Methodology for Solving the Assembly System Reconfiguration Planning Problem
Bibliographic record
Abstract
The need to cost effectively introduce new generations of product families within ever decreasing time frames have led manufacturers to seek product development strategies with a multigenerational outlook. Co-evolution of product families and assembly systems is a methodology that leads to the simultaneous design of several generations of product families and reconfigurable assembly systems that optimize life cycle costs. Two strategies that are necessary for the implementation of the co-evolution of product families and assembly systems methodology are: (1) The concurrent design of product families and assembly systems and (2) Assembly system reconfiguration planning (ASRP). ASRP is used for the determination of the assembly system reconfiguration plans that minimize the cost of producing several generations of product families. More specifically, the objective of ASRP is to minimize the net present cost of producing successive generations of products. This paper introduces a method for finding optimum solutions to the ASRP problem. The solution methodology involves the generation of a staged network of assembly system plans for all the generations that the product family is expected to be produced. Each stage in the network represents a generation that the product family is produced, while each state within a stage represents a potential assembly system configuration. A novel algorithm for generating the states (i.e. assembly system configurations) within each generation is also introduced. A dynamic program is used to find the cost minimizing path through the network. An example is used to demonstrate the implementation of the ASRP methodology.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".