MétaCan
Menu
Back to cohort
Record W4414568697 · doi:10.1016/j.ifacol.2025.09.179

Designing Efficient Supply Chains with Unreliable Suppliers

2025· article· en· W4414568697 on OpenAlexafffund
Amir Azaron, Kai Furmans

Bibliographic record

VenueIFAC-PapersOnLine · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsKwantlen Polytechnic UniversityUniversity of British Columbia
FundersKwantlen Polytechnic UniversityAlexander von Humboldt-Stiftung
KeywordsSupply chainMarkov chainProduction (economics)Finished goodTime horizonMarkov processService (business)Random variableExponential distribution

Abstract

fetched live from OpenAlex

In this research, a novel two-stage stochastic model is developed to find the optimal locations of the potential manufacturing facilities at the first stage, and to determine the optimal production levels, inventory levels, and quantities of raw materials and finished goods shipped among the members of the supply chain network at the second stage. The main feature of this research is that some suppliers are unreliable, and each unreliable supplier’s lifetime is assumed to be an exponentially distributed random variable. However, once the supplier breaks down or loses its ability to supply raw materials, it can be repaired. The length of time the supplier is out of service or does not operate is assumed to be another exponentially distributed random variable. The transitions between the two states of available when the supplier normally operates and unavailable when it is out of service follow a continuous-time Markov chain. Demands at markets are also random variables following normal distributions. The goal is to minimize the sum of first stage construction costs and the expected second stage production, inventory and shipping costs over the planning horizon while meeting service levels at the markets.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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 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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueIFAC-PapersOnLineSame topicSupply Chain Resilience and Risk ManagementFrench-language works237,207