Solving the Storage Location Assignment Problem in the Manufacturing Industry - A Study in Mathematical Modeling & Applied Optimization
Bibliographic record
Abstract
Companies in the manufacturing industry are constantly seeking to be more effective in cutting costs and increase revenue to improve profits. Order picking is often the most expensive operation in the inventory, as it stands for 55% of the inventory operating expenses. Alfa Laval, a heavy industry company, is rolling out a new corporate strategy involving its operations, which this thesis is a part of. The authors of this thesis developed a tool to support Alfa Laval’s manually operated inventory. The tool was built with a mathematical modeling approach in the programming language Python. It was later tested in real-life settings where it successfully reduced the travel time of picking items in the inventory. The objective was to optimize the storage location of items in the inventory, which was done by introducing heuristic- and metaheuristic algorithms and applying them to a linear- and a quadratic model, known as GAP and QAP. The thesis was written by authors from different majors, creating an interdisciplinary team where both applied their academic backgrounds to achieve the purpose of the thesis. This master thesis is a study in mathematical modeling. (Less)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 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".