A simulation optimization framework to solve Stochastic Flexible Job-Shop Scheduling Problems—Case: Semiconductor manufacturing
Bibliographic record
Abstract
This paper addresses a Stochastic Flexible Job-Shop Scheduling Problem (SFJSSP) in the context of semiconductor manufacturing. Semiconductor industry is among the most capital-intensive businesses whose operational excellence is of vital importance. Within the front-end fab of the semiconductor industry, the photolithography workstation is the well-known bottleneck process. To elevate the performance of the whole semiconductor manufacturing system, developing a competent schedule for its bottleneck is essential. However, the re-entrant product flows, high uncertainties in operations times, and rapidly changing products and technologies within the photolithography, make it difficult to develop a schedule for the whole semiconductor fab. Considering Industry 4.0, hybrid methods such as Simulation Optimization (SO) have proven their applicability in addressing complex production scheduling problems. Thus, this paper develops a mathematical model for SFJSSP of the semiconductor manufacturing considering special constraints of the photolithography workstation (machine process capability, machine dedication, and maximum reticles (masks) sharing constraints). Next, we transform the developed model into an SO model integrated with a computer simulation model capable of modeling the photolithography workstation. The simulation model develops an initial schedule based on the Least Work Remaining (LWR) dispatching rule. Moreover, the simulation model calculates the objective function of the SFJSSP. A tailored Genetic Algorithm (GA) is then developed, which attempts to optimize the initially proposed schedule. To validate the superiority of the presented SO methodology in addressing SJSSPs, it is compared with previously proposed methods. Furthermore, to assess the impact of the three special constraints of the photolithography work area on system performance, two sets of experiments are proposed. In the first set of experiments, the performance of two SFJSS environments, one with the special constraints and one without, is compared. The second set of experiments involves observing the system’s performance while systematically varying the severity of the special constraints. The results indicate that improved performance levels can be accomplished by enhancing flexibility within both the operations of individual jobs and the machines within the manufacturing system.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".