Evidence map›Paper›PMID 36059572›Full record

ArticleOcean & coastal management2022

An analysis of port congestion alleviation strategy based on system dynamics.

Haonan Lin, Weijun Zeng, Jian Luo, Guofang Nan

Open access · greenAbstract read
In one paragraph

Article in Ocean & coastal management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
4.7field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 38 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Haonan LinManagement School, Hainan University, Haikou, Hainan, 570228, China.
Weijun ZengManagement School, Hainan University, Haikou, Hainan, 570228, China.
Jian LuoManagement School, Hainan University, Haikou, Hainan, 570228, China.
Guofang NanManagement School, Hainan University, Haikou, Hainan, 570228, China.
Hainan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Port congestion has become a key factor restricting the international trade and economic development, especially during the COVID-19 epidemic. It is essential for the port to implement the effective alleviation strategies for handling the uncertain congestion. This paper aims to investigate the performance of the epidemic prevention alliance strategy (EPAS), shared berths strategy (SBS) around adjacent ports and their hybrid strategy in alleviating the port congestion. To simulate the effect of these three strategies, a system dynamics model of dual-port operation is developed considering the factors of the integrated service level of liner routes, empty container allocation, port congestion and regional economics, and so forth. The results indicate that the key issue of port congestion stems from the implementation of epidemic preventive measures. Among these three strategies, the hybrid strategy performs the best in alleviating the port congestion, improving integrated service levels, and curbing the fluctuation of container price. Moreover, the measures of investing more human resources and fixed assets are always taken in many current ports to alleviate the issue of port congestion. Therefore, the impacts of various investment in human resources and fixed assets on alleviating the port congestion are discussed. Finally, some suggestions are provided for the government to strengthen the cooperation between ports and promote the construction of port facility resources.

Indexed as

COVID-19 pandemicPort alliancePort congestionShared berthSystem dynamics

Identifiers

PMID36059572
PMCPMC9417887
OpenAlexW4293795626

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.