Multi-objective and multi-period optimization of a regional timber supply network with uncertainty
Bibliographic record
Abstract
To assess the impacts of uncertainty and environmental objectives on the configuration of timber supply networks, we develop a generic multi-period, mixed-integer fuzzy linear programming model with demand uncertainty and two objectives of minimizing total transportation cost and greenhouse gas (GHG) emissions. We then use the triangular fuzzy number method to define the uncertain demands and convert the model into its equivalent auxiliary crisp counterpart. To derive Pareto solutions more efficiently, we propose the nondominated sorting genetic algorithm (NSGA-II) to solve the model. Finally, we apply the model framework and solution method to a real-world case of regional timber supply in Fujian, China, to demonstrate their applicability. The simulation results of the model show that trade-offs exist between total cost and GHG emissions and that the proper selection of the number and locations of distribution centers can help reduce both the cost and GHG emissions. Demand uncertainty and supply fluctuations across different time periods can increase the cost and GHG emissions. Our empirical results provide useful insights into the design and management of regional timber supply networks, and our generic model is applicable to the analysis of regional supply networks of other products or materials besides timber.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".