Multi-period supply chain optimization with contango and backwardation effects using an improved hybrid genetic algorithm
Why this work is in the frame
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Bibliographic record
Abstract
In real-world markets, supply chain costs often fluctuate over time due to the contango and backwardation effects, making multi-period supply chain planning complex and critical. This paper presents a multi-period supply chain optimization model that explicitly incorporates these effects into cost forecasting and decision-making. A multi-period supply chain model is developed, considering the cost uncertainty introduced by contango and backwardation. An integrated polynomial regression fuzzy method is proposed to address this problem by predicting future fluctuations in purchasing, ordering, and logistics costs. A mixed-integer linear programming (MILP) model is formulated to minimize the total supply chain cost across multiple periods. Moreover, improving the hybrid genetic algorithm (IHGA) is proposed to solve this problem. The performance of the proposed IHGA is triggered by integrating trust region, quasi-Newton, and pattern search methods. Response Surface Methodology (RSM) determines the optimal parameter settings and hybridization structure. A real-world case study involving surgical instrument manufacturing companies validates the proposed approach. The results highlight optimal supplier selection and order allocations for each period, and performance comparisons reveal that the IHGA outperforms traditional algorithms in terms of cost efficiency, computational time, and convergence behavior.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it