Network Disruptions and Ripple Effects: Queueing Model, Simulation, and Data Analysis of Port Congestion
Round 1
Reviewer 1 Report
The paper entitled “Network Disruptions and Ripple Effects: Queueing Model, Simulation and Data Analysis of Port Congestion”, investigates the network disruption and its resultant ripple effects when the transportation system is under disruption. The manuscript is well written, interesting and concise with the main findings and the novelty properly highlighted. Some minor comments should be addressed prior to publication in Journal of Marine Science and Engineering:
1. Terms “Jackson network” and “Arena” simulation model should be explained when first mentioned within the manuscript for better understanding (for readers that are not that familiar with such specific technical terms).
2. In my opinion a past tense would be more suitable for a literature review. Also, please carefully check the verbs for the case of one and more than one author within the literature review.
3. To ensure the completeness of the literature survey, consider citing relevant recently published papers:
Pengfei, Z., Bing, H., & Haibo, K. (2021). Risk transmission and control of port-hinterland service network: From the perspective of preventive investment and government subsidies. Brodogradnja, 72(1), 59-78.
Wu, G., Li, Y., Jiang, C., Wang, C., Guo, J., & Cheng, R. (2022). Multi-vessels collision avoidance strategy for autonomous surface vehicles based on genetic algorithm in congested port environment. Brodogradnja, 73(3), 69-91.
4. All abbreviations should be explained when first mentioned.
5. Perhaps I’ve missed it, but how did the authors determine the probability for vessels to travel to port j after visiting port i?
6. Line 334: excess symbol.
7. Please consider improving the quality of Figure 2 to enhance its readability.
8. Line 466: a container shipping company in France.
9. Can the authors provide a reference for the stay in port time?
10. How could the port management benefit from the proposed algorithm? Could the authors give some more details on that? In other words, how can the proposed algorithm help to improve the port management in order to avoid or reduce the port congestion?
Minor editing of English language required.
Author Response
Comment 1-0: The paper entitled “Network Disruptions and Ripple Effects: Queueing Model, Simulation and Data Analysis of Port Congestion”, investigates the network disruption and its resultant ripple effects when the transportation system is under disruption. The manuscript is well written, interesting and concise with the main findings and the novelty properly highlighted. Some minor comments should be addressed prior to publication in Journal of Marine Science and Engineering.
Response: Thank you for your very positive assessment of our manuscript. We have carefully addressed all your comments and our replies to each of them can be found as follows.
Comment 1-1: Terms “Jackson network” and “Arena” simulation model should be explained when first mentioned within the manuscript for better understanding (for readers that are not that familiar with such specific technical terms).
Response: Thanks for the advice. We are sorry for not making it clear in the last version of our manuscript. We have added the related description of “Jackson network” and “Arena” in the revised manuscript. Please see Line 65–66 and Line 162–165. In summary, we opt for a closed Jackson network because our queueing system has a fixed number of vessels. This differs from an open queueing system where customers arrive from external sources and depart after receiving service. We select Arena as our simulation tool due to its capability to analyze complex transportation systems.
Comment 1-2: In my opinion a past tense would be more suitable for a literature review. Also, please carefully check the verbs for the case of one and more than one author within the literature review.
Response: Thanks for the valuable advice. We have revised the tense of the literature review thoroughly.
Comment 1-3: To ensure the completeness of the literature survey, consider citing relevant recently published papers:
Pengfei, Z., Bing, H., & Haibo, K. (2021). Risk transmission and control of port-hinterland service network: From the perspective of preventive investment and government subsidies. Brodogradnja, 72(1), 59-78.
Wu, G., Li, Y., Jiang, C., Wang, C., Guo, J., & Cheng, R. (2022). Multi-vessels collision avoidance strategy for autonomous surface vehicles based on genetic algorithm in congested port environment. Brodogradnja, 73(3), 69-91.
Response: Thanks for the suggestion and we have added these two literatures to our reference, which helps us enrich the literature part.
Comment 1-4: All abbreviations should be explained when first mentioned.
Response: Thanks for the suggestion. We have conducted a thorough review of the entire manuscript to ensure that each abbreviation is appropriately defined upon its first occurrence.
Comment 1-5: Perhaps I’ve missed it, but how did the authors determine the probability for vessels to travel to port j after visiting port i?.
Response: Thank you for your question. Our research focuses on proving and analyzing conclusions related to disruptions and ripple effects between two ports. So, after visiting one port, the vessel will sail to another port with probability 1.
Comment 1-6: Line 334: excess symbol.
Response: Thank you for your reminder. We have checked line 334, and the square symbol there indicates the end of our proof.
Comment 1-7: Please consider improving the quality of Figure 2 to enhance its readability.
Response: Thank you for your suggestion. Figure 2 is our simulation model in Arena software. To enhance readability, we have improved the image resolution in the revised version.
Comment 1-8: Line 466: a container shipping company in France.
Response: Thanks for you detailed advice and we have revised it.
Comment 1-9: Can the authors provide a reference for the stay in port time?
Response: Thanks for your advice. We can refer to https://www.econdb.com/maritime/ports/ for the stay time in ports across the world and we also added a footnote to the manuscript to clarify.
Comment 1-10: How could the port management benefit from the proposed algorithm? Could the authors give some more details on that? In other words, how can the proposed algorithm help to improve the port management in order to avoid or reduce the port congestion?
Response: In the conclusion section, we have added more practical discussions, analyzing how our results can guide port practices. For your convenience, we also show it below.
