Towards Formal Reliability Analysis of Logistics Service Supply Chains using Theorem Proving
Bibliographic record
Abstract
Logistics service supply chains (LSSCs) are composed of several nodes, with distinct behaviors, that ensure moving a product or service from a producer to consumer. Given the usage of LSSC in many safety-critical applications, such as hospitals, it is very important to ensure their reliable operation. For this purpose, many LSSC structures are modelled using Reliability Block Diagrams (RBDs) and their reliability is assessed using paper-and-pencil proofs or computer simulations. Due to their inherent incompleteness, these analysis techniques cannot ensure accurate reliability analysis results. In order to overcome this limitation, we propose to use higher-order-logic (HOL) theorem proving to conduct the RBD-based reliability analysis of LSSCs in this paper. In particular, we present the higher-order-logic formalizations of LSSNs with different and same types of capacities. As an illustrative example, we also present the formal reliability analysis of a simple three-node corporation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".