Inexact Piecewise Quadratic Programming for Waste Flow Allocation under Uncertainty and Nonlinearity
Bibliographic record
Abstract
In practical waste management systems, most relationships among different system components are nonlinear in nature. Effects of economies-of-scale can often bring about such nonlinearity in objective functions within an inexact optimization framework. To handle both nonlinearity and uncertainty, an inex act piecewise quadratic programming (IPQP) model was developed through coupling piecewise linear regression with interval linear programming. In IPQP, uncertainties expressed as intervals for transportation/operation costs, treatment capacities, waste generation rates, waste flows/amounts were reflected; a more accurate approximation for nonlinearities reflecting effects of economies-o f-scale between unit transportation costs and waste flows as well as between unit operation costs and waste treatment amounts were provided. An interactive algorithm was designed for solving IPQP. IPQP was applied to a hypothesis case of waste allocation planning and compared with a conventional inexact quadratic programming model (IQP). The results indicated that, in the investigated waste allocation system, the optimized waste flows from the districts to the waste treatment facilities (WTFs) and the optimized waste treatment amounts in WTFs had no significant differences between both models. However, most of unit transportation costs or unit operation costs in IPQP were less than those in IQP, which finally contributed to a lower net system costs in IPQP than IQP. Th is implied that the often ignored effects of economies-of-scale should be considered accurately in the real-world waste management system to obtain lower costs. Strategies to balance the tradeoff between approximation accuracy and computational complexity for IPQP were also discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".