Integrated production scheduling and vehicle routing problem with due dates, inventory holding and penalty costs
Bibliographic record
Abstract
The integration of production scheduling (PS) and vehicle routing problems (VRP) is crucial for achieving operational efficiency in today’s competitive market, yet these problems have often been studied in isolation. A lack of integration can result in increased costs, such as longer lead times and higher holding costs, ultimately affecting customer satisfaction. To address these challenges, this study develops an integrated PS and VRP model aimed at minimizing logistical costs, including inventory holding and fixed vehicle usage costs, as well as penalties for earliness and lateness in deliveries. The proposed model considers multiple customers with varying demands, due dates, and batch sizes, requiring efficient scheduling, production, and timely delivery. The objective is to balance customer satisfaction by minimizing both early and late delivery penalties while optimizing production scheduling to reduce inventory holding costs. An Improved Genetic Algorithm (IGA) is employed to solve the integrated problem, with parameter tuning performed to enhance the balance between exploration and exploitation. The results demonstrate that the IGA achieves superior convergence and solution quality compared to conventional approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".