The stochastic production routing problem with adaptive routing and service level constraints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Demand uncertainty poses a challenge to most companies in manufacturing and services as it can lead to significant profit losses if not addressed properly. To deal with this risk, companies may adopt specific service level targets to satisfy at least a certain proportion of their demand while considering operational constraints and minimizing the total cost. In this study we address the stochastic production routing problem (PRP) with adaptive routing and service level constraints. The PRP unifies the production, inventory and routing decisions into an integrated problem aimed at improving coordination across different parts of the system. We consider four different types of service levels, where each type uses a specific metric based on assumptions aligning with the needs of the company. These metrics encompass aspects such as the occurrence of stockouts or allowed ratios of backlogs or backorders to average demand. A two-stage stochastic formulation is proposed for each type of service level. Setup decisions are made in the first stage, and production, inventory, and routing decisions are adapted after demand realization. Considering routing decisions in the second stage increases flexibility while lowering overall costs. However, the resulting optimization problem is more challenging to solve than the case where routing decisions are made in the first stage. To address this issue, we introduce an iterative matheuristic algorithm designed to yield high-quality solutions within a reasonable computation time. The effectiveness of the proposed heuristic algorithm is demonstrated through extensive experiments, highlighting its potential to assist companies in managing demand uncertainty and enhancing operational efficiency.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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