Integrated Supplier Selection and Inventory Management Problems Considering Discounts and Uncertainty: A Fuzzy-Probabilistic Approach
Bibliographic record
Abstract
This study addresses the integration of supplier selection and inventory management issues that involve discounts, with a unified decision-making support formulated through a mathematical programming approach.The challenges addressed encompass uncertain parameters such as defective goods rates, late delivery rates, and demand, some of which are treated as probabilistic/random variables under data availability assumptions, while others are managed as fuzzy variables where data is not explicitly required.The joint problems were synthesized into a piecewise fuzzy-probabilistic optimization model with the aim of minimizing total operational costs, and the optimal decision was deduced by solving this model.Further, the model was constructed incorporating multi-period observations, indicating its ability to generate optimal solutions for multiple procurement activity periods.Computational simulations were executed to demonstrate the calculation of the optimal decision and to appraise the proposed model.All calculations were performed in LINGO 19.0 optimization software, leveraging its uncertain programming package.The computational process employed the generalized reduced gradient -a popular method for solving optimization problems due to its requirement of only a differentiable objective function -in conjunction with the branch and bound algorithmrecognized for its simplicity in branching and bounding feasible solutions.The results affirmed that the proposed model successfully delivered the optimal solution for the problem at hand.Therefore, the proposed model is deemed appropriate for implementation by practitioners in manufacturing/retail industries as a decision-making tool to curtail operational costs associated with their procurement and warehousing operations.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".