Column generation for low carbon berth allocation under uncertainty

Zhen, Lu; Sun, Qian; Zhang, Wei; Wang, Kai; Yi, Wen

doi:10.1080/01605682.2020.1776168

Cited by 29 publications

(16 citation statements)

References 46 publications

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“…They introduced an enhanced genetic algorithm, combining an adaptive genetic algorithm and heuristic berthing, to solve the problem. Zhen et al [13] and Xiang and Liu [14] have investigated the problem of the joint allocation of berths and cranes in an uncertain vessel arrival time and with loading and unloading operations. Among these, Zhen et al developed a two-stage stochastic optimization model for a low-carbon environment and designed a solution algorithm using a column generation technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Collaborative Scheduling Optimization of Container Port Berths and Cranes under Low-Carbon Environment

Jiang,

Ma,

Zhang

et al. 2024

Sustainability

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Motivated by the need for a green and low-carbon economy, we explore the co-scheduling optimization of berths and cranes. Our aim is to balance the carbon tax and operating costs of ports under uncertain conditions, proposing an innovative nonlinear mixed-integer programming formulation. To address this optimization challenge, we have developed an enhanced version of the adaptive spiral flying dung beetle algorithm (ASFDBO). In order to evaluate the performance of the ASFDBO algorithm, we performed a benchmark function test and a convergence analysis with other recognized metaheuristics. In addition, we verified the practical applicability of the ASFDBO algorithm in different test scenarios. Through numerical experiments, we analyze the feasibility and effectiveness of the algorithm’s scheduling solutions and improvement strategies. Results indicate that our collaborative scheduling optimization, which considers both carbon and production costs, achieves feasible solutions and reduces carbon expenses. Finally, we investigate the impact of different carbon tax rates on the joint scheduling optimization of berths and quay cranes, and the results show that a reasonable carbon tax policy can effectively reduce the carbon emissions of ports.

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Section: Literature Reviewmentioning

confidence: 99%

Collaborative Scheduling Optimization of Container Port Berths and Cranes under Low-Carbon Environment

Jiang,

Ma,

Zhang

et al. 2024

Sustainability

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“…They developed a three-phase simulation optimization method with the aim of congestion mitigation at a container port. Zhen et al (2021) solved a two-stage stochastic programming model for the discrete BACAP in which carbon emissions are associated with adjustments in QC operation. Tan and He (2021) considered a continuous BACAP of time-variant QC assignment under uncertainties in vessel arrivals and fluctuation of loading and unloading volumes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To deal with uncertainties, reactive and proactive approaches have been discussed. The reactive approach minimizes the deviation from the initial schedule to respond to real‐time disruptions (Zeng et al., 2011; Li et al., 2015; Umang et al., 2017; Xiang et al., 2018; Lv et al., 2020; Kim et al., 2021; Al‐Refaie and Abedalqader, 2022), whereas the proactive approach absorbs uncertainties in a baseline schedule (Lu and Xi, 2010; Xu et al., 2012; Rodriguez‐Molins et al., 2014; Mohammadi and Forghani, 2018; Iris and Lam, 2019; Liu et al., 2020; Zhen et al., 2021). In the literature, studies on proactive approaches are largely divided into two groups: robust and stochastic approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Proactive berth scheduling with data‐driven buffer time in container terminals

Woo,

Park,

Cho

et al. 2023

Int Trans Operational Res

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Global shipment volumes have been increasing due to changes in the business environment of e‐commerce and manufacturing. Consequently, container vessels carry more cargo for international trade, increasing uncertainties in terminal management. Terminal operators manage terminals by establishing a proactive schedule that responds to disruptions such as vessel delays, and the introduction of buffer time is a representative proactive strategy. In this study, by analyzing historical delay data with machine learning, we propose data‐driven buffer times to consider the heterogeneous arrival uncertainty of vessels. Thus, we proactive scheduling with data‐driven buffer times according to the desired robustness levels. This is a novel study on berth scheduling that applies data mining approaches to improve operations research techniques. Numerical experiments were conducted on the berth scheduling with time‐invariant quay crane assignment using real‐life data to validate the effectiveness of the proposed method. These experimental results revealed that applying the data‐driven buffer time could effectively reduce the cost incurred at the terminal by balancing baseline and recovery costs. In addition, our proposed methodology ensured the quality of the solution compared with a stochastic method and reduced the computational burden of a stochastic problem by using the data‐driven buffer times obtained prior to the solution construction. Therefore, the proposed method can be introduced into terminal operations to overcome the deficiencies of traditional approaches in terms of academic perspective.

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“…Standing at the perspective of a port providing SP, Yu et al [21] develop a multi-objective optimization model, to investigate the problem of berth allocation and quay crane assignment. Zhen et al [22] developed a columngeneration algorithm, to solve the two-stage stochastic programming model, proposed for the low-carbon berth-allocation problem under uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Detailed information of the case study result. ,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66, 67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91 Routes that finally adopt SP 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35, 36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,53,54,55,56,57,58,59,60,62,63,64,65,66,67, 68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85…”

mentioning

confidence: 99%

Shore Power Deployment Problem—A Case Study of a Chinese Container Shipping Network

Wang

Zheng

2022

Sustainability

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Shipping emissions, especially those in port areas, have become one of the main concerns of the maritime industry. Shore power has been recognized as a promising way to alleviate the problem. However, shore power has not been extensively adopted in China. Therefore, from the government’s point of view, this paper conducts a case study of the shore power deployment problem based on the real container shipping network of China, including the Port of Hong Kong. In addition to the basic case, we, also, conduct numerical experiments with different budgets, to analyze its influence on the optimal subsidy plan and cost–benefit analysis. The results give two useful managerial insights: (i) it might be unnecessary to spend a large amount of the budget on subsidization, and (ii) the subsidy expenditure needs to be considered together with the final bunker reduction, while creating the budget.

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Column generation for low carbon berth allocation under uncertainty

Cited by 29 publications

References 46 publications

Collaborative Scheduling Optimization of Container Port Berths and Cranes under Low-Carbon Environment

Collaborative Scheduling Optimization of Container Port Berths and Cranes under Low-Carbon Environment

Proactive berth scheduling with data‐driven buffer time in container terminals

Shore Power Deployment Problem—A Case Study of a Chinese Container Shipping Network

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