Obtaining optimal and approximate solutions to the problem of scheduling inbound and outbound trucks in cross docking operations
Bibliographic record
Abstract
The thesis focuses on optimization of inbound and outbound truck scheduling with the\ngoal of minimizing total operation time of cross docking. A model of cross docking is\ndeveloped; two different methods are applied on the model in order to find an optimal\ndocking sequence for receiving and shipping trucks and their assignment to receiving and\nshipping docks, and product routing from receiving to shipping trucks.\nThe two methods used were mathematical modeling and heuristic algorithm. For the first\nmethod, a mixed integer programming model was developed to minimize total operation\ntime; AMPL modeling language is used for the mathematical modeling for small sized\nproblems. For the second method, a heuristic algorithm was developed to find near\noptimal solutions fast and was used for problems of larger size. In order to examine the\nperformance of heuristic algorithm, small problems were solved by both mathematical\nmodel and the heuristic algorithm.\nThe results from the mathematical model and the heuristic algorithm are very close with\nslight differences in receiving and shipping truck docking sequence, and in product\nrouting between these two methods. In addition, the heuristic algorithm also calculates\nnumber of products transferring from receiving trucks to the temporary storage as well as\nthe number of products transferring from the temporary storage to shipping truck in\ncontrary to the mathematical model. Total number of units of products passing through\nthe temporary storage calculated by heuristic algorithm is presented and it can be seen\nthat the heuristic algorithm transfers to the temporary storage as few products as possible.\nFurthermore, in cases that receiving and shipping trucks are divided into groups or\nclusters in the cross docking operation, heuristic algorithm can be used to calculate\noptimal number of receiving and shipping docks based on preferences of total operation\ntime or total number of products passing through the temporary storage.\nAnother issue which is focused on is the problem of dock door assignment. Close\nshipping docks to each receiving dock are determined and the percentage of products\ntransferred from a receiving dock to its close shipping docks is calculated as a method to\nmeasure the performance of the dock assignment solution.
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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 | 0.006 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".