A Pareto Simulated Annealing for the Integrated Problem of Berth and Quay Crane Scheduling at Maritime Container Terminals with Multiple Objectives and Stochastic Arrival Times of Vessels

Nourmohammadzadeh, Abtin; Voß, Stefan

doi:10.1007/978-3-030-53552-0_30

Cited by 2 publications

(2 citation statements)

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“…Nourmohammadzadeh and Voß [34] is a basis for this current research, which provides a similar model for the BQCSP but uses stochastic optimisation to deal with the uncertain arrival times of vessels. The solution approaches in that work are the -constraints and the PSA, too.…”

Section: Related Workmentioning

confidence: 99%

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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%

“…Our mathematical model is a robust version of the one given in [34] with some modifications. This model is presented in the following three subsections explaining its assumptions, notations and formulations, respectively.…”

Section: Mathematical Modelmentioning

confidence: 99%