Inexact Minimax Regret Integer Programming for Long-Term Planning of Municipal Solid Waste Management—Part A: Methodology Development
Bibliographic record
Abstract
In real-world municipal solid waste (MSW) management systems, identification of proper policies under uncertainty for accomplishing desired waste-disposal targets is critical. An inexact minimax regret integer programming (IMMRIP) method for the long-term planning of MSW management is developed. It incorporates the technique of minimax regret analysis (MMR) into an interval-parameter mixed-integer linear programming (IMILP) framework. The IMMRIP method can handle dual uncertainties presented as both random variables and interval values; it only needs a list of scenarios without any assumption on their probability distributions. It can facilitate dynamic analysis for decisions of system-capacity expansion and/or development within a multi-facility and multi-period context. Moreover, it can also be used for analyzing multiple scenarios associated with different system costs and risk levels. An interval-element cost matrix can be transformed into an interval-element regret matrix based on an interactive algorithm. Solutions based on an inexact minimax regret criterion can identify desired alternatives for MSW management and planning under a variety of uncertainties. In a companion paper, the developed method will be applied to a real case study in the City of Regina, Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".