A Micro Simulated and Demand Driven Supply Chain Model To Estimate Regional Production and Consumption Relations
Bibliographic record
Abstract
The various operations of production, distribution and consumption of goods make up supply chain networks. The usual pre requisite to model freight flows on between geographical zones, is an understanding of intra zonal production and consumption relations. This is in essence an aggregation of all individual firm to firm interactions taking place in form of production and consumption of goods, as well as transportation firms involved in the goods exchange process. In this paper, a demand driven micro-simulated supply chain model is presented. The presented model is a microsimulation model modeling the main firm types present in a traditional supply chain. It is a demand driven model using quantity of goods requested at the consumer side as a starting point. The different interacting firms use a modified Economic Order Quantity (EOQ) model as the basis of the simulation process. The cost function used includes also transportation and labor costs. The model simulates also shipping and carrying firms and takes into account raw material suppliers able to supply all or some subcomponents needed by production firms. Different initial conditions can be used to mimic real life firm to firm interactions. Firm level and zone level scenarios are simulated and results are shown.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".