An APS software selection methodology integrating experts and decisions-maker’s opinions on selection criteria: A case study
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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