Novel formulations and modeling enhancements for the dynamic berth allocation problem

Kramer, Arthur; Lalla-Ruiz, Eduardo; Iori, Manuel; Voß, Stefan

doi:10.1016/j.ejor.2019.03.036

Cited by 37 publications

(17 citation statements)

References 34 publications

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“…It is proved that the approaches are able to provide promising results for this problem. The methods proposed in [25] provide excellent results at that time and the presented modelling improvements allow to stretch the limits of problem sizes to be solved efficiently. The work of [20] investigates the berth allocation problem with stochastic vessel handling times as a bi-objective problem.…”

Section: Related Workmentioning

confidence: 99%

A robust multiobjective model for the integrated berth and quay crane scheduling problem at seaside container terminals

Nourmohammadzadeh

Voß

2021

Ann Math Artif Intell

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The ever increasing demand for container transportation has led to the congestion of maritime container terminals in the world. In this work, the two interrelated problems of berth and quay crane scheduling are considered in an integrated multiobjective mathematical model. A special character of this model is that the arrival times of vessels and the failure (working) times of quay cranes are not deterministic and can vary based on some scenarios. Hence, a robust model is devised for the problem having three objectives of minimising the deviations from target berthing locations and times as well as departure delays of all vessels. This robust optimisation seeks to minimise the value of the objectives regarding all the scenarios. An exact solution approach based on the 𝜖-constraint method by the Gurobi software is applied. Moreover, regarding the complexity of the problem, two Simulated Annealing (SA) based metaheuristics, namely a Multi-Objective Simulated Annealing (MOSA) and a Pareto Simulated Annealing (PSA) approach are adapted with a novel solution encoding scheme. The three methods are compared based on some multiobjective metrics and a statistical test. The advantage of the integration of berth and quay crane scheduling is examined as well.

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Section: Related Workmentioning

confidence: 99%

A robust multiobjective model for the integrated berth and quay crane scheduling problem at seaside container terminals

Nourmohammadzadeh

Voß

2021

Ann Math Artif Intell

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“…Buhrkal et al [3] improved the mathematical formulation by Cordeau et al [4] by introducing a Generalized Set Partitioning Problem (GSPP) mathematical formulation. Lalla-Ruiz et al [34] proposed a hybrid heuristic combining a TS metaheuristic with an artificial intelligence method, namely the Path Relinking algorithm, for solving the DDBAP. They compared exact solutions of the GSPP mathematical formulation proposed by Buhrkal et al [2] and near-optimal solutions of the TS metaheuristic proposed by Cordeau et al [4].…”

Section: Literature Review On the Ddbapmentioning

confidence: 99%

“…In the DBAP, arrival times are scheduled and assigned to each ship, in order to avoid overlapping. Recently, Kramer et al [34] have proposed two novel formulations: a time-indexed formulation and an arc-flow formulation for solving a big-size DBAP considering 250 ships and 20 berths instances in a reasonable computation time. Generally, various models aim at minimising the sum of the ships' waiting time or the total staying time for completing the activities, increasing the attractiveness and efficiency of container terminal ports.…”

Section: Introductionmentioning

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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“…Not coincidentally, Hsu et al [19] dealt with the dynamic and continuous berth allocation problem (DCBAP) in which both arrived and incoming ships are considered and a quay is used as a continuous line to accommodate as many ships as possible at one time. Kramer et al [20] presented two novel formulations, a time-indexed formulation and an arc-flow one, to efficiently tackle the dynamic berth allocation problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Container Terminal Logistics Generalized Computing Architecture and Green Initiative Computational Pattern Performance Evaluation

Song

2019

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Container terminals are the typical representatives of complex supply chain logistics hubs with multiple compound attributes and multiple coupling constraints, and their operations are provided with the strong characteristics of dynamicity, nonlinearity, coupling, and complexity (DNCC). From the perspective of computational logistics, we propose the container terminal logistics generalized computing architecture (CTL-GCA) by the migration, integration, and fusion of the abstract hierarchy, design philosophy, execution mechanism, and automatic principles of computer organization, computing architecture, and operating system. The CTL-GCA is supposed to provide the problem-oriented exploration and exploitation elementary frameworks for the abstraction, automation, and analysis of green production at container terminals. The CTL-GCA is intended to construct, evaluate, and improve the solution to planning, scheduling, and decision at container terminals, which all are nondeterministic polynomial hard problems. Subsequently, the logistics generalized computational pattern recognition and performance evaluation of a practical container terminal service case study is launched by the qualitative and quantitative approach from the sustainable perspective of green production. The case study demonstrates the application, utilization, exploitation, and exploration of CTL-GCA preliminarily, and finds the unsustainable patterns of production at the container terminal. From the above, we can draw the following conclusions. For one thing, the CTL-GCA makes a definition of the abstract and automatic running architecture of logistics generalized computation for container terminals (LGC-CT), which provides an original framework for the design and implementation of control and decision mechanism and algorithm. For another, the CTL-GCA can help us to investigate the roots of DNCC thoroughly, and then the CTL-GCA makes for conducting the efficient and sustainable running pattern recognition of LGC-CT. It is supposed to provide a favorable guidance and supplement to define, design, and implement the agile, efficient, sustainable, and robust task scheduling and resource allocation for container terminals by computational logistics whether in the strategy level or the tactical one.

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Novel formulations and modeling enhancements for the dynamic berth allocation problem

Cited by 37 publications

References 34 publications

A robust multiobjective model for the integrated berth and quay crane scheduling problem at seaside container terminals

A robust multiobjective model for the integrated berth and quay crane scheduling problem at seaside container terminals

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

Container Terminal Logistics Generalized Computing Architecture and Green Initiative Computational Pattern Performance Evaluation

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