Research Repository

Robust berth allocation using a hybrid approach combining branch-and-cut and the genetic algorithm

Alsoufi, G and Yang, X and Salhi, A (2016) 'Robust berth allocation using a hybrid approach combining branch-and-cut and the genetic algorithm.' In: UNSPECIFIED, (ed.) UNSPECIFIED UNSPECIFIED, 187 - 201. ISBN 9783319396354

[img] Text
RBAP_finalVer.pdf
Restricted to Repository staff only

Download (278kB)

Abstract

© Springer International Publishing Switzerland 2016. Seaside operations at container ports often suffer from uncertainty due to events such as the variation in arrival and/or processing time of vessels, weather conditions and others. Finding a robust plan which can accommodate this uncertainty is therefore desirable to port operators. This paper suggests ways to generate robust berth allocation plans in container terminals. The problem is first formulated as a mixed-integer programming model whose main objective is to minimize the total tardiness of vessel departure time. It is then solved exactly and approximately. Experimental results show that only small instances of the proposed model can be solved exactly. To handle large instances in reasonable times, the Genetic Algorithm (GA) is used. However, it does not guarantee optimality and often the approximate solutions returned are of low quality. A hybrid meta-heuristic which combines Branch-and- Cut (B & C) as implemented in CPLEX, with the GA as we implement it here, is therefore suggested. This hybrid method retains the accuracy of Branch-and-Cut and the efficiency of GA. Numerical results obtained with the three approaches on a representative set of instances of the problem are reported.

Item Type: Book Section
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Xinan Yang
Date Deposited: 29 Jun 2016 12:38
Last Modified: 17 Aug 2017 17:26
URI: http://repository.essex.ac.uk/id/eprint/17067

Actions (login required)

View Item View Item