) Procedures for port state control, 2021, 2022. Available from: https://www.register-iri.com/wp-content/uploads/A.115532.pdf. |
| [18] |
S. Wang, L. Zhen, D. Zhuge, Dynamic programming algorithms for selection of waste disposal ports in cruise shipping, Transp. Res. Part B Methodol., 108 (2018), 235–248. https://doi.org/10.1016/j.trb.2017.12.016 doi: 10.1016/j.trb.2017.12.016
|
| [19] |
W. Yi, S. Wu, L. Zhen, G. Chawynski, Bi-level programming subsidy design for promoting sustainable prefabricated product logistics, Cleaner Logist. Supply Chain, 1 (2021), 100005. https://doi.org/10.1016/j.clscn.2021.100005 doi: 10.1016/j.clscn.2021.100005
|
| [20] | Tokyo MoU, Information sheet of the new inspection regime (NIR), 2014. Available from: http://www.tokyo-mou.org/doc/NIR-information%20sheet-r.pdf. |
| [21] |
C. Chai, Z. Zhou, W. Yin, H. David, S. Zhang, Evaluating the moderating effect of in-vehicle warning information on mental workload and collision avoidance performance, J. Intell. Connected Veh., 5 (2022), 49–62. https://doi.org/10.1108/JICV-03-2021-0003 doi: 10.1108/JICV-03-2021-0003
|
| [22] |
C. Heij, S. Knapp, Shipping inspections, detentions, and incidents: an empirical analysis of risk dimensions, Marit. Policy Manage., 46 (2019), 866–883. https://doi.org/10.1080/03088839.2019.1647362 doi: 10.1080/03088839.2019.1647362
|
| [23] | R. Xu, Q. Lu, K. Li, W. Li, Web mining for improving risk assessment in port state control inspection, in 2007 International Conference on Natural Language Processing and Knowledge Engineering, IEEE, (2007), 427–434. https://doi.org/10.1109/NLPKE.2007.4368066 |
| [24] | R. Xu, Q. Lu, W. Li, K. Li, H. S. Zheng, A risk assessment system for improving port state control inspection, in 2007 International Conference on Machine Learning and Cybernetics, IEEE, 2 (2007), 818–823. https://doi.org/10.1109/ICMLC.2007.4370255 |
| [25] |
R. Yan, S. Wang, K. Fagerholt, A semi-"smart predict then optimize"(semi-SPO) method for efficient ship inspection, Transp. Res. Part B Methodol., 142 (2020), 100–125. https://doi.org/10.1016/j.trb.2020.09.014 doi: 10.1016/j.trb.2020.09.014
|
| [26] | R. Yan, S. Wang, Ship inspection by port state control—review of current research, in Smart Transportation Systems 2019, Springer, (2019), 233–241. https://doi.org/10.1007/978-981-13-8683-1_24 |
| [27] | S. Knapp, C. Heij, Improved strategies for the maritime industry to target vessels for inspection and to select inspection priority areas, Safety, 6 (2020), 18. https://www.mdpi.com/2313-576X/6/2/18 |
| [28] |
S. Wu, X. Chen, C. Shi, J. Fu, Y. Yan, S. Wang, Ship detention prediction via feature selection scheme and support vector machine (svm), Marit. Policy Manage., 49 (2022), 140–153. https://doi.org/10.1080/03088839.2021.1875141 doi: 10.1080/03088839.2021.1875141
|
| [29] |
Z. Yang, Z. Yang, J. Yin, Realising advanced risk-based port state control inspection using data-driven bayesian networks, Transp. Res. Part A Policy Pract., 110 (2018), 38–56. https://doi.org/10.1016/j.tra.2018.01.033 doi: 10.1016/j.tra.2018.01.033
|
| [30] |
S. Wang, R. Yan, X. Qu, Development of a non-parametric classifier: Effective identification, algorithm, and applications in port state control for maritime transportation, Transp. Res. Part B Methodol., 128 (2019), 129–157. https://doi.org/10.1016/j.trb.2019.07.017 doi: 10.1016/j.trb.2019.07.017
|
| [31] |
R. Yan, S. Wang, J. Cao, D. Sun, Shipping domain knowledge informed prediction and optimization in port state control, Transp. Res. Part B Methodol., 149 (2021), 52–78. https://doi.org/10.1016/j.trb.2021.05.003 doi: 10.1016/j.trb.2021.05.003
|
| [32] |
R. Yan, S. Wang, C. Peng, An artificial intelligence model considering data imbalance for ship selection in port state control based on detention probabilities, J. Comput. Sci., 48 (2021), 101257. https://doi.org/10.1016/j.jocs.2020.101257 doi: 10.1016/j.jocs.2020.101257
|
| [33] |
X. Tian, R. Yan, Y. Liu, S. Wang, A smart predict-then-optimize method for targeted and cost-effective maritime transportation, Transp. Res. Part B Methodol., 142 (2020), 100–125. https://doi.org/10.1016/j.trb.2020.09.014 doi: 10.1016/j.trb.2020.09.014
|
| [34] | IMO, Procedures for Port State Control, 2019. Available from: https://www.imo.org/en/OurWork/IIIS/Pages/Port%20State%20Control.aspx. |
| [35] |
D. Bertsimas, N. Kallus, From predictive to prescriptive analytics, Manage. Sci., 66 (2020), 1025–1044. https://doi.org/10.1287/mnsc.2018.3253 doi: 10.1287/mnsc.2018.3253
|
| [36] |
L. Galli, T. Levato, F. Schoen, L. Tigli, Prescriptive analytics for inventory management in health care, J. Oper. Res. Soc., 72 (2021), 2211–2224. https://doi.org/10.1080/01605682.2020.1776167 doi: 10.1080/01605682.2020.1776167
|
| [37] | Tokyo MoU, Annual report 2016 on port state control in the Asia-Pacific region, 2017. Available from: http://www.tokyo-mou.org/doc/ANN16.pdf. |
| [38] | Tokyo MoU, Black–Grey–White lists, 2017. Available from: http://www.tokyo-mou.org/doc/Flag%20performance%20list%202017.pdf. |
| [39] | Paris MoU, Criteria for responsibility assessment of recognized organizations (RO), 2013. Available from: https://www.parismou.org/sites/default/files/RO%20responsibility%20rev11_0.pdf. |
| [40] |
E. Fix, J. L. Hodges, Discriminatory analysis: nonparametric discrimination, consistency properties, Int. Stat. Rev., 57 (1989), 238–247. https://doi.org/10.2307/1403797 doi: 10.2307/1403797
|
| [41] |
A. J. Smola, B. Schölkopf, A tutorial on support vector regression, Stat. Comput., 14 (2004), 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88 doi: 10.1023/B:STCO.0000035301.49549.88
|
| [42] | H. Bhavsar, M. H. Panchal, A review on support vector machine for data classification, Int. J. Adv. Res. Comput. Eng. Technol., 1 (2012), 185–189. |
Ran Yan, Ying Yang, Yuquan Du. Stochastic optimization model for ship inspection planning under uncertainty in maritime transportation[J]. Electronic Research Archive, 2023, 31(1): 103-122. doi: 10.3934/era.2023006
DownLoad: