Design and Development of an Intelligent Agent-Based Supply Chain Simulation System
Bibliographic record
Abstract
An intelligent agent-based supply chain simulation model, in which each enterprise/consumer is represented by an agent, is designed. There are six layers in this supply chain simulation model: raw material providers, component manufacturers, product assemblers, product holders, retailers, and final customers. Each entity in the supply chain represented by an agent has five components: interface, task distribution, business processing activities, knowledge management and decision support, and information storage. A detailed agent structure is designed and various functions of an agent including communication among agents are described. Issues in the supply chain integration, information sharing among supply chain partners, demand forecasting, supply chain risk management, and automated communication and negotiation, could be simulated and studied by using the proposed system. Based on the proposed supply chain simulation model, a generic six-layer prototype mobile phone supply chain simulation system is designed, developed and implemented. The system allows a user to setup and adjust a large number of parameters, including (1) simulation period, loan and saving interest rates; (2) customers' behavior and market demand; (3) each retailer's initial cash, loan, market share, inventories, Order Amount Policy and Order Point Strategy; (4) each product holder's initial cash, loan, market share, inventories, Order Amount Policy, Order Point Strategy and inventory strategy; (5) each assembler's and component agent's initial cash, loan, inventories, Order Amount Policy, Order Point Strategy, production strategy, and production capacities; and (6) each material provider's initial cash, loan, inventory, production strategy, and production capacities. Extensive simulation studies are carried out to examine and compare many supply chain management strategies and agent behaviors. This system can be used to test which strategy is most suitable in certain environments, The generic supply chain simulation system developed can be used in a number of ways, including: as an analysis tool for entity in a supply chain from the entity's perspective; as a tool for studying supply chain coordination and integration from the perspective of an entire supply chain, or portion of it; as a tool to design supply chains by answering "what-if" questions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".