A Memetic Heuristic for the Generalized Quadratic Assignment Problem
Bibliographic record
Abstract
In the generalized quadratic assignment problem (GQAP) we are given n weighted facilities, m capacitated sites, a traffic intensity matrix between facilities, a distance matrix between sites, unit traffic costs, and assignment costs of facilities to sites. The aim is to determine an assignment of facilities to sites so that the sum of assignment and traffic costs is minimized and the total weight of all facilities assigned to the same site does not exceed the site capacity. The GQAP is a generalization of the quadratic assignment problem (QAP) in which n = m and exactly one facility must be assigned to each site. The problem has applications in container yard management and in the assignment of equipment to manufacturing sites. This article describes a memetic heuristic for the GQAP, as well as an integer linear programming formulation that can be solved by CPLEX for small instances. For larger instances, feasible solutions can be obtained by a truncated branch-and-bound procedure. Computational experiments show that on small instances the proposed heuristic always yields an optimal solution; on larger instances it always outperforms the truncated branch-and-bound algorithm.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".