Research on integrated optimization of order allocation and lotsizing sequencing for mixed-model parallel assembly lines using improved intelligent optimization algorithm
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".