An analysis of port congestion alleviation strategy based on system dynamics

Lin, Haonan; Zeng, Weijun; Nan, Guofang

doi:10.1016/j.ocecoaman.2022.106336

Cited by 20 publications

(5 citation statements)

References 46 publications

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“…operational time of tugboats in the port refers to the total time spent on both idle operations and assisting operations for completing all tasks. The first objective function is shown in Eq (1).…”

Section: Plos Onementioning

confidence: 99%

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An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem

Yao,

Duan,

Tang

2024

PLoS ONE

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With the continuous prosperity of maritime transportation on a global scale and the resulting escalation in port trade volume, tugboats assume a pivotal role as essential auxiliary tools influencing the ingress and egress of vessels into and out of ports. As a result, the optimization of port tug scheduling becomes of paramount importance, as it contributes to the heightened efficiency of ship movements, cost savings in port operations, and the promotion of sustainable development within the realm of maritime transportation. However, a majority of current tugboat scheduling models tend to focus solely on the maximum operational time. Alternatively, the formulated objective functions often deviate from real-world scenarios. Furthermore, prevailing scheduling methods exhibit shortcomings, including inadequate solution accuracy and incompatibility with integer programming. Consequently, this paper introduces a novel multi-objective tugboat scheduling model to align more effectively with practical considerations. We propose a novel optimization algorithm, the Improved Grey Wolf Optimization (IGWO), for solving the tugboat scheduling model. The algorithm enhances convergence performance by optimizing convergence parameters and individual updates, making it particularly suited for solving integer programming problems. The experimental session designs several scale instances according to the reality of the port, carries out simulation experiments comparing several groups of intelligent algorithms, verifies the effectiveness of IGWO, and verifies it in the comprehensive port area of Huanghua Port to get the optimal scheduling scheme of this port area, and finally gives management suggestions to reduce the cost of tugboat operation through sensitivity analysis.

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Section: Plos Onementioning

confidence: 99%

“…Against the backdrop of open world ports and economic development, international trade competition has become increasingly fierce. The expansion of port scale and the continuous growing operational demands have become major trends and currents [ 1 ]. The demand for tugboats during ship arrivals and departures at ports is increasing.…”

Section: Introductionmentioning

confidence: 99%

An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem

Yao,

Duan,

Tang

2024

PLoS ONE

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“…Notably, simulation methods have garnered substantial attention from scholars within this research domain because simulation can involve the complex and dynamic nature of the transportation system comprising both ports and vessels. Lin et al [11] used system dynamics to evaluate strategies designed for alleviating port congestion during COVID-19. Liu et al [12] also conducted simulations based on system dynamics and they suggested implementing advanced smart regulation systems to enhance port operational efficiency and effectively alleviate port congestion.…”

Section: Mechanism Of Port Congestionmentioning

confidence: 99%

Network Disruptions and Ripple Effects: Queueing Model, Simulation, and Data Analysis of Port Congestion

Guo,

Wang,

Wang

2023

JMSE

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Disruptions often happen to ports and cause varying degrees of port congestion. This study employs a queueing model to investigate network disruption and the resultant ripple effects in the global transportation system. We first propose an algorithm to solve the queueing model. Based on the queueing model, we obtain analytical results or propose hypotheses regarding the mechanism under disruptions. We further conduct simulations to examine the analytical results and hypotheses. Three key findings in this study are: (1) disruptions in the small port lead to a longer round-trip time compared to those in the large port; (2) herding behavior in the transportation system causes heavier congestion and also produces more emissions; and (3) major-rare disruptions cause a longer waiting time at both the port under disruption and other ports of call in the transportation system. These insights can help operators understand the mechanism of disruptions and put in place countermeasures.

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“…The escalating congestion observed in CTs has a detrimental impact on productivity and the ability to adhere to schedules, consequently causing a ripple effect that disrupts the operational plans of other vessels [4]. As a result, the identification of mitigating congestion CTs as a pivotal factor is crucial in addressing the decline in productivity [5].…”

Section: Introductionmentioning

confidence: 99%

“…Equation ( 11) updates the start time of the job for YC (𝑐), selecting the larger value between the arrival time of IT (𝑦 𝑠 𝑖 (𝑘) + 𝑦 ̂𝑖(𝑘)) and the finish time of the preceding job for YC (𝑐). If the 𝑘-th job is twin job, the finish time is increased by the product of the number of handling (𝑥 𝑡𝑤 (𝑘)) and the expected job handling time of YC referring to equation (5).…”

mentioning

confidence: 99%

A Predictive Discrete Event Simulation for Predicting Operation Times in Container Terminal

Park,

Kim,

Bae

2024

IEEE Access

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Container terminals (CTs) play a crucial role within the global supply chain in the context of the global container transportation business. The primary obligation associated with these terminals involves ensuring the punctual execution of primary vessel operations, while strictly adhering to the Estimated Time of Departure (ETD) for vessels. To accomplish this goal, it is crucial to develop rigorous and exact operating strategies. Nonetheless, the accurate prediction of operation times in CT presents a significant challenge due to the concurrent involvement of various Container Handling Equipment (CHE) and the occurrence of unforeseen situations. To address this issue, this study proposes a novel approach called Predictive Discrete Event Simulation (PDES) that utilizes data collected from CT to predict the operation times. The PDES is an advanced approach that builds upon the widely used Discrete Event Simulation (DES) technique in the field of simulations. It is specifically designed to provide precise predictions of the operation times in CTs, where multiple events take place concurrently. The PDES in this paper aims to overcome the shortcomings of current simulation-based approaches for prediction in CT. These approaches often rely on predefined task sequences and assumed time for job handling time of CHE in their scenarios, which can result in reduced accuracy when predicting operation times. Through the resolution of these issues, the proposed PDES exhibits the capacity to improve predictive performance. To enhance the predictive performance of operation times in CTs through PDES, two approaches are introduced. The first approach entails the application of Support Vector Machine (SVM) algorithms for the purpose of predicting operation times. This approach is further augmented by integrating it with DES to improve the accuracy of predictive performance. The second approach involves predicting by simulating real-world operational scenarios in CTs using algorithms for CHE assignment. The predictive performance of the proposed PDES is assessed through the utilization of data gathered from Busan Port Terminal (BPT) in South Korea, demonstrating superior performance compared to alternative prediction approaches.

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An analysis of port congestion alleviation strategy based on system dynamics

Cited by 20 publications

References 46 publications

An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem

An improved gray wolf optimization to solve the multi-objective tugboat scheduling problem

Network Disruptions and Ripple Effects: Queueing Model, Simulation, and Data Analysis of Port Congestion

A Predictive Discrete Event Simulation for Predicting Operation Times in Container Terminal

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