A Logic-Based Benders Decomposition for the Car Resequencing Problem with a Painted Body Storage
Bibliographic record
Abstract
The cost of producing diverse cars depends on the sequence in which they are arranged in the body shop, paint shop, and assembly shop. Before entering the downstream assembly shop, the upstream car sequence shared by the body shop and paint shop is readjusted via the painted body storage, which consists of several first-in-first-out lanes. The car resequencing problem addressed in this paper requires determining the upstream and downstream sequences and the car-to-lane assignment to minimize the total cost of the three shops. We propose a nested logic–based Benders decomposition approach with three levels, where each car is assigned a body and a color in the first level to determine the upstream sequence. In the second level, cars are rearranged by determining their configurations and downstream positions. A feasible assignment of cars to lanes is sought in the third level to respect this sequence change. We provide a mathematical formulation for each level and propose two shortest-path problem reformulations for the first level, where solving the first reformulation is equivalent to a k-shortest-path problem. The second reformulation is a shortest-path model restricted by demand constraints. A lower bound, valid inequalities, and a heuristic method are also proposed as enhancements. Computational results show that our approach can handle instances of up to 120 cars, about 10 times more than previous studies. A sensitivity analysis is conducted to provide some managerial insights. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: Financial support for this work was provided by the Canadian Natural Sciences and Engineering Research Council (NSERC) [Grant 2021-04037] and the National Natural Science Foundation of China [Grant 72372087]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0904 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0904 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".