scholarly journals Food and Agricultural Product Pilot Selection for Geographical Indication Projects

New Medit ◽  
2020 ◽  
Vol 19 (3) ◽  
Author(s):  
SERTAÇ Dokuzlu ◽  
Jean-Claude PONS ◽  
Emilie VANDECANDELAERE ◽  
Maud ROGGIA ◽  
Maria RICCI ◽  
...  
Keyword(s):  
Lessons Learned ◽  
Agricultural Product ◽  
Control Group ◽  
Geographical Indication ◽  
Analytic Hierarchy ◽  
Product Selection ◽  
Standard Data ◽  
Concrete Experience ◽  
Hierarchy Process

This study assesses methodologies used during the selection of pilot products for the support to development of sustainable geographical indication projects by using the FAO/EBRD project as a case study. Relevant pilot products are essential to provide stakeholders with concrete experience, demonstrative effects and lessons learned in order to disseminate bets practices and facilitate scaling-out of sustainable GI processes. Qualitative data were transformed to quantitative data for product selection because data for local products were insufficient, and standard data were unavailable for each product. Analytic hierarchy process (AHP), simple scoring and geographical indication assessment form were used together as product selection methods. Gemlik Olives, the first registered geographical indication product in the Bursa province, was included during assessment as a control group. Six local products with a potential for GI registration were considered for pilot product selection to serve as demonstrative process. Results suggest that the most important selection criteria were “reputation of the product” and “power of the organisation” and first two ranked products selected for the project were Bursa Black Figs and Bursa Peaches.

scholarly journals Study on Nuclear Accident Precursors Using AHP and BBN

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Sujin Park ◽  
Huichang Yang ◽  
Gyunyoung Heo ◽  
Muhammad Zubair ◽  
Rahman Khalil Ur
Keyword(s):  
Safety Assessment ◽  
Posterior Probability ◽  
Prior Probability ◽  
Lessons Learned ◽  
Nuclear Accident ◽  
Analytic Hierarchy ◽  
Fukushima Accident ◽  
Hierarchy Process ◽  
Limited Scope

Most of the nuclear accident reports used to indicate the implicit precursors which are not easily quantified as underlying factors. The current Probabilistic Safety Assessment (PSA) is capable of quantifying the importance of accident causes in limited scope. It was, therefore, difficult to achieve quantifiable decision-making for resource allocation. In this study, the methodology which facilitates quantifying these precursors and a case study were presented. First, four implicit precursors have been obtained by evaluating the causality and hierarchy structure of various accident factors. Eventually, it turned out that they represent the lack of knowledge. After four precursors are selected, subprecursors were investigated and their cause-consequence relationship was implemented by Bayesian Belief Network (BBN). To prioritize the precursors, the prior probability is initially estimated by expert judgment and updated upon observations. The pair-wise importance between precursors is calculated by Analytic Hierarchy Process (AHP) and the results are converted into node probability tables of the BBN model. Using this method, the sensitivity and the posterior probability of each precursor can be analyzed so that it enables making prioritization for the factors. We tried to prioritize the lessons learned from Fukushima accident to demonstrate the feasibility of the proposed methodology.

Prioritization of Underlying Precursors in Nuclear Accidents

2014 ◽  
Author(s):  
Sujin Park ◽  
Huichang Yang ◽  
Gyunyoung Heo ◽  
Muhammad Zubair
Keyword(s):  
Safety Assessment ◽  
Posterior Probability ◽  
Prior Probability ◽  
Bayesian Belief Network ◽  
Lessons Learned ◽  
Nuclear Accidents ◽  
Analytic Hierarchy ◽  
Fukushima Accident ◽  
Hierarchy Process

The facts that the implicit precursors which are not easily quantified are underlying factors are already known. The current Probabilistic Safety Assessment (PSA) is limited in its ability to quantify the importance of accident causes. It is, therefore, difficult to achieve quantifiable decision-making for resource allocation. In this study, the methodology which facilitates quantifying these precursors and a case study is presented. First, four implicit precursors have been obtained by evaluating the causality and hierarchy structure of various accident factors. Eventually it turned out they represent the lack of knowledge. After four precursors are selected, sub-precursors were investigated and their cause-consequence relationship was implemented by Bayesian Belief Network (BBN). To prioritize the precursors, the prior probability is initially estimated by expert judgment and updated upon observations. The pair-wise importance between precursors is calculated by Analytic Hierarchy Process (AHP) and the results are converted into node probability tables of the BBN model. Using this method, the sensitivity and the posterior probability of each precursor can be analyzed so that it enables to make prioritization for the factors. Authors tried to prioritize the lessons-learned from Fukushima accident to demonstrate the feasibility of the proposed methodology.

