An Integrated Multi-Period Layout Planning and Scheduling Model for Sustainable Reconfigurable Manufacturing Systems
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
In a dynamic production environment, not only the customer’s needs change with time, but the economic aspects of that environment, such as energy pricing, also change. Reconfigurable Manufacturing Systems (RMSs) are designed to respond to such changes by reconfiguring system components efficiently. This paper presents a novel mathematical model to maximize energy sustainability of RMS. The novelty aspect of the model is the consideration of energy sustainability concurrently with system configuration and scheduling decisions in each period of the planning horizon. The objective of this mixed integer linear model is to minimize the total cost of energy consumption, system reconfiguration, and part transportation between machines, depending on fluctuations of energy pricing and demand during different periods. Several case studies are solved by GAMS Software to illustrate the performance of the presented model and analyze its sensitivity to the volatility of energy pricing and demand to show their effect on system changeability. An efficient genetic algorithm (GA) has been developed to solve the model in larger scale due to its NP-hardness and compared to GAMS for validation. Results show that the presented GA finds near-optimal solutions in 70% shorter time than GAMS on average.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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