Buffer allocation, equipment selection and line balancing optimisation in unreliable production lines
Bibliographic record
Abstract
This paper presents an integrated optimisation model to simultaneously solve buffer allocation, equipment selection and line balancing problems in unreliable production line systems. The considered unreliable serial production line consists of m workstations and m − 1 intermediate buffers. The objective is to maximise the system throughput level. A decomposition method is used to estimate the production line throughput. The decision variables in the formulated optimal design problem are buffer levels, types of equipment and the sets of tasks assigned to the workstations. An efficient algorithm, based on the nonlinear threshold accepting algorithm (NLTA) is proposed to solve this problem. The efficiency of the proposed approach is compared to existing algorithms and first tested on a simple assembly line balancing type-2 problem (SALB-2). Here the objective is to minimise the cycle time with a fixed number of workstations. In the second numerical experiment, the integrated model is solved using the NLTA, and its performance is compared to that of the great deluge algorithm (GDA) through several numerical examples. [Received: 9 June 2018; Revised: 15 September 2018; Revised: 18 April 2019; Accepted: 2 August 2019]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".