Capacity Control and Distribution Problem for Manufactures in Supply Chain Networks
Bibliographic record
Abstract
Most manufacturing firms have focused on managing efficiently their supply chains that purchasing raw materials, producing final products, and supplying them to retailers. Since a supply chain network is composed of several stages and components, a little variation of retail sales may result in significant changes for each component on supply chains. In this view, a manufacturer is expected both to synchronize its products with the retailer's demand and to coordinate the ordering of raw materials with production processes so that both raw materials and final goods inventories are reduced. In general, the market for final goods can be grouped into different segments, and suppliers can sell the same goods or services to different segments for different price and supply policies to maximize their total revenues. That is the basic concept of RM (Revenue Management) techniques. The success of airline RM has been widely reported, and stimulated development of RM systems for other transportation and service sectors such as hotels, cruise lines, rental cars, retail etc. (McGill and Van Ryzin, 1999; Feng and Xiao, 2006). This paper addresses an integration of SCM(Supply Chain Management) and RM problems in manufacturing systems, specifically, the simultaneous determination of procurement of raw materials, production plan and supply policy for each customer in the circumstance of demand uncertainty. We focus on modeling our problem as a stochastic dynamic programming model. Applying RM techniques, we will develop an optimization model to solve our comprehensive problem encountered in manufacturing, and some computational results with randomly generated problems are reported.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".