An adaptive large neighborhood search algorithm for the tugboat scheduling problem

Wang, Xin; Liang, Yijing; Wei, Xiaoyang; Chew, Ek Peng

doi:10.1016/j.cie.2023.109039

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…Ref. [10] developed an adaptive large-neighborhood search algorithm to solve the tugboat scheduling problem in large-scale instances and obtain good scheduling rules. Ref.…”

Section: Literature Review 21 Tug Dispatching Methodsmentioning

confidence: 99%

Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction

Li,

Duan,

Xiong

et al. 2024

Sustainability

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The scheduling of harbor tugboats is a crucial task in port operations, aiming to optimize resource allocation and reduce operational costs, including fuel consumption of tugboats and the time cost of vessels waiting for operations. Due to the complexity of the port environment, traditional scheduling methods, often based on experience and practice, lack scientific and systematic decision support, making it difficult to cope with real-time changes in vessel dynamics and environmental factors. This often leads to scheduling delays and resource waste. To address this issue, this study proposes a mathematical model based on fuzzy programming, accounting for the uncertainty of the arrival time of target vessels. Additionally, we introduce the NRPER-DDPG algorithm (DDPG Algorithm with Prioritized Experience Replay and Noise Reduction), which combines a prioritized replay mechanism with a decaying noise strategy based on the DDPG algorithm. This approach optimizes the time for tugboats to reach the task location as a continuous action space, aiming to minimize the total system cost and improve scheduling efficiency. To verify the effectiveness of the mathematical model and algorithm, this study conducted experimental validation. Firstly, the optimal algorithm hyperparameter combinations were adjusted through random examples to ensure the stability and reliability of the algorithm. Subsequently, large-scale examples and actual port cases were used to further verify the performance advantages of the algorithm in practical applications. Experimental results demonstrate that the proposed mathematical model and algorithm significantly reduce system costs and improve scheduling efficiency, providing new insights and methods for the sustainable development of port operations.

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“…Ref. [10] developed an adaptive large-neighborhood search algorithm to solve the tugboat scheduling problem in large-scale instances and obtain good scheduling rules. Ref.…”

Section: Literature Review 21 Tug Dispatching Methodsmentioning

confidence: 99%

Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction

Li,

Duan,

Xiong

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…In the first stage, researchers mainly concentrated in the initial exploration of intelligent tugboat scheduling research, placing greater emphasis on model construction while neglecting the actual needs of ports. For example, researchers such as Liu, Xu, and Kang described Tug-SP as a multiprocessor task scheduling problem, optimized the port tug tugboat scheduling problem based on the objective of maximizing the tugboat's running time using different intelligent algorithms [9][10][11], and Wang et al investigated the tugboat schedule ng problem considering a multi-service model with multiple waypoints, with the optimization objective of minimizing the cost, and solved it with an adaptive large-domain algorithm and applicable to large-scale problems [12]. This significantly promoted the study of Tug-SP models and represented a substantial improvement over traditional scheduling models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Eq (11) indicates that the number of tugs at a base should meet the required number of tugs to complete the tasks. Eq (12) states that a tug can only dock at one base at a given time. Eq (13) implies that in the absence of any tasks at the current stage, the tug remains at the previous base.…”

Section: Plos Onementioning

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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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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“…Li et al [40] used a hybrid ALNS to solve the large-scale heterogeneous container loading problem. Wang et al compared the results of ALNS with the model when solving the tugboat scheduling problem [41]. Compared with other algorithms, ALNS has the characteristics of self-adaptability and multi-neighborhood search, has a good effect on the solving speed and the solving result, and has rarely been applied in the optimization of machinery scheduling.…”

Section: Proposed Algorithmsmentioning

confidence: 99%

An Adaptive Large Neighborhood Search Algorithm for Equipment Scheduling in the Railway Yard of an Automated Container Terminal

Chen,

Liu

2024

JMSE

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In container sea–rail combined transport, the railway yard in an automated container terminal (RYACT) is the link in the whole logistics transportation process, and its operation and scheduling efficiency directly affect the efficiency of logistics. To improve the equipment scheduling efficiency of an RYACT, this study examines the “RYACT–train” cooperative optimization problem in the mode of “unloading before loading” for train containers. A mixed-integer programming model with the objective of minimizing the maximum completion time of automated rail-mounted gantry crane (ARMG) tasks is established. An adaptive large neighborhood search (ALNS) algorithm and random search algorithm (RSA) are designed to solve the abovementioned problem, and the feasibility of the model and algorithm is verified by experiments. At the same time, the target value and calculation time of the model and algorithms are compared. The experimental results show that the model and the proposed algorithms are feasible and can effectively solve the “RYACT–train” cooperative optimization problem. The model only obtains the optimal solution of the “RYACT–train” cooperative scheduling problem with no more than 50 tasks within a limited time, and the ALNS algorithm can solve examples of various scales within a reasonable amount of time. The target value of the ALNS solution is smaller than that of the RSA solution.

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An adaptive large neighborhood search algorithm for the tugboat scheduling problem

Cited by 10 publications

References 23 publications

Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction

Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction

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

An Adaptive Large Neighborhood Search Algorithm for Equipment Scheduling in the Railway Yard of an Automated Container Terminal

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