The Integrated Production and Transportation Scheduling Problem for a Product with a Short Lifespan
Why this work is in the frame
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Bibliographic record
Abstract
The integrated production and transportation scheduling problem (PTSP) with capacity constraints is common in many industries. An optimal solution to PTSP requires one to simultaneously solve the production scheduling and the transportation routing problems, which requires excessive computational time, even for relatively small problems. In this study, we consider a variation of PTSP that involves a short shelf life product; hence, there is no inventory of the productin process. Once a lotof the productis produced, itmustbe transported with nonnegligible transportation time directly to various customer sites within its limited lifespan. The objective is to determine the minimum time required to complete producing and delivering the product to meet the demand of a given set of customers over a wide geographic region. This problem is NP-hard in the strong sense. We analyze the properties of this problem, develop lower bounds on the optimal solution, and propose a two-phase heuristic based on the analysis. The first phase uses either a genetic or a memetic algorithm to select a locally optimal permutation of the given set of customers; the second phase partitions the customer sequence and then uses the Gilmore-Gomory algorithm to order the subsequences of customers to form the integrated schedule. Empirical observations on the performance of this heuristic are reported.
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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 | 0.004 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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