An interval-parameter two-stage stochastic integer programming model for environmental systems planning under uncertainty
Bibliographic record
Abstract
An interval-parameter two-stage stochastic mixed integer programming (ITMILP) technique is developed for waste management under uncertainty. It is a hybrid of inexact two-stage stochastic programming and mixed integer linear programming methods. The ITMILP method can directly handle uncertainties expressed not only as probability density functions but also as discrete intervals. It can be used to analyse various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated. More importantly, it can facilitate dynamic analysis of decisions on capacity expansion planning within a multi-region, multi-facility, multi-period, and multi-option context. The results will help to generate a range of decision alternatives under various system conditions, and thus offer insight into the trade-offs between environmental and economic objectives. The ITMILP method is applied to planning facility expansion and waste flow allocation within a waste management system. The results indicate that reasonable solutions have been generated for both binary and continuous variables. The binary-variable solutions represent the decisions of facility expansion, while the continuous-variable solutions are related to decisions on waste flow allocation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".