ANALYSIS OF MARKOV CHAIN MONTE CARLO METHODS IN MULTI-INDENTURE INVENTORY OPTIMIZATION
Bibliographic record
Abstract
U.S. Navy aircraft are required to meet minimum operational availability targets, while minimizing spare parts procurement costs. The current optimization model written by Salmeron and Buss, uses marginal analysis, as described by Sherbrooke, to determine optimal sparing policies for this highly complex multi-indenture model. The literature lacks alternative optimization methodologies for such a problem, so we propose an alternative approach utilizing simulated annealing (SA), a Markov Chain Monte Carlo algorithm. We present three SA approaches tested in three case studies of varying size and complexity. Our initial findings show that in very simple problems, SA is easily capable of outperforming marginal analysis; however, problems with more complexity have large optimality gaps. This is likely because the SA Markov chain is unable to effectively explore the multi-indenture structure of the problem. We implement a method to account for this structure that intelligently builds initial feasible solutions using an epsilon-greedy approach to marginal analysis. This approach produces better results than NAVARM in more than half of the trials on problems of moderate complexity. We also implement a novel method for calculating operational availability that may allow full scale problems to be optimized more efficiently.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".