Research on integrated optimization of order allocation and lotsizing sequencing for mixed-model parallel assembly lines using improved intelligent optimization algorithm
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
The growing demand for customization in manufacturing industries such as automotive and home appliances has brought significant production challenges, making Mixed-Model Assembly Lines (MMALs) widely adopted in mass customization due to their flexibility advantages. The integrated optimization of order allocation and lot-sizing sequencing for MMALs under the Assembly-To-Order (ATO) mode is crucial, which needs to balance the minimization of assembly completion time, production line load balancing, and material consumption equalization. This paper addresses this integrated optimization problem by constructing a multi-objective mathematical model for joint decision-making. Furthermore, an improved multi-objective evolutionary algorithm (INSGA-II) is proposed. Specific encoding-decoding methods and neighborhood operators are designed to achieve effective search. Variable Neighborhood Descent (VND) is embedded to enhance local search capability. An elite archive with information feedback combined with the population diversity detection strategy is adopted to improve algorithm diversity. The purpose of this study is to enhance the efficiency of the production system and ensure the flexible production of multi-variety products and on-time delivery of orders through the proposed optimization scheme. By constructing multiple instances and conducting comparative experiments with other competitive algorithms, the results demonstrate that the performance of the improved algorithm is superior to that of other algorithms.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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