scholarly journals User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Leandro Pecchia ◽  
Jennifer L Martin ◽  
Angela Ragozzino ◽  
Carmela Vanzanella ◽  
Arturo Scognamiglio ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Analytic Hierarchy Process ◽  
User Needs ◽  
Ct Scanner ◽  
Analytic Hierarchy ◽  
Hierarchy Process

Integrated Quality Function Deployment as a Tool for Quality Achievement in Healthcare

2012 ◽  
Vol 1 (2) ◽  
pp. 80-92 ◽  
Author(s):  
Chintala Venkateswarlu ◽  
A. K. Birru
Keyword(s):  
Analytic Hierarchy Process ◽  
Quality Function Deployment ◽  
Healthcare Services ◽  
Relative Importance ◽  
Analytic Hierarchy ◽  
Direct Evaluation ◽  
Quality Function ◽  
Model Combining ◽  
Hierarchy Process

Quality function deployment (QFD) is a methodology that extracts client demands (CDs) and inducting them in the final service/product. Once CDs are extracted from client the traditional QFD approach uses absolute importance to identify the degree of importance for each CD. Direct evaluation of CDs based on absolute weighting without tradeoffs is easy to perform, but may lead to serious deviations from reality. An alternative to avoid this problem is to adopt the analytic hierarchy process (AHP) approach. In this paper, an integrated model combining AHP and QFD has been delineated as a quality achievement tool in healthcare. A case study is performed on the healthcare services provided by government general hospital, Indore District, Madhya Pradesh, India and data has been analyzed to benchmark the proposed framework by computing the degree of relative importance for CDs through AHP and incorporating them in subsequent deployment matrices.

scholarly journals A decision support method for evaluating database designs

2012 ◽  
Vol 9 (1) ◽  
pp. 81-106 ◽  
Author(s):  
Erki Eessaar ◽  
Marek Soobik
Keyword(s):  
Decision Support ◽  
Data Model ◽  
Processing System ◽  
Transaction Processing ◽  
Database Management System ◽  
Pairwise Comparisons ◽  
Analytic Hierarchy ◽  
Hierarchy Process ◽  
Decision Support Method

It is possible to produce different database designs based on the same set of requirements to a database. In this paper, we present a decision support method for comparing different database designs and for selecting one of them as the best design. Each data model is an abstract language that can be used to create many different databases. The proposed method is flexible in the sense that it can be used in case of different data models, criteria, and designs. The method is based on the Analytic Hierarchy Process and uses pairwise comparisons. We also present a case study about comparing four designs of SQL databases in case of PostgreSQL? database management system. The results depend on the context where the designs will be used. Hence, we evaluate the designs in case of two different contexts - management of measurements data and an online transaction processing system.

scholarly journals Аn example of different AHP structuring in a forest management problem

2016 ◽  
pp. 127-137
Author(s):  
Milena Lakicevic ◽  
Bojan Srdjevic ◽  
Ivaylo Velichkov ◽  
Zorica Srdjevic
Keyword(s):  
Decision Making ◽  
Forest Management ◽  
Analytic Hierarchy Process ◽  
Evaluation Procedure ◽  
Decision Making Process ◽  
Management Problem ◽  
Consistency Check ◽  
Analytic Hierarchy ◽  
Hierarchy Process

The paper investigates how different hierarchy structuring in analytic hierarchy process (AHP) may affect the final results in the decision-making process. This problem is analyzed in a case study of the Rila monastery forest stands in Bulgaria. There were three similar and mutually overlapped hierarchies defined. A decision maker evaluated all of them and after analyzing final results and consistency performance, he selected and revised the most appropriate hierarchy structure. Consistency check assisted in detecting the judgments which have strongly violated evaluation procedure. These mistakes are interpreted as a consequence of a large number of required pair-wise comparisons. The paper emphases the importance of properly defining hierarchy structure and recommends using consistency analysis as a guide and not as a directive for the revision of judgments.

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