OPtimally Balancing Large Assembly Lines: Updating Johnson S 1988 Fable Algorithm<sup>*</sup>
Why this work is in the frame
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Bibliographic record
Abstract
In 1988 Roger Johnson published a paper entitled “Optimally Balancing Large Assembly Lines with Fable” describing a depth-tirst. branch-and-bound algorithm tor solving the type-1 line balancing problem. The Fable algorithm sought to achieve three goals. 1) The algorithm could be a heuristic that would quickly find good solutions to instances containing lOOOor more tasks. 2) After finding a good solution the algorithm could continue until it found a verifiable optimal solution. 3) The algorithm would require a small and predictable amount of computer memory. Fable did remarkably well at achieving goals 1 and 3 and reasonably well at achieving goal 2. Though unstated. Fable achieved another goal. It was easy to understand and easy to program. Over the years researchers have proposed alternatives and improvements aimed at doing better at goal 2. The objective of this paper is to apply the best of these improvements to the original Fable algorithm to see what progress has been made in 15 years and where work i.s still needed. Tbe resulting algorithm is called Fable 2003 and it seems to peribrm as well as the current best algorithms in the literature.The range of instances solved by Fable 2003 is significantly larger than what Johnson's 11988] Fable was able to solve. This is due primarily to three groups of improvements: I) Changing direction, task priorities, and running more than I trial per instance; 2) Tbe Nourie-Venta list and other fathoming methods; and 3) The C programming language. The first improvement ensures that the most promising parts of the solution space are searched. The second improvement fathoms targe parts of the branchand- bound tree. Johnson's Fable was written in Fortran. Fable 2003 is written in C and so can use pointers to make more effective use of computer memory.One group of instances is difficult for Fable 2003 and the best algorithms in the literature. These are instances having a small average number of tasks per station, say tbree or fewer. Solving these instances is the research area where work is needed most. This is the same area that Johnson identified 15 years ago.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| 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