A Mathematical Model for the Sustainable Design of a Cellular Manufacturing System in the Tactical Planning of a Closed-Loop Supply Chain Featuring Alternative Routings and Outsourcing Option
Bibliographic record
Abstract
In this paper, a new mathematical model is presented for a cellular manufacturing system into tactical planning of a closed-loop supply chain to build a sustainable manufacturing enterprise. On the manufacturing side of the model, a comprehensive cellular manufacturing system is designed considering dynamic cell configuration, alternative process routings, lot splitting, sequence of operations, multiple units of identical machines, machine capacity, machine adjacency requirements, and cell size limits. On the closed-loop supply chain side of the model, different activities are considered including acquiring returned products, setting up the system for the implementation of disassembly operations, inventory holding of the returned products, remanufacturing the parts having high quality, and disposing of the returned products that cannot be economically recovered. The mathematical model in this paper, to the best of our knowledge, is the first model reducing the total costs of the cellular manufacturing system while considering the alternative process routings and subcontracting of the part demands. A detailed economic analysis is done on the large-sized example problem of the mathematical model to investigate the impacts of adopting different production policies such as internal production, inventory holding, and subcontracting as well as different manufacturing attributes such as dynamic reconfiguration and alternative process routings. The mathematical model is also solved for different instances to investigate the effects of incorporating subcontracting, alternative process routings, and dynamic reconfigurations in the model. Sensitivity analyses are also conducted to investigate the effects of the recovery rate of returned products on the objective function value and the number of returned products to be acquired. The effect of taking alternative process routings into consideration on the objective function value is also investigated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".