A Tabu-Search Heuristic for the Capacitated Lot-Sizing Problem with Set-up Carryover
Bibliographic record
Abstract
This paper presents a tabu-search heuristic for the capacitated lot-sizing problem (CLSP) with set-up carryover. This production-planning problems allows multiple items to be produced within a time period, and setups for items to be carried over from one period to the next. Two interrelated decisions, sequencing and lot sizing, are present in this problem. Our tabu-search heuristic consists of five basic move types—three for the sequencing decisions and two for the lot-sizing decisions. We allow infeasible solutions to be generated at a penalty during the course of the search. We use several search strategies, such as dynamic tabu list, adaptive memory, and self-adjusting penalties, to strengthen our heuristic. We also propose a lower-bounding procedure to estimate the quality of our heuristic solution. We have also modified our heuristic to produce good solutions for the CLSP without set-up carryover. The computational study, conducted on a set of 540 test problems, indicates that on average our heuristic solutions are within 12% of a bound on optimality. In addition, for the set of test problems our results indicate an 8% reduction in total cost through set-up carryover.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".