Solving Dynamic Multi-Criteria Resource-Target Allocation Problem Under Uncertainty: A Comparison of Decomposition and Myopic Approaches
Bibliographic record
Abstract
This paper is concerned with multi-criteria and dynamic resource allocation problem in a naval engagement context. The scenario under investigation considers air threats directed towards a ship that has to plan its engagement by efficiently allocating the available weapons against the threats to maximize its survivability. This dynamic and multi-criteria decision-making problem is modeled using a multi-criteria decision tree and solved with two approaches: the multi-criteria decomposition approach and the multi-criteria myopic approach. We propose a novel metric for comparing two strategies within a multi-criteria decision tree and have developed a testbed in order to simulate the engagements. The results show that, when sufficient decomposition conditions are verified, the decomposition approach produces superior decision-making strategies compared to the myopic approach. Conversely, when the multi-criteria decision aid (MCDA) method does not satisfy the decomposition conditions (e.g., TOPSIS), there is no guarantee that decomposition will provide the best compromise strategies. From a military perspective, this work will help develop tactics, procedures and training packages for such a highly complex and dynamic decision-making problem. The plans generated by the approach presented here can also serve as a reference for assessment of the quality of the engagement plans yielded by real-time planning algorithms.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".