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Record W4241480063 · doi:10.32920/ryerson.14649348

Inventory routing of industrial gases with stochastic demand

2021· preprint· en· W4241480063 on OpenAlexaff
Bhupeshkumar Gandhi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStockoutMathematical optimizationOperations researchSensitivity (control systems)Computer scienceRouting (electronic design automation)Holding costVehicle routing problemEngineeringMathematics

Abstract

fetched live from OpenAlex

This report proposes a methodology to solve the inventory routing problem of industrial gases with stochastic demand. The gas tanker distributes gases from a depot to several dispersed customers in a route, and each customer has stochastic demand modeled by a Brownian motion. The proposed model determines the optimal quantity required to refill each customer by minimizing the cost associated with earliness, which increases number of visits per year, and lateness, which increases probability of stockout. Overall, the proposed model minimizes the total system cost, helps find the optimal tanker capacity for a given route, and improves supplier and customers' relationship. Numerical examples and sensitivity analysis are given to illustrate the proposed model.

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 score0.628
Threshold uncertainty score0.778

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 integrity0.0000.001
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 head0.059
GPT teacher head0.205
Teacher spread0.146 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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