Optimisation of Supply Chain with Reactive Lateral Transhipment Under Imperfect Production System
Bibliographic record
Abstract
Because batik is one of the most popular products in Indonesia, improving the supply chain system for batik products, for example total supply chain costs efficiency, will have a substantial and lasting effect on all chains involved.In the batik supply chain, it is common practice for each chain to have an independent policy, resulting in total supply chain cost inefficiency.This paper discusses the development of a mathematical model to optimise batik supply chain with a single-vendor multi-buyers and multiproducts.The optimisation model was developed by taking into account the frequent lateral transhipment system in the batik supply chain system as well as the buyers' random demand fluctuations.In addition, the optimisation model takes into account imperfect production systems in the vendor that produce a random number of defective products on a periodic basis.To obtain the optimum solution from the dynamic model, an optimisation-in-the-loop simulation system based on genetic algorithms was then used to solve the developed mathematical model.The presented case study demonstrates that the proposed Genetic Algorithms (GA) able to reach a convergent point; thus, the proposed optimisation-in-the-loop model able to provide an optimum solution for the supply chain system under consideration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".