Adaptive Production Scheduling and Control in One-Of-A-Kind Production
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Bibliographic record
Abstract
Consequently, production of a product is rarely repeated in OKP (Wortmann et al., 1997).Moreover, OKP companies usually adopt a market strategy of make-to-order or engineering-to-order. Therefore, it is very important to meet the promised due dates in OKP.This market strategy challenges production scheduling and control differently from that of make-to-stock.Typically, there are five types of problems challenging production scheduling and control in an OKP company.(1) Job insertion or cancellation frequently happens in OKP due to www.intechopen.comProduction Scheduling 114 high customer involvement.(2) Operator absence or machine breakdown needs to be carefully controlled to fulfill the critical due dates.(3) Variation in processing times usually happens to an operation, because a highly customized product is rarely repeated.(4) The overflow of work-in-process (WIP) inventories occurs.(5) Production delay on the previous day will affect the production on the current day; so will production earliness.When these problems dynamically happen to an OKP company, the daily production has to be adjusted online, i.e. adaptive production control.Therefore, OKP companies are continuously seeking new methods for adaptive production scheduling and control on shop floors. Former research of flow shop production scheduling and controlFlow shop production scheduling has been researched for more than five decades since 1954 (Gupta & Stafford, 2006).Early research of flow shop production scheduling was highly theoretical, using optimization techniques to seek optimal solutions for n-job m-machine flow shop scheduling problems.However, the emergence of NP-completeness theory in 1976 (Garey et al., 1976) profoundly influenced the direction of research in flow shop production scheduling.NP-completeness implies that it is highly unlikely to get an optimal solution in a polynomially bounded duration of time, for a given complex problem in general.That is why heuristics are required to solve large problems.Adaptive production control acutely challenges the research of flow shop production scheduling, because the relationship has not been completely revealed, among the number of jobs, the number of machines, job processing times and scheduling objectives.Moreover, the research of flow shop production scheduling is often based on strong assumptions, such as no machine breakdown or operator absence, processing times and some constraints are deterministic and known in advance (MacCarthy & Liu, 1993).During real production, disturbances are manifested in such occurrences as machine breakdown, operator absence, longer than expected processing times, new emergent orders, and so on (McKay et al., 2002), all of which may fail the original offline schedule and then require online re-scheduling for adaptive production control.Consequently, heuristics based on strong assumptions are not robust, making production scheduling systems inflexible (Kouvelis et al., 2005), and a large gap exists between theoretical research and industrial applications (Gupta & Stafford, 2006;MacCarthy & Liu, 1993). Status of production scheduling and control in OKPCurrently, OKP companies primarily use priority dispatching rules (PDRs) to deal with disturbances.It is fast and simple to use PDRs to control production online, but PDRs depend heavily on the configuration of shop floors, characteristics of jobs, and scheduling objectives (Goyal et al., 1995), and no single specific PDR clearly dominates the others (Park et al., 1997).Moreover, the performance of PDRs is poor on some scheduling objectives (Ruiz & Maroto, 2005), and inconsistent when a processing constraint changes (King & Spachis, 1980).Consequently, there is a considerable difference between the scheduled and actual production progress (Ovacik & Uzsoy, 1997), and production may run into an "ad hoc fire fighting" manner (Tu, 1996a(Tu, , 1996b)).www.intechopen.com
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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