A Robust Scheduling Model for Continuous Berth Allocation Problem under Uncertainty

Wu, Yangcan; Liu, Miao

doi:10.1109/icectt50890.2020.00017

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…To solve the problem, the authors considered buffer times and buffer QCs for calling vessels and proposed a two‐stage heuristic framework constructed by an adaptive large neighbor search. Wu and Miao (2020) introduced the berthing regions for vessels as buffer spaces to enhance the robustness of the continuous BAP. The authors presented a heuristic algorithm in which the berthing regions and priorities of vessels are assigned by genetic algorithm, and the constructed solutions are evaluated based on a simulation procedure.…”

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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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

View full text Add to dashboard Cite

show abstract

“…(The use of buffers is well known from other areas, as, e.g., flight scheduling (Baumgarten et al 2014) or production management (Leus and Herroelen 2007), where the buffers help in deriving robust schedules as well.) In contrast, Wu and Miao (2020) do not use time buffers but space buffers, i.e., they do not determine a fixed position for each vessel but add some "slack" to the planned berths and hence aim for robustness by enhancing flexibility in terms of space. While they can show that this leads to a reduction in expected waiting times and, therefore, expected costs, this may impede efficiency when too much space and capacity is reserved for each vessel.…”

Section: Berth Allocation Planning Under Uncertaintymentioning

confidence: 99%

Robust berth scheduling using machine learning for vessel arrival time prediction

et al. 2022

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In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach.

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Distributionally robust optimization for the berth allocation problem under uncertainty

Agra¹,

Rodrigues²

2022

Transportation Research Part B: Methodological

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A Robust Scheduling Model for Continuous Berth Allocation Problem under Uncertainty

Cited by 5 publications

References 5 publications

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

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

Robust berth scheduling using machine learning for vessel arrival time prediction

Distributionally robust optimization for the berth allocation problem under uncertainty

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