Integrated Production, Quality and Inspection Optimization Models for Production Systems with Delayed Failure
Bibliographic record
Abstract
Businesses are expected to meet customers’ demands, including high-quality products and timely deliveries. However, in practice, production lines are subject to unforeseen events (i.e., failures), which affect production efficiency. Despite several attempts in the literature, there is still a need for models that can minimize those issues. This dissertation contains different optimization models that can help managers deal with various unexpected events and keep manufacturing firms competent and efficient. In the first contribution (Chapter 2), the case of failures in a single machine production machine is considered. The machine’s failures are described using a two-stage delay time model. The model integrates the machine’s condition, inspection policy, and maintenance actions in a recursive equation to obtain the total expected makespan and production cost. The developed model helps managers reduce the number of failures and resource waste due to failure downtime. In the second contribution (Chapter 3), we consider the possibility of performing minimal repair or replacement depending on the machine's age and investigate how the scheduling decisions are impacted accordingly. Several managerial insights are provided regarding the amount of investment in a maintenance operation depending on the machine's failure characteristics and inspection policy. In Chapter 4, a parallel-machine scheduling problem is studied, in which decisions about inspection and production schedules are made based on the machines' stochastic failure process. The identical parallel machines are exposed to a two-stage delay time model (DTM). Due to the large-size configuration of the solution space for this problem, a branch and bound algorithm is used for optimization. Finally, in Chapter 5, we use the direct effect of a machine's health condition as one of the significant factors that cause non-conforming products. The DTM is used as an opportunity to perform periodic quality inspections of the products and to detect a higher than acceptable level of non-conformity, which is an indicator of a defective machine. We formulate the effects of the machine's condition, inspection, and maintenance activities on the proportion of non-conforming products. Apart from product quality control, inspection is also used to assess the machine's condition and perform a maintenance action, if required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".