The stochastic production routing problem with adaptive routing and service level constraints
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".