A mathematical model of operation allocation and materials handling system selection problems in a flexible manufacturing system.
Bibliographic record
Abstract
Materials handling systems are an integrating component of a manufacturing system and as such must be considered within an integrated approach to manufacturing systems design. This work proposes to integrate the operation allocation and the materials handling system selection problems in a flexible manufacturing system by extending the operation allocation model to include some aspects of materials handling system design. The objective of the operation allocation model is to select a group of machines where the operations of the part types will be performed and then to assign those operations to the selected machines. The operation allocation model interfaces with the materials handling system selection model by providing input data in the form of the manufacturing operations to be performed at each machining center. The selection of the materials handling system is centered on the matching of the parts visiting a machining center to perform a manufacturing operation and the abilities of the handling equipment to perform the required materials handling functions of those part types. The objective is to select an optimal group of materials handling equipment to be assigned to a cell. A computer program was developed to greatly automate the process of solving the models. This allows the program to be used as a rapid modeling tool. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .P39. Source: Masters Abstracts International, Volume: 39-02, page: 0576. Advisers: R. S. Lashkari; S. P. Dutta. Thesis (M.A.Sc.)--University of Windsor (Canada), 2000.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".