Supply chain network design with flexibility, resiliency, and sustainability
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
Supply chain network designs (SCNDs) have gained significant popularity in recent years as a means to reduce overall supply chain (SC) costs and establish a competitive edge. A flexible supply chain network (FSCN) holds promise for effectively managing SC complexity and optimizing total costs by eliminating unnecessary nodes and central hubs. This study develops a multi-objective mathematical model that integrates flexibility, resiliency, and sustainability dimensions within supply chain network design (SCND). The proposed model simultaneously optimizes three conflicting objectives, i.e. total cost, supply chain resilience, and environmental emissions, while addressing demand uncertainty through a scenario-based approach. To generate high-quality Pareto solutions, two multi-objective meta-heuristic algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Simulated Annealing (SA), are employed. The Taguchi analysis is subsequently employed to fine-tune the meta-heuristic parameters. Numerical experiments demonstrate that the solutions generated by MOPSO outperform SA, yielding a remarkable 46% increase in total cost benefits. Sensitivity analysis reveals that the most critical parameters are the number of days in inventory and production cost. The findings underscore the scientific contribution of this study by providing a comprehensive and adaptive framework for designing flexible and resilient SCs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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