Decision Making in Materials Selection: an Integrated Approach with AHP
Bibliographic record
Abstract
Materials selection is a multi-criteria decision-making (MCDM) problems because the large numberof factors affecting on decision making. The best choice of available material is critical to thecompetitiveness and success of the manufacturing organisation. The analytical hierarchy process(AHP) is an important tool to solve MCDM problems. The choosing process of suitable material(such as a refrigerant fluid) for the Air Condition System (ACS) is faced with challenges such aslack of a systematic approach in setting the optimal performance in terms of its impact on theenvironment and operation. Selecting process for the one refrigerant from a range suitable ofsuitable refrigerant is complex process. The study presents a comparative performance analysisof ACS for using four alternative refrigerants R290, R410, R404 and R22. Then, one of these suitablerefrigerant is selected. The comparison is based on three criteria system operation, environmentand maintenance.Novels ACS performance assessment model is proposed based on an analytical hierarchy process(AHP). The model is based on two main criteria of ACS, quantitative criteria, cooling capacity(CC), coefficient of performance (COP), etc.).And qualitative criteria (Ozone Depletion Potential (ODP), Global Warming Potential (GWP) andmaintenance cost (MC)). It is necessary to look for new technique help decision making to selectalternative refrigerants, to fulfill the goals of the international protocols (Montreal and Kyoto)and optimum operation, to satisfy the growing worldwide demand, in addition the increase outdoortemperature in some countries.This study provides a developed methodology for evaluating ACS performance. Moreover, it helpsto select a robust decision. The results obtained from AHP process that the best rank of the suitablerefrigerant was R404 (0.3763) followed by R22 (0.3657) and so on for the other. Therefore,the proposed methodology can help the decision maker to select the best alternative for bothcriteria (qualitative and quantitative) in complex selecting process.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".