Solving a Multi-Objective No-Wait Flow Shop Problem by a Hybrid Multi-Objective Immune Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A frequently occurring operational problem is one of processing a given number of jobs (commodities) on a specified number of machines (facilities) -referred to by various investigators as scheduling, dispatching, sequencing, or combinations thereof In most manufacturing environments, a set of processes is needed to be serially performed in several stages before a job is completed. Such system is referred to as the flow shop environment that is one class of scheduling problems. In a flow shop problem, we consider n different jobs that need to be processed on m machines in the same order. Each job has one operation on each machine and the operation of job i on machine j has processing time p ij The early groups of flow shop researchers were quite small and these people were concentrated in a few US academic and research institutions. However, today's flow shop research community is global and from every continent and every geographical region Recently, flow shop scheduling problems have been one of the most renowned problems in the area of scheduling and there are numerous papers that have investigated this issue For instance, They introduced the current flow shop problems and the approaches were used to solve them optimally or approximately. They proposed a branch and bound algorithm for solving this problem. Grabowski and Wodecki (2004) proposed a tabu search based algorithm for the permutation flow shop problem with makespan criterion. Whang et al. (2006) dealt with a two machine flow shop scheduling problem with deteriorating jobs by minimizing the total completion time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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