1,720,955 research outputs found
A Learning-Based Robust Optimization Framework for Resilient and Sustainable Synchromodal Freight Transportation Under Uncertainty
No full textSynchromodal freight transport is an advanced logistics approach that allows for the seam-
less integration and dynamic interchange between transport modes—such as road, rail, and
maritime transport. The increasing complexity of global logistics and the dynamic nature
of synchromodal transport necessitate innovative solutions for optimal decision-making under
real-world uncertainties and disruptions. This research presents a learning-based robust opti-
mization framework that integrates Machine Learning (ML), Operations Research (OR), and
uncertainty analysis to address the challenges of synchromodal transportation, aiming to derive
data-driven, explainable decisions that enhance system performance and resilience.
The proposed approach employs a predict-then-optimize framework, combining Bayesian
Neural Networks with uncertainty quantification and dynamic robust optimization modules
to solve the shipment matching problem within a synchromodal framework This integration is
achieved through scenario-based adjustable uncertainty sets, enabling the framework to generate
flexible plans. Decision-makers can evaluate trade-offs across various scenarios, improving the
adaptability and robustness of logistics operations.
In addition, this study incorporates disruption management to identify unexpected events
impacting transportation networks and dynamically re-plan to mitigate disruptions by proposing
Reassign with Delay Buffer and (De)consolidation strategies. Implemented for the Great Lakes
region with nine intermodal terminals and real-world data, the framework demonstrates its
efficacy in handling large-scale demand instances (up to 700 shipment requests). A heuristic-
based pre-processing algorithm for feasible path generation ensures timely solutions, further
enhancing computational efficiency.
Numerical experiments reveal that the integration of upstream ML-based prediction with
downstream optimization provides a range of optimal solutions instead of a single solution,
particularly focusing on variations in road travel times, transshipment operations, and storage
costs. By seamlessly merging the predictive capabilities of ML with the prescriptive strengths
of OR, the framework optimizes resource utilization, reduces operational costs, and lowers en-
vironmental impact, advancing synchromodal freight transport with a sustainable and resilient
approach.
This research aims to contribute to the field of synchromodal transportation by providing a
novel framework that integrates OR and ML techniques, offering practical solutions to enhance
logistics efficiency and reduce costs. By addressing the challenges and opportunities presented
by the digital transformation of transportation networks, this study seeks to pave the way for
more sustainable and resilient logistics systems in the future.ThesisDoctor of Philosophy (PhD)This research aims to enhance the efficiency and resilience of freight transportation through a
flexible framework known as synchromodal freight transport. Synchromodality enables dynamic
switching between different transportation methods—such as road, rail, and maritime—based on
real-time conditions. With increasing complexity in supply chains and transportation networks,
decision-making becomes particularly challenging when faced with disruptions and uncertain-
ties, such as road traffic congestions or unforeseen events.
To address these challenges, this thesis integrates machine learning, optimization, and uncer-
tainty analytic techniques to develop a predictive system capable of anticipating uncertainties in
delays and demand and adapting transportation plans accordingly. By using advanced methods
to account for uncertainties, this system helps logistics planners make better, more reliable de-
cisions that save time, reduce costs, and lower environmental impact. Tested in the Great Lakes
region, this approach shows promise in making freight transport more resilient and sustainable,
ultimately improving efficiency and reducing disruptions across complex logistics networks
Decarbonization through modal shift using a synchromodal platform: A case study in the Great Lakes
Get PDFThis paper offers an empirical study to explore the relationship between transportation modalities and environmental concerns, promoting the adoption of synchromodality as a strategic pathway to achieving sustainable freight transport. The study uses a synchromodal freight transportation platform to analyze the impact of carbon tax policy on modal shift and environmental sustainability. The synchromodal platform is based on an optimization model using Mixed Integer Linear Programming (MILP), incorporating carbon tax as a surrogate measure for environmental costs. A sensitivity analysis is conducted across four distinct scenarios in a case study in the Great Lakes region, focusing on the Canada-US transborder trade. The results of this study illustrate the considerable potential for increasing the utilization of more environmentally sustainable transportation modes in this region. While the addition of carbon tax entails increased total transportation costs for each unit of cargo, the synchromodal-enabled modal shift promises to mitigate transportation’s negative externalities, including congestion, environmental impacts, and noise pollution. The results also highlight the role of synchromodality as a catalyst for sustainable freight transport decisions in the context of a carbon-conscious world
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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