@Article{MauriRibeLoreLapo:2016:AdLaNe,
author = "Mauri, Regis Mauri and Ribeiro, Glaydston Mattos and Lorena, Luiz
Antonio Nogueira and Laporte, Gilbert",
affiliation = "{Universidade Federal do Esp{\'{\i}}rito Santo (UFES)} and
{Universidade Federal do Rio de Janeiro (UFRJ)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {CIRRELT and HEC
Montr{\'e}al}",
title = "An adaptive large neighborhood search for the discrete and
continuous Berth allocation problem",
journal = "Computers \& Operations Research",
year = "2016",
volume = "70",
pages = "140--154",
month = "June",
keywords = "Berth allocation problem, Metaheuristic, Adaptive large
neighborhood search.",
abstract = "The Berth Allocation Problem (BAP) consists of assigning ships to
berthing positions along a quay in a port. The choice of where and
when the ships should move is the main. decision to be made in
this problem. Considering the berthing positions, there are
restrictions related to the water depth and the size of the ships
among others. There are also restrictions related to the berthing
time of the ships which are modeled as time windows. In this work
the ships are represented as rectangles to be placed into a space
x time area, avoiding overlaps and satisfying time window
constraints. We consider discrete and continuous models for the
BAP and we propose an Adaptive Large Neighborhood Search heuristic
to solve the problem. Computational experiments indicate that the
proposed algorithm is capable of generating high-quality solutions
and outperforms competing algorithms for the same problem. In most
cases the improvements are statistically significant.",
doi = "10.1016/j.cor.2016.01.002",
url = "http://dx.doi.org/10.1016/j.cor.2016.01.002",
issn = "0305-0548",
language = "en",
targetfile = "mauri_adaptive.pdf",
urlaccessdate = "27 abr. 2024"
}