Personnel scheduling problem for ready-mixed concrete delivery
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the personnel scheduling problem for ready-mixed concrete (RMC) delivery. The goal is to create schedules for drivers over a large planning horizon of a week that minimize multiple objectives under tight operational and regulatory constraints. At the operational level, multiple production plants are available to satisfy the requests of several construction sites. A fixed fleet of homogeneous trucks is available at each period of the planning horizon to transport RMC from production plants to construction sites. We formally describe and solve the problem using a metaheuristic algorithm based on a two-stage approach. Computational experiments are conducted on a new set of artificial instances and on a set of instances generated based on real data. Through a sensitivity analysis, we demonstrate that important savings in the cost of the schedules can be achieved with some degree of flexibility in several parameters. However, the well-being of the drivers must always be considered to guarantee the right balance between the cost-effectiveness of the schedules and a good work environment. Real data provided by an industrial partner is used to test the solution approach and compare the quality of our solutions. The results show that our solution approach largely outperforms the approach used by the industrial partner. • We explore personnel scheduling problems for ready-mixed concrete (RMC) delivery. • We formally describe and solve the problem using a metaheuristic algorithm. • We demonstrate that savings in schedule costs can be achieved with flexibility. • We balance driver well-being with cost-effective, efficient schedules. • Our solution approach largely outperforms the approach used by an industrial partner.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it