Optimal Ordering Decision and Incentives for Yield Improvement under Random Demand
Bibliographic record
Abstract
In this thesis, we focus on the applications of incentive mechanism design in operations and supply chain management (OSCM). Most significant--and interesting--topics arising in OSCM are concerned with the management of relationships among supply chain members under asymmetric information. Since the incentive mechanism design based on the principal-agent model deals with asymmetric information in a satisfactory way, it has become an important tool in investigating OSCM-related asymmetric information problems. We start with an introduction in Chapter 1. In this chapter, we briefly describe the theory of incentive mechanism design and its applications to OSCM, and the organization structure of this thesis. In Chapter 2, we study the optimal wage scheme and effort level in a contracting problem where both the principal and the agent are risk-averse. This chapter is a starting point for analyzing the buyer's optimal ordering decision and incentives for yield improvement in Chapters 3 and 4. Chapter 3 investigates the buyer's optimal ordering decision and incentives for yield improvement in the setting of random yield for the critical component and uncertain demand for the finished product. In Chapter 4, we assume the supplier's effort and yield become continuous and study a continuous optimization problem where the buyer decides the optimal order quantities and incentives for yield improvement under random demand. Our thesis ends with a conclusion and addresses the future research in Chapter 5.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".