Optimal periodic review order-up-to policy in a single-vendor multi-retailer cold chain with continuous multi-stage quality degradation
Bibliographic record
Abstract
This study presents a cost optimization model for a two-echelon cold chain involving a single vendor and multiple retailers with stochastic product demand. The vendor and retailers follow periodic review order-up-to policies, where the vendor’s review period is an integer multiple of each retailer’s review interval. Product quality degradation, influenced by storage time and temperature, is modeled using the non-Arrhenius equation and the global stability index method. Degradation begins at the vendor’s side and continues at the retailers’, necessitating a continuous modeling approach. An algorithm is developed to minimize the expected total cost and to determine the optimal decisions for the cold chain. The study explores various scenarios and their optimal solutions, highlighting the impact of storage time and temperature on quality degradation and cost efficiency. The findings demonstrate that periodic review order-up-to policies enhance cost efficiency and inventory management in cold chains, while controlling storage time and temperature is essential to mitigate quality degradation and maintain product integrity. A continuous degradation model provides a more accurate understanding of quality loss across multiple stages, enabling better decision-making. Scenario analysis helps identify cost-effective strategies tailored to specific supply chain conditions, and the proposed algorithm serves as a practical tool for managers to balance cost and quality in cold chain operations. This research advances cold chain management by integrating cost optimization with quality degradation modeling, offering actionable insights for improved replenishment strategies and operational decision-making.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".