Bibliographic record
Abstract
Internet-based technologies have changed the way firms do business and manage their supply chains. They have influenced customers’ purchase patterns, thereby motivating manufacturers to introduce online channels alongside traditional ones. Such structures are known as dual-channels. Nowadays, an increasing number of manufacturers offer a return policy to attract more customers and to stay competitive. Furthermore, learning-based continuous improvements help firms cope with market changes and be competitive, flexible and efficient. This thesis presents three main models: The first model investigates the effect of adopting a dual-channel (comprised of a retail channel and an online channel) on the performance of a two-level (vendor-retailer) supply chain. The objective is to maximize the total profit of the system by finding the optimal markup margin and inventory decisions before and after adopting the dual-channel. The results show that adding an online channel would increase the profit of the system. However, it creates a conflict due to competition between the retail and online channels. The second model studies a supply chain system, which is comprised of production, refurbishing, collection, and waste disposal processes. A return policy in which customers can return the purchased item for a refund is also considered. The purpose is to examine the effect of different return policies on the behavior of the system before and after adopting the dual-channel strategy. In both strategies, the model analyzes the change in the profit, the pricing and inventory decisions. The findings demonstrate that the more generous the return policy is, the higher the demand, the selling prices and the overall profit. The third model investigates the effects of learning and forgetting in the vendor’s production processes. It also considers single- and dual-channel strategies. Each channel structure can adopt any of six inventory policies. Learning and forgetting effects are considered in all policies except one. The objective is to maximize the profit of the system by finding the joint optimal pricing and inventory decisions. The results suggests that learning, despite being impeded by forgetting, reduces inventory-related costs thereby allowing the chain to reduce the prices of its product(s), which increases demand and subsequently sales.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".