An Adaptive Sequential Decision-Making Approach for Perishable Food Procurement, Storage and Distribution Using Hyperconnected Logistics
Bibliographic record
Abstract
The fast-food industry currently relies on frozen ingredients to reduce the cost of procurement of raw materials. In recent years, consumers have started to curb their habit of eating out from fast-food chain restaurants due to the growing concerns for unhealthy menu choices made primarily from highly processed and/or frozen food ingredients. To address these issues, some organizations in the fast-food industry have started to offer menus with fresh unfrozen ingredients sourced locally or regionally. This paper addresses the problem of integrating sourcing, storage, and distribution strategies for a fast-food restaurant chain at the regional level. We present an adaptive sequential optimization decision-making approach for procurement, storage, and distribution of perishable food products to multi-unit restaurants at the regional level. This solution approach uses shelf-life considerations in developing a procurement and distribution strategy for fresh produce in the era of hyperconnected logistics. Three models are developed using Mixed Integer Linear Programming (MILP). First, a procurement model is developed to find the cost-effective supplier for each produce category based on shelf life. Then, a distribution model is developed to find the cost-optimal solution for distributing produce to multiple restaurant locations considering weight, volume, and operation hours. Finally, an integrated model is developed to optimally combine procurement and distribution options generated by the first two models to minimize costs while respecting total shelf-life constraints. Numerical experiments based on realistic data are carried out to show that the proposed sequential approach yields valid decisions and presents the effects of price, shelf-life, and demand changes on the supply chain.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".