A Robust Berth Allocation Optimization Procedure Based on Machine Learning

Kolley, Lorenz; Rückert, Nicolas; Fischer, Kathrin

doi:10.1007/978-3-030-85843-8_7

Cited by 6 publications

(9 citation statements)

References 32 publications

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“…A study [82] develops a ML‐based vessel arrival time prediction (ATP) method for efficient berth allocation using k‐nearest neighbours, linear regression, and regression trees. This study considers continuous berthing layout and dynamic vessel arrival along with a robust optimization approach based on dynamic time buffers.…”

Section: Current Literature On Stand‐alone Bapmentioning

confidence: 99%

A survey on computational intelligence approaches for intelligent marine terminal operations

Aslam,

Michaelides,

Herodotou

2024

IET Intelligent Trans Sys

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Marine container terminals (MCTs) play a crucial role in intelligent maritime transportation (IMT) systems. Since the number of containers handled by MCTs has been increasing over the years, there is a need for developing effective and efficient approaches to enhance the productivity of IMT systems. The berth allocation problem (BAP) and the quay crane allocation problem (QCAP) are two well‐known optimization problems in seaside operations of MCTs. The primary aim is to minimize the vessel service cost and maximize the performance of MCTs by optimally allocating berths and quay cranes to arriving vessels subject to practical constraints. This study presents an in‐depth review of computational intelligence (CI) approaches developed to enhance the performance of MCTs. First, an introduction to MCTs and their key operations is presented, primarily focusing on seaside operations. A detailed overview of recent CI methods and solutions developed for the BAP is presented, considering various berthing layouts. Subsequently, a review of solutions related to the QCAP is presented. The datasets used in the current literature are also discussed, enabling future researchers to identify appropriate datasets to use in their work. Eventually, a detailed discussion is presented to highlight key opportunities along with foreseeable future challenges in the area.

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Section: Current Literature On Stand‐alone Bapmentioning

confidence: 99%

A survey on computational intelligence approaches for intelligent marine terminal operations

Aslam,

Michaelides,

Herodotou

2024

IET Intelligent Trans Sys

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“…All other AIS messages not related to the vessel's approach are deleted. In addition to the remaining Euclidian distance to the designated terminal, also the attribute "drift", describing the discrepancy between COG and heading (Kolley et al 2021), is derived from the AIS messages.…”

Section: Data Selection and Preparationmentioning

confidence: 99%

“…Robust berth scheduling using machine learning for vessel… when the schedule stays feasible (Scholl 2001). To derive a robust berth allocation based on the forecasts, the concepts of conflicts (Liu et al 2017) and of dynamic time buffers (DTBs) are used (Kolley et al 2021) and further refined. More specifically, Liu et al (2017) define and measure robustness as a function of the service level that is achieved, where the service level in turn is defined as the number (or percentage) of vessels which-in the planned berth allocation-are not in conflict with other vessels.…”

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confidence: 99%

“…For this purpose, the BAP model using DTBs from Kolley et al (2021) is further modified and improved in this work. It is then applied to the forecast data.…”

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confidence: 99%

“…c) It is shown how the forecast data can be used to derive more robust berth allocations by applying the concept of DTBs. d) Moreover, the BAP model from Kolley et al (2021) is further refined such that the solutions are of more practical use and relevance. This is shown by comparing and benchmarking the resulting solutions with the model of Liu et al (2017).…”

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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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Berth Allocation and Quay Crane Assignment and Scheduling Problem Under Energy Constraints: Literature Review

Ech-Charrat¹,

El‐Amrani²,

Ezziyyani³

2023

Lecture Notes in Networks and Systems

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A Robust Berth Allocation Optimization Procedure Based on Machine Learning

Cited by 6 publications

References 32 publications

A survey on computational intelligence approaches for intelligent marine terminal operations

A survey on computational intelligence approaches for intelligent marine terminal operations

Robust berth scheduling using machine learning for vessel arrival time prediction

Berth Allocation and Quay Crane Assignment and Scheduling Problem Under Energy Constraints: Literature Review

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