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Record W4231106073 · doi:10.1504/ejie.2020.10031931

A Systematic Literature Review of the Design of Intermodal Freight Transportation Networks Addressing Location-Allocation Decisions

2020· article· en· W4231106073 on OpenAlexaff
José Moyano‐Fuentes, José Pedro García Sabater, Ángel Ruiz, Anny Del Mar Agamez Arias

Bibliographic record

VenueEuropean J of Industrial Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSupply chainOperations researchNetwork planning and designComputer scienceSystematic reviewKey (lock)Supply chain managementTransport engineeringManagement scienceEngineeringBusinessMarketingTelecommunications

Abstract

fetched live from OpenAlex

This systematic literature review focuses on planning models jointly addressing location and allocation decisions related to the design of intermodal freight transportation networks. Since this body of literature is evolving quickly, a methodology based on a linked two-stage analysis is proposed. The first stage analyses recent surveys to establish the guidelines and criteria that enable the subsequent systematic review. Then, the review concentrates on analysing contributions to the current state of the art on intermodal freight transportation from two close, yet different research streams: transportation networks and supply chain networks. Key features identified in the first stage such as: 1) the research problem's characteristics; 2) the intermodal networks design's particularities; 3) proposed solution techniques, among others, are used to classify and analyse the different contributions. The review identifies current trends, emerging topics and some issues that merit being researched. [Received: 4 May 2019; Revised: 12 December 2019; Accepted: 1 February 2020]

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.068
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0160.016
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.049
GPT teacher head0.214
Teacher spread0.165 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2020
Admission routes1
Has abstractyes

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