“Given that disruptions exert a pervasive influence over the entire transportation system, comprehending the subsequent effects and their underlying mechanisms assumes paramount importance. This facet not only constitutes a pivotal concern but also serves as a guiding principle for operators in devising scientifically informed response strategies to effectively contend with disruptions. Since we find that disruptions in smaller ports yield longer round-trip durations in comparison to those observed in larger ports, small ports are suggested to enhance their level of automation to improve operational efficiency, thereby avoiding becoming bottlenecks in the entire transportation system when disruptions happen. Herding behavior is a lose-lose strategy and thus ship operators should avoid engaging in blind acceleration behavior. Major-rare disruptions have a significant impact on the entire transportation system. Adequate predictive methods are needed to monitor major-rare disruptions effectively.”
Once again, thank you for all the constructive comments. We hope that our revision has addressed them and is now of a publishable standard.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
This paper is of sound quality on a subject deserving the Journal's attention. This study attempts to analyze Network Disruptions and Ripple Effects of Port Congestion. Proposing an algorithm to solve the queueing model, this study has empirically identified (1) disruptions in the small port lead to longer round-trip time compared to those in the large port; (2) herding in the transportation will cause heavier congestion and also produce more emissions; (3) the major-rare disruption cause longer waiting time at both the port under disruption and other ports of call in the transportation system. Overall, the paper is well structured, therefore it is easy to follow and builds a clear conclusion from the data. But requires some editing and revision before publication.
At first, Section 1.1
‘This study divides literature reviews into two categories. The first category mainly focuses on analyzing port congestion and its mechanism. The second category pays attention to making optimal plans to alleviate port congestion.’
-Recommends presenting each literature reviews separately ex) 1.1.1 port congestion and its mechanism, and optimal plans to alleviate port congestion.
In addition, this study clearly presented the finding of this study, but research implication part is weak. Research conclusion (implication) part is weak, focusing on data analysis results (enumerating bits of information). Additional explanations incorporating theoretical and practical are required.
Author Response
Comment 2-0: This paper is of sound quality on a subject deserving the Journal's attention. This study attempts to analyze Network Disruptions and Ripple Effects of Port Congestion. Proposing an algorithm to solve the queueing model, this study has empirically identified (1) disruptions in the small port lead to longer round-trip time compared to those in the large port; (2) herding in the transportation will cause heavier congestion and also produce more emissions; (3) the major-rare disruption cause longer waiting time at both the port under disruption and other ports of call in the transportation system. Overall, the paper is well structured, therefore it is easy to follow and builds a clear conclusion from the data. But requires some editing and revision before publication.
Response: Thank you for your very positive assessment of our manuscript. We have carefully addressed all your comments and our replies to each of them can be found as follows.
Comment 2-1: At first, Section 1.1 ‘This study divides literature reviews into two categories. The first category mainly focuses on analyzing port congestion and its mechanism. The second category pays attention to making optimal plans to alleviate port congestion.’
-Recommends presenting each literature reviews separately ex) 1.1.1 port congestion and its mechanism, and optimal plans to alleviate port congestion.
Response: Thanks for the advice. We have divided the literature review into three subsections: 1.1.1 Mechanism of Port Congestion; 1.1.2 Optimal Plans for Alleviating Port Congestion; and 1.1.3 Summary.
Comment 2-2: In addition, this study clearly presented the finding of this study, but research implication part is weak. Research conclusion (implication) part is weak, focusing on data analysis results (enumerating bits of information). Additional explanations incorporating theoretical and practical are required.
Response: Thanks for the valuable suggestion. We have revised the conclusion part according to your advice. For your convenience, we also show it below.
“
In this study, we investigate the network disruption and its resultant ripple effects when the transportation system is under disruption, e.g., COVID-19. Initially, we introduce an innovative algorithm tailored to tackle the specific queueing model pertinent to our study. Through the development of these queueing models, we derive analytical insights pertaining to the response of ports of varying sizes to disruptions of identical magnitude, as well as the manifestation of herding behavior within the transportation system. Furthermore, we extend our inquiry to encompass disruptions of differing magnitudes, culminating in the formulation of two hypotheses. Our analysis is substantiated through simulation experiments. In summation, our findings reveal that (1) disruptions in smaller ports yield longer round-trip durations in comparison to those observed in larger ports; (2) herding behavior exacerbates port congestion and contributes to increased emissions; and (3) major-rare disruptions engender prolonged waiting times not only at the directly affected port but also at other ports of call within the transportation system.
Given that disruptions exert a pervasive influence over the entire transportation system, comprehending the subsequent effects and their underlying mechanisms assumes paramount importance. This facet not only constitutes a pivotal concern but also serves as a guiding principle for operators in devising scientifically informed response strategies to effectively contend with disruptions. Since we find that disruptions in smaller ports yield longer round-trip durations in comparison to those observed in larger ports, small ports are suggested to enhance their level of automation to improve operational efficiency, thereby avoiding becoming bottlenecks in the entire transportation system when disruptions happen. Herding behavior is a lose-lose strategy and thus ship operators should avoid engaging in blind acceleration behavior. Major-rare disruptions have a significant impact on the entire transportation system. Adequate predictive methods are needed to monitor major-rare disruptions effectively.
”
Once again, thank you for all the constructive comments. We hope that our revision has addressed them and is now of a publishable standard.
Author Response File: Author Response.pdf
