0-1 Piecewise linearization approach for interval-parameter nonlinear programming: application to environmental management under uncertainty
Bibliographic record
Abstract
Interval-parameter nonlinear programming (INP) is an extension of conventional nonlinear optimization methods for handling both nonlinearities and uncertainties. However, challenges exist in its solution method, leading to difficulties in obtaining a global optimum. In this study, a 0-1 piecewise approximation approach is provided for solving the INP, through integration with an interactive algorithm for interval-parameter optimization problems. Thus, the INP model can be transformed into two deterministic submodels that correspond to the lower and upper bounds of the objective-function value. By solving the two submodels, interval solutions can be obtained, which are used for generating a range of decision options. The developed method is applied to a case of long-term municipal solid waste (MSW) management planning. Not only uncertainties expressed as interval values but also nonlinearities in the objective function can be tackled. Moreover, economies of scale (EOS) effects on waste-management cost can also be reflected. The results obtained can be used for generating decision alternatives and thus help waste managers to identify desired policies for MSW management and planning. Compared with the conventional interval-parameter linear and quadratic programs, the developed INP can better reflect system-cost variations and generate more robust solutions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".