Multi-retailer Cold Chain Management with Multi-stage Quality Degradation and Stochastic Demand
Bibliographic record
Abstract
Comprehensive models are developed to minimize the total costs of single-supplier (or manufacturer) multi-retailer cold chains in food industry systems. A cold chain is a supply chain with the storage temperature controlled. A well-operated cold chain can preserve the quality of food at a high level and reduce the cost resulted from the quality degradation. Quality degradation is influenced by storage temperature and time and can be measured by using many indices. The global stability index method and non-Arrhenius model are integrated to model the overall quality degradation of a product and to formulate the cost associated with quality degradation. The total cost of a cold chain is the summation of the supplier’s (or manufacturer’s) and retailers’ costs. The costs associated with the supplier or manufacturer include setup, holding, cooling, and transportation costs. The carbon emission cost in transit is also taken into account. The costs related with the retailers include setup, holding, and cooling costs as well as the loss of value resulted from quality degradation of a product. The customer demand of a product is modeled as both deterministic and stochastic. The following four scenarios are investigated: (1) the optimal replenishment times for the supplier and retailers are determined for a single-supplier multi-retailer cold chain with deterministic demand and quality degradation occurring only at the retailers’ side, (2) the optimal replenishment times for the supplier and retailers are found for a single-manufacturer multi-retailer cold chain having deterministic demand and quality degradation taking place in transit and at the retailers’ side, (3) the optimal production rate and replenishment time for the manufacturer as well as the optimal replenishment times and reorder levels for the retailers are determined for a single- manufacturer multi-retailer cold chain with stochastic demand and quality degradation occurring at the manufacturer’s and retailers’ sides, and (4) the optimal replenishment times and order up-to-levels for the vendor and retailers are found for a single-vendor multi-retailer cold chain having stochastic demand and quality degradation taking place at the manufacturer’s and retailers’ sides.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".