Simultaneous Modular Product Scheduling and Manufacturing Cell Reconfiguration Using a Genetic Algorithm
Bibliographic record
Abstract
Modular product design can facilitate the diversification of product variety at a low cost. Reconfigurable manufacturing, if planned properly, is able to deliver high productivity and quick responsiveness to market changes. Together, the two could provide an unprecedented competitive edge to a manufacturing company. The production of a family of modular products in a reconfigurable manufacturing system often requires reorganizing the manufacturing system in such a way that each configuration corresponds to one product variant in the same family. The successful implementation of this strategy lies in proper scheduling of the modular product operations and optimal selection of a configuration for producing each product variant. These two issues are closely related and have a strong impact on each other. Nevertheless, they have often been treated separately, rendering inefficient, infeasible, and conflicting decisions. As such, an integrated model is developed to address the two problems simultaneously. The objective is to minimize the sum of the manufacturing cost components that are affected by the two planning decisions. These include reconfiguration cost, machine idle cost, material handling cost, and work-in-process cost incurred in producing a batch of product variants. Due to the combinatorial nature of the problem, a genetic algorithm (GA) is proposed to provide quick and near-optimal solutions. A case study is conducted using a steering column to illustrate the application of the integrated approach. Our computational experience shows that the proposed GA substantially outperforms a popular optimization software package, LINGO, in terms of both solution quality and computing efficiency.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".