Supply chain network design with flexibility, resiliency, and sustainability
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".