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Article: A novel mathematical model and a large neighborhood search algorithm for container drayage operations with multi-resource constraints

TitleA novel mathematical model and a large neighborhood search algorithm for container drayage operations with multi-resource constraints
Authors
KeywordsContainer drayage scheduling
Linearization
Mathematical modeling
Multi-resource constraint
Large neighborhood search
Issue Date2020
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/cie
Citation
Computers & Industrial Engineering, 2020, v. 139, p. article no. 106143 How to Cite?
AbstractMulti-resource constraints in container drayage operations exist widely in real-life scenarios but have been seldom reported in literature. This study mainly addresses a container drayage problem with a limited number of empty containers at depots, with the goal of minimizing both the number of trucks in operation and the total working time of trucks. The problem is firstly formulated as a bi-objective mathematical model based on a so-called determined-activities-on-vertex graph. Three schemes are proposed to handle the developed mathematical model as: (a) a nonlinear constraint is linearized; (b) a parameter that is a large enough constant in the model is analyzed and tuned deeply; and (c) the bi-objective model is converted into a single-objective model. Furthermore, a large neighborhood search algorithm is designed to solve the problem. The two solution methods are validated and evaluated based on randomly generated instances as well as instances from literature. Numerical experimental results indicate that the methods can provide optimal or near-optimal solutions for medium- and large-scale instances in a short running time.
Persistent Identifierhttp://hdl.handle.net/10722/287150
ISSN
2019 Impact Factor: 4.135
2015 SCImago Journal Rankings: 1.630

 

DC FieldValueLanguage
dc.contributor.authorZhang, R-
dc.contributor.authorHuang, C-
dc.contributor.authorWang, J-
dc.date.accessioned2020-09-22T02:56:32Z-
dc.date.available2020-09-22T02:56:32Z-
dc.date.issued2020-
dc.identifier.citationComputers & Industrial Engineering, 2020, v. 139, p. article no. 106143-
dc.identifier.issn0360-8352-
dc.identifier.urihttp://hdl.handle.net/10722/287150-
dc.description.abstractMulti-resource constraints in container drayage operations exist widely in real-life scenarios but have been seldom reported in literature. This study mainly addresses a container drayage problem with a limited number of empty containers at depots, with the goal of minimizing both the number of trucks in operation and the total working time of trucks. The problem is firstly formulated as a bi-objective mathematical model based on a so-called determined-activities-on-vertex graph. Three schemes are proposed to handle the developed mathematical model as: (a) a nonlinear constraint is linearized; (b) a parameter that is a large enough constant in the model is analyzed and tuned deeply; and (c) the bi-objective model is converted into a single-objective model. Furthermore, a large neighborhood search algorithm is designed to solve the problem. The two solution methods are validated and evaluated based on randomly generated instances as well as instances from literature. Numerical experimental results indicate that the methods can provide optimal or near-optimal solutions for medium- and large-scale instances in a short running time.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/cie-
dc.relation.ispartofComputers & Industrial Engineering-
dc.subjectContainer drayage scheduling-
dc.subjectLinearization-
dc.subjectMathematical modeling-
dc.subjectMulti-resource constraint-
dc.subjectLarge neighborhood search-
dc.titleA novel mathematical model and a large neighborhood search algorithm for container drayage operations with multi-resource constraints-
dc.typeArticle-
dc.identifier.emailWang, J: jwwang@hku.hk-
dc.identifier.authorityWang, J=rp01888-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.cie.2019.106143-
dc.identifier.scopuseid_2-s2.0-85074703111-
dc.identifier.hkuros314573-
dc.identifier.volume139-
dc.identifier.spagearticle no. 106143-
dc.identifier.epagearticle no. 106143-
dc.publisher.placeUnited Kingdom-

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