Abstract
Abstract This paper addresses the well-known dynamic berth allocation problem (DBAP), which finds numerous applications at container terminals aiming to allocate and schedule incoming container vessels into berthing positions along the quay. Due to its impact on ports’ performance, having efficient DBAP formulations is of great importance, especially for determining optimal schedules in quick time as well as aiding managers and developers in the assessment of solution strategies and approximate approaches. In this work, we propose two novel formulations, a time-indexed formulation and an arc-flow one, to efficiently tackle the DBAP. Additionally, to improve computational performance, we propose problem-based modeling enhancements and a variable-fixing procedure that allows to discard some variables by considering their reduced costs. By means of these contributions, we improve the models’ performance for those instances where the optimal solutions were already known, and we solve to optimality for the first time other instances from the literature.
Highlights
The management of limited resources at maritime container terminals has a direct and relevant impact on their productivity and competitiveness
As a follow up of previous studies on this problem, see (Buhrkal et al, 2011), our goal is to provide a detailed comparison between our formulations and the best one proposed in the literature so far, in order to determine their performance and their likely complementarity for tackling the different dynamic berth allocation problem (DBAP) benchmark instances
The dynamic berth allocation problem (DBAP) was initially proposed by Imai et al (2001) with the goal of scheduling and allocating vessels along a discrete quay partitioned into berths
Summary
The management of limited resources at maritime container terminals has a direct and relevant impact on their productivity and competitiveness. Terminal managers require the use of suitable methods and approaches to efficiently exploit resources at maritime terminals This involves the need for reliable and fast approaches for providing schedules within reasonable computational times, as well as having efficient mathematical models enabling the proper evaluation of those schedules by means of a given objective function. The integration of exact approaches within a metaheuristic or viceversa can lead to higher computational times than heuristic methods, but may lead to a better performance robustness and quality of the solutions in some applications. As a follow up of previous studies on this problem, see (Buhrkal et al, 2011), our goal is to provide a detailed comparison between our formulations and the best one proposed in the literature so far, in order to determine their performance and their likely complementarity for tackling the different DBAP benchmark instances.
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