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Record W3214896910 · doi:10.1080/25725084.2021.1982636

A simulation approach towards a sustainable and efficient container terminal layout design

2021· article· en· W3214896910 on OpenAlexaffabout
Tareq Abu-Aisha, Mustapha Ouhimmou, Marc Paquet

Bibliographic record

VenueJournal of International Maritime Safety Environmental Affairs and Shipping · 2021
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsTerminal (telecommunication)Container (type theory)Port (circuit theory)Discrete event simulationPage layoutComputer scienceTransport engineeringEngineeringSimulationTelecommunicationsBusinessElectrical engineering

Abstract

fetched live from OpenAlex

Seaports are considered gateways for international maritime trade. The flow of trade and the size of containerships have been growing exponentially in the last two decades, and as a result, the exchange of containers has increased dramatically. In consequence, adapting container terminal designs is required in order to confront these new challenges more efficiently and with improved sustainability. This paper studies the effect of changing the layout of seaport terminals by taking into consideration both costs and emissions. A new design of a container terminal located in the Port of Montreal is proposed and compared with the current layout. A discrete event-based simulation approach is adopted to investigate the impact of terminal layout on economic and environmental performance. This study aims to improve the performance and handling capacity of a terminal by recommending a new layout. Computational experiments were conducted to evaluate and compare the performance of both layouts using data collected from the Port of Montreal. The results indicate that terminal layout design has a significant impact on terminal performance and emission under the configuration of different transportation modes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.200
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2021
Admission routes2
Has abstractyes

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