Coordinated optimization of equipment operations in a container terminal

dc.contributor.authorJonker, T.
dc.contributor.authorDuinkerken, Mark B.
dc.contributor.authorYorke-Smith, Neil
dc.contributor.authorde Waal, A.
dc.contributor.authorNegenborn, Rudy R.
dc.contributor.departmentOSB
dc.contributor.facultySuliman S. Olayan School of Business (OSB)
dc.contributor.institutionAmerican University of Beirut
dc.date.accessioned2025-01-24T12:15:52Z
dc.date.available2025-01-24T12:15:52Z
dc.date.issued2021
dc.description.abstractIncreasing international maritime transport drives the need for efficient container terminals. The speed at which containers can be processed through a terminal is an important performance indicator. In particular, the productivity of the quay cranes (QCs) determines the performance of a container terminal; hence QC scheduling has received considerable attention. This article develops a comprehensive model to represent the waterside operations of a container terminal. Waterside operations comprise single and twinlift handling of containers by QCs, automated guided vehicles and yard cranes. In common practice, an uncoordinated scheduling heuristic is used to dispatch the equipment operating on a terminal. Here, uncoordinated means that the different machines that operate in the container terminal seek optimal productivity solely considering their own respective stage. By contrast, our model provides a coordinated schedule in which operations of all terminal equipment can be considered at once to achieve productivity closer to the QC optimal. The model takes the form of a hybrid flow shop (HFS) with novel features for bi-directional flows and job pairing. The former enables jobs to move freely through the HFS in both directions; the latter constrains certain jobs to be performed simultaneously by a single machine. We solve the coordinated model by means of a tailored simulated annealing (SA) algorithm that balances solution quality and computational time. We empirically study time-bounded variants of SA and compare them with a branch-and-bound algorithm. We show that our approach can produce coordinated schedules for a terminal with up to eight QCs in near real time. © 2019, The Author(s).
dc.identifier.doihttps://doi.org/10.1007/s10696-019-09366-3
dc.identifier.eid2-s2.0-85069750267
dc.identifier.urihttp://hdl.handle.net/10938/33464
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofFlexible Services and Manufacturing Journal
dc.sourceScopus
dc.subjectHybrid flow shop
dc.subjectPort operations
dc.subjectScheduling
dc.subjectSimulated annealing
dc.subjectAutomatic guided vehicles
dc.subjectBranch and bound method
dc.subjectContainers
dc.subjectCranes
dc.subjectMachine shop practice
dc.subjectPort terminals
dc.subjectProductivity
dc.subjectRailroad yards and terminals
dc.subjectAutomated guided vehicles
dc.subjectBranch-and-bound algorithms
dc.subjectCoordinated optimization
dc.subjectPerformance indicators
dc.subjectScheduling heuristics
dc.subjectSimulated annealing algorithms
dc.subjectJob shop scheduling
dc.titleCoordinated optimization of equipment operations in a container terminal
dc.typeArticle

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