Sugar beet transportation problem under growers’ equity regulations: Metaheuristic approach
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Bibliographic record
Abstract
We consider the optimization problem related to the sugar beet transportation when supplying sugar mills in the sugar production. The sugar beet transportation comprises of loading the beet collected at storage piles and then delivering it to sugar mills. An essential prerequisite to guarantee a viability of the considered sugar mill, is to transport the required quantities of sugar beet while maximizing technological quality and minimizing transportation costs. Some growers may be privileged to conduct collection activities in days of a planning period when sugar beet is fresh and contains larger amount of sucrose, while others do not. This unfair collect scheduling plan should be avoided to provide equal treatment of growers. We propose an Integer Linear Programming (ILP) model with the aim of simultaneously maximizing the amount of collected sucrose during the planning period while minimizing the number of vehicles of a homogenous vehicle fleet, including constraints that provide equal opportunities for growers in sugar beet collection. The problem is denoted by the Sugar Beet Transportation Problem under Growers’ Equity Regulations (SBT-GER). By applying the weighted sum method, the two objective functions are combined to transform the bi-objective problem into a single-objective one. Equity regulations are expressed through the requirement that the minimum percentage of the quantity of sugar beet is guaranteed to be collected from each grower on the day of harvest. For real-sized instances, we propose two metaheuristic algorithms, based on Variable Neighborhood Search (VNS) and Greedy Randomized Adaptive Search Procedure (GRASP), respectively. The developed mathematical model and the proposed metaheuristic approaches are evaluated on a set of randomly generated test instances. The obtained results show that VNS outperforms exact solver and GRASP for the majority of examples.
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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 | 0.000 | 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 | 0.000 |
| 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 it