MCFP: A Monte Carlo Simulation-based Fuzzy Programming Approach for Optimization under Dual Uncertainties of Possibility and Continuous Probability
Bibliographic record
Abstract
The efficiency and confidence of decision making much rely on accurate information and objective judgement, however, which are usually compromised by uncertainties existing in the system. Although in many studies uncertainties are reflected during optimization processes, few models considered the dual uncertainties of possibility and continuous probability. This study proposed a Monte Carlo simulation-based fuzzy programming (MCFP) approach to handle such dual uncertainties. The developed approach was tested by a municipal solid waste management (MSW) problem to demonstrate its feasibility and efficiency. The results indicated that the proposed approach could obtain a reliable solution and adequately support the decision making process in MSW management. It is significantly advantageous in handling the coexistence of various fuzzy sets and complex probability distributions when compared to the conventional fuzzy stochastic programming approaches. Furthermore, three levels of the optimal results to help decision makers effectively manage the composting facility: the entire distributions for general policy makers in long term policy making and trade-off, risk and reliability analyses of the system; the range of most frequent occurrences for project/plant managers in a medium arrangement; and the expected values for the plant operators for short term operating and adjusting the facility to minimize the system cost. Such different levels of decision supports could make the MCFP approach highly feasible, flexible and adaptable in real-work applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".