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Record W4200634922 · doi:10.4337/9781800370173.00019

Operations research for planning and managing city logistics systems

2023· book-chapter· en· W4200634922 on OpenAlexafffund
Teodor Gabriel Crainic, Jesús González-Feliu, Nicoletta Ricciardi, Frédéric Semet, Tom Van Woensel

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2023
Typebook-chapter
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCentre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transportUniversité de Montréal
KeywordsBusinessHumanitarian LogisticsCity logisticsTraffic managementIntegrated logistics supportProcess managementSupply chainIntegrated business planningOperations managementOperations researchTransport engineeringEngineeringMarketing

Abstract

fetched live from OpenAlex

City Logistics defines an integrated logistics system, based on stakeholders' cooperation, resource sharing, consolidation, synchronization of operations, multi- and intermodal transport, and the separation of commercial transactions generating demand for goods movements from the planning and execution of the supply activities addressing this demand. Operations Research provides the methodology to design and deploy the advanced planning and management decision-support tools needed to account for the complexity of City Logistics systems and to reach their goals of service, economic, and environmental efficiency, the latter being particularly important in view of the growing climate-change crisis and the immense and accelerating impact of transportation on climate change. We recall the systemic view of City Logistics and its paradigm-changing role for urban freight transportation and logistics. We then review the main Operations Research methods addressing the City Logistics supply-planning issues at strategic, tactical-operational, and dynamic-management levels.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.827
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0020.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.215
GPT teacher head0.302
Teacher spread0.087 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations14
Published2023
Admission routes2
Has abstractyes

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