DC FieldValueLanguage
dc.contributor.authorKovač, Natašaen
dc.contributor.authorStanimirović, Zoricaen
dc.contributor.authorDavidović, Tatjanaen
dc.date.accessioned2020-04-03T08:16:01Z-
dc.date.available2020-04-03T08:16:01Z-
dc.date.issued2018-01-01en
dc.identifier.isbn978-3-319-61801-2-
dc.identifier.issn1868-4394en
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/249-
dc.description.abstractThe Minimum Cost Hybrid Berth Allocation problem is defined as follows: for a given list of vessels with fixed handling times, the appropriate intervals in berth and time coordinates have to be determined in such a way that the total cost is minimized. The costs are influenced by positioning of vessels, time of berthing, and time of completion for all vessels. Having in mind that the speed of finding high-quality solutions is of crucial importance for designing an efficient and reliable decision support system in container terminal, metaheuristic methods are the obvious choice for solving MCHBAP. In this chapter, we survey Evolutionary Algorithm (EA), Bee Colony Optimization (BCO), and Variable Neighborhood Descent (VND) metaheuristics, and propose General Variable Neighborhood Search (GVNS) approach for MCHBAP. All four metaheuristics are evaluated and compared against each other and against exact solver on real-life and randomly generated instances from the literature. The analysis of the obtained results shows that on instances reflecting real-life situations, all four metaheuristics were able to find optimal solutions in short execution times. The newly proposed GVNS showed to be superior over the remaining three metaheuristics in the sense of running times. Randomly generated instances were out of reach for exact solver, while EA, BCO, VND, and GVNS easily provided high-quality solutions in each run. The results obtained on generated data set show that the newly proposed GVNS outperformed EA, BCO, and VND regarding the running times while preserving the high quality of solutions. The computational analysis indicates that MCHBAP can be successfully addressed by GVNS and we believe that it is applicable to related problems in maritime transportation.en
dc.publisherSpringer Link-
dc.relationGraph theory and mathematical programming with applications in chemistry and computer science-
dc.relationMathematical Modelas and Optimization Methods on Large-Scale Systems-
dc.relation.ispartofModeling, Computing and Data Handling Methodologies for Maritime Transportationen
dc.relation.ispartofseriesIntelligent Systems Reference Library-
dc.subjectBee-colony optimization | Container terminal | Evolutionary algorithm | Penalties | Scheduling vessels | Variable neighborhood searchen
dc.titleMetaheuristic approaches for the minimum cost hybrid berth allocation problemen
dc.typeBook Chapteren
dc.identifier.doi10.1007/978-3-319-61801-2_1en
dc.identifier.scopus2-s2.0-85029461092en
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Arts-
dc.relation.firstpage1en
dc.relation.lastpage47en
dc.relation.volume131en
dc.description.rankM14-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairetypeBook Chapter-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
crisitem.author.orcid0000-0001-9561-5339-
crisitem.project.projectURLhttp://www.mi.sanu.ac.rs/novi_sajt/research/projects/174033e.php-
crisitem.project.projectURLhttp://www.mi.sanu.ac.rs/novi_sajt/research/projects/174010e.php-
crisitem.project.fundingProgramDirectorate for Computer & Information Science & Engineering-
crisitem.project.fundingProgramDirectorate for Engineering-
crisitem.project.openAireinfo:eu-repo/grantAgreement/NSF/Directorate for Computer & Information Science & Engineering/1740333-
crisitem.project.openAireinfo:eu-repo/grantAgreement/NSF/Directorate for Engineering/1740103-
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