An APS software selection methodology integrating experts and decisions-maker’s opinions on selection criteria: A case study
Bibliographic record
Abstract
With important advancements achieved in information technology, wide varieties of advanced planning and scheduling (APS) software has emerged in recent decades. Each of those APS software uses their own techniques, algorithms and logic to plan and schedule operations, which makes the task of evaluating them very difficult. However, choosing the right APS software is critical for companies because of significant resources engaged and risk of disturbing operations. Presently, a clear, structured and rational approach is lacking in the literature for APS software selection. The main contribution of this paper is to fill this gap by developing an APS software selection methodology. The methodology is based on fuzzy quality function deployment (QFD) and two well-known multiple criteria decision-making (MCDM) techniques, analytic hierarchy process (AHP) and VIKOR. This work considers both company needs and APS selection criteria to build a hybrid hierarchical decision structure. House of quality helps in translating the relevance of the company needs in the evaluation of criteria. Triangular fuzzy numbers are also used to reduce uncertainties in the process. An application of the proposed methodology to an aero-derivative gas turbine case company is carried out to demonstrate the useful and easy implementation of the proposed methodology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".