Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ejor.2012.06.025
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dc.titleShort-term liner ship fleet planning with container transshipment and uncertain container shipment demand
dc.contributor.authorMeng, Q.
dc.contributor.authorWang, T.
dc.contributor.authorWang, S.
dc.date.accessioned2014-10-09T07:39:26Z
dc.date.available2014-10-09T07:39:26Z
dc.date.issued2012-11-16
dc.identifier.citationMeng, Q., Wang, T., Wang, S. (2012-11-16). Short-term liner ship fleet planning with container transshipment and uncertain container shipment demand. European Journal of Operational Research 223 (1) : 96-105. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ejor.2012.06.025
dc.identifier.issn03772217
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/91218
dc.description.abstractThis paper proposes a short-term liner ship fleet planning problem by taking into account container transshipment and uncertain container shipment demand. Given a liner shipping service network comprising a number of ship routes, the problem is to determine the numbers and types of ships required in the fleet and assign each of these ships to a particular ship route to maximize the expected value of the total profit over a short-term planning horizon. These decisions have to be made prior to knowing the exact container shipment demand, which is affected by some unpredictable and uncontrollable factors. This paper thus formulates this realistic short-term planning problem as a two-stage stochastic integer programming model. A solution algorithm, integrating the sample average approximation with a dual decomposition and Lagrangian relaxation approach, is then proposed. Finally, a numerical example is used to evaluate the performance of the proposed model and solution algorithm. © 2012 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.ejor.2012.06.025
dc.sourceScopus
dc.subjectContainer transshipment and uncertain demand
dc.subjectLiner shipping
dc.subjectLogistics
dc.subjectSample average approximation with dual decomposition and Lagrangian relaxation
dc.subjectStochastic integer programming
dc.typeArticle
dc.contributor.departmentCIVIL & ENVIRONMENTAL ENGINEERING
dc.description.doi10.1016/j.ejor.2012.06.025
dc.description.sourcetitleEuropean Journal of Operational Research
dc.description.volume223
dc.description.issue1
dc.description.page96-105
dc.description.codenEJORD
dc.identifier.isiut000307796100009
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