Optimisation of assortment breadth and allocation of the selected product groups to the two-dimensional shelves
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper addresses the integrated optimisation of assortment breadth, shelf assignment, shelf space allocation, and positioning of the selected product groups on the multi-level shelves to maximise store profitability. To improve the tidiness and findability of the product groups along the shelves, following the merchandising rules, the allocated space to each product group should be rectangular and enclosed within the region of the larger group it belongs to. We formulate the problem as a mixed-integer linear programming model. A two-phase matheuristic algorithm is proposed to solve the problem. In the first phase, a simplified version of the problem is solved by a column generation heuristic. An optimisation-based algorithm provides the initial columns by which the efficiency of the column generation heuristic is improved. The second phase uses the output of the first phase and solves a set of independent single-shelf problems. The numerical studies show that the proposed algorithm yields high-quality solutions for problem instances with up to 40 multi-level shelves and more than 1000 product groups with a relative optimality criterion of less than 3.8% in a reasonable time. Further, we demonstrate the usefulness of the proposed methodology by using a case study.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it