Integrated production-inventory-routing problem for multi-perishable products under uncertainty by meta-heuristic algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study aims to introduce an integrated production-inventory-routing problem (PIRP) with a mixed-integer linear programming model, remarking a multi-perishable product, multi-period, and heterogeneous fleets with time windows in a distribution network. The objective of the proposed model is to maximise the total profit, which equals the selling revenue subtract the aggregation of the holding, production, transportation, and utility preference costs. At the production level, a multi-period production system with production capacity constraints is considered, in which the inventory at each stage of production is intended to compute the related holding costs and schedule more appropriate planning. The vehicle routing problem is tackled at the distribution level regarding vehicles with various capacities in a multi-period condition. Consequently, a fuzzy chance-constrained programming model is used to deal with fuzzy parameters. Furthermore, two evolutionary algorithms, namely a hybrid imperialist competitive algorithm (HICA) and self-adaptive differential evolution (SADE), are proposed to solve the given problem. Subsequently, several numerical examples with managerial insights are solved to evaluate the performances of the proposed algorithms and show their effectiveness and efficiency. Computational results demonstrate the superiority of the proposed algorithms for this problem. Finally, the applicability of the proposed algorithms is investigated by a real-case study.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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