Optimization of transport constraints and quality of service for joint resolution of uncertain scheduling and the job-shop problem with routing (JSSPR) as opposed to the job-shop problem with transport (JSSPT)
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Bibliographic record
Abstract
To better meet the qualitative and quantitative requirements of customers or relevant sector managers, workshop environments are implementing increasingly complex task management systems. The job shop scheduling problem (JSSP) involves assigning each task to a single machine while scheduling many tasks on different machines. Finding the best scheduling for machines is one of the challenging optimizations of difficult non-deterministic polynomial (NP) time problems. The fundamental goal of optimization is to shorten the makespan (total execution time of all tasks). This paper is interested in the joint resolution of scheduling and transport problems and more particularly the Job-shop problem with Routing (JSSPR) as opposed to the Job-shop problem with Transport (JSSPT). These two problems are modeled in the form of a disjunctive graph. For the JSSPT, the solution to the transport problem is not linked to any quality of service (QoS) criterion and the solution is therefore often semi-active. The Job-shop with Routing explicitly considers transport operations and uses algorithms from the transport community to solve the transport problem. It is shown that the routing part of the JSSPR is a problem of the vehicle routing family and of the Pickup and Delivery Problem family. QoS in the JSSPR is defined by the duration of tours, the duration of transport of parts and the waiting time for them. A new evaluation function – named Time-Lag Insertion Heuristic (TLH) – is proposed to evaluate a disjunctive graph by simultaneously minimizing the makespan and maximizing the quality of service. Thus, the solution obtained is not semi-active, but a compromise between the different criteria. This evaluation function is included in a metaheuristic. Our numerical evaluations demonstrate that, on the one hand, the TLH evaluation can find almost optimal solutions regarding the QoS criterion; and on the other hand, the TLH evaluation is not very sensitive to the order of insertion of the maximum time-lags during the different minimization steps.
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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 | 0.000 | 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 | 0.000 |
| 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