A multi-objective model for discrete and dynamic Berth Allocation Problem

Issam, El Hammouti; Lajjam, Azza; Mohamed, El Merouani; kaoutar, Aharmouch; Tabaa, Yassine

doi:10.1145/3090354.3090464

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…At present, many scholars have proposed new solutions and methods for the berth allocation problem. Issam, E. H. et al [13] investigated the Dynamic Berth Allocation Problem (DBAP) and provided a multi-objective mathematical model; the validity of the model was verified by numerical experiments. Hsu, H. P. [14] proposed a hybrid particle swarm optimization (PSO) algorithm, which combined the improved PSO algorithm with the event-based heuristic algorithm to solve the DDBAP problem and dynamic QCAP (DQCAP) seaside operation planning problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Ant Colony Algorithm for Discrete Dynamic Berth Allocation in a Case Container Terminal

Yu,

Lv,

Wang

et al. 2023

JMSE

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Berth allocation is a critical concern in container terminal port logistics, involving the precise determination of where and when arriving vessels should dock along a quay. With berth space limitations and a continuous surge in container handling demands, ensuring an effective berth allocation is paramount for the smooth and efficient operation of container ports. However, due to the randomness of vessel arrival times and uncertainties surrounding container ship loading capacities, berth allocation problems (BAP) often present discrete and dynamic challenges. This paper addresses these challenges by considering real-world terminal operational factors, formulating relevant assumptions, and establishing a model for dynamic berth allocation and efficient ship berthing scheduling. The primary motivation stems from the parallels observed between the BAP problem and ant foraging path selection, leading to the proposal of a novel Parallel Search Structure Enhanced Ant Colony Algorithm (PACO). A proper set of parameters of the algorithm are selected based upon sensitivity analyses on the convergence and parallelism efficiency of the algorithm. To validate our method, a real-world case-container terminal operation in Shanghai Port was studied. The experimental comparison results show that the PACO algorithm outperforms other commonly used algorithms, making it more effective and efficient for the Discrete Dynamic Berth Allocation Problem (DDBAP).

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Section: Literature Reviewmentioning

confidence: 99%

Enhanced Ant Colony Algorithm for Discrete Dynamic Berth Allocation in a Case Container Terminal

Yu,

Lv,

Wang

et al. 2023

JMSE

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“…They used a Lagrangian heuristic algorithm for solving large-sized instances reaching near-optimal solutions in a reasonable computation time. Issam et al [29] extended the Imai's DDBAP mathematical formulation as a multiobjective MILP including ships' draft and greenhouse gas emissions evaluation. Xu et al [59] proposed a MILP formulation for solving the DDBAP and the SDBAP considering water depth and tidal effect with the aim to minimise the ships' completion time.…”

Section: Literature Review On the Ddbapmentioning

confidence: 99%

“…According to Table 1, the proposed mathematical formulation can be classified as discrete (disc), and draft ships dependent (draft) as a spatial attribute; dynamic (dyn), and due timedependent (due) as a temporal attribute; and positiondependent handling time (pos) as a handling time attribute. Among the above-mentioned authors, Zhou et al [62], Han et al [20], Xu et al [59], and Issam et al [29] have considered draft constraints in their mathematical formulations. Accordingly, as highlighted by Sheikholeslami et al [50], we considered the compatibility between ships' draft and berths' draft since it is relevant, especially for shallow ports where tide effects and low-draft access (e.g., channel port access) could influence the berth planning negatively.…”

Section: Literature Review On the Ddbapmentioning

confidence: 99%

A novel mathematical formulation for solving the dynamic and discrete berth allocation problem by using the Bee Colony Optimisation algorithm

Prencipe

Marinelli

2020

Appl Intell

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Berth allocation is one of the crucial points for efficient management of ports. This problem is complex due to all possible combinations for assigning ships to available compatible berths. This paper focuses on solving the Berth Allocation Problem (BAP) by optimising port operations using an innovative model. The problem analysed in this work deals with the Discrete and Dynamic Berth Allocation Problem (DDBAP). We propose a novel mathematical formulation expressed as a Mixed Integer Linear Programming (MILP) for solving the DDBAP. Furthermore, we adapted a metaheuristic solution approach based on the Bee Colony Optimisation (BCO) for solving large-sized combinatorial BAPs. In order to assess the solution performance and efficiency of the proposed model, we introduce a new set of instances based on real data of the Livorno port (Italy), and a comparison between the BCO algorithm and CPLEX in solving the DDBAP is performed. Additionally, the application of the proposed model to a real berth scheduling (Livorno port data) and a comparison with the Ant Colony Optimisation (ACO) metaheuristic are carried out. Results highlight the feasibility of the proposed model and the effectiveness of BCO when compared to both CPLEX and ACO, achieving computation times that ensure a real-time application of the method.

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