Exploring Multicriteria Decision Strategies in GIS with Linguistic Quantifiers – A Case Study of Residential Quality Evaluation

Commonly used GIS combination operators such as Boolean conjunction/disjunction and weighted linear combination can be generalized to the ordered weighted averaging (OWA) family of operators. This multicriteria evaluation method allows decision-makers to define a decision strategy on a continuum between pessimistic and optimistic strategies. Recently, OWA has been introduced to GIS-based decision support systems. We propose to extend a previous implementation of OWA with linguistic quantifiers to simplify the definition of decision strategies and to facilitate an exploratory analysis of multiple criteria. The linguistic quantifier-guided OWA procedure is illustrated using a dataset for evaluating residential quality of neighborhoods in London, Ontario. <div><br></div><div>This is a post-peer-review, pre-copyedit version of an article published in Journal of Geographical Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10109-005-0159-2 <br></div>

Exploring Multicriteria Decision Strategies in GIS with Linguistic Quantifiers – A Case Study of Residential Quality Evaluation

Commonly used GIS combination operators such as Boolean conjunction/disjunction and weighted linear combination can be generalized to the ordered weighted averaging (OWA) family of operators. This multicriteria evaluation method allows decision-makers to define a decision strategy on a continuum between pessimistic and optimistic strategies. Recently, OWA has been introduced to GIS-based decision support systems. We propose to extend a previous implementation of OWA with linguistic quantifiers to simplify the definition of decision strategies and to facilitate an exploratory analysis of multiple criteria. The linguistic quantifier-guided OWA procedure is illustrated using a dataset for evaluating residential quality of neighborhoods in London, Ontario. <div><br></div><div>This is a post-peer-review, pre-copyedit version of an article published in Journal of Geographical Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10109-005-0159-2 <br></div>

APPLYING THE SUPERIOR IDENTIFICATION GROUP LINGUISTIC VARIABLE TO CONSTRUCT KANO MODEL ORIENTED QUALITY FUNCTION DEPLOYMENT

This study attempts to manipulate 2-tuple linguistic variables rather than pure linguistic variables in quality function deployment (QFD) in order to significantly improve the identification of the QFD model. The Kano model, a two-dimensional quality technique, is also integrated to recognize the degree of urgency in terms of enhancing and prioritizing quality-related requirements of customers via a fuzzy linguistic quantifier with a soft majority concept to fit the optimal aggregation weights. This study also retains the goodness on the usage of multi-granularity linguistic approach to facilitate the implementation of a group decision. Simultaneously, two-dimensional analysis is performed to explain the results synthetically between relationship matrix and correlation matrix from a management perspective, capable of providing comprehensive information for the decision process. Owing to the integration of several quality and management methods, results of this study demonstrate the capability of TRIZ.

Fuzzy Simple Additive Weighting Untuk Diagnosis Penyakit Pneumonia

Pneumonia is an infection of the lungs that is caused by bacteria, viruses, fungi, or parasites. It is characterized primarily by inflammation of the alveoli in the lungs or by alveoli that are filled with fluid (alveoli are microscopic sacs in the lungs that absorb oxygen). The shortage of medical personnel at health community centers to serve a population often results in care delays for pneumonia patients. The purpose of this research is to make a modelling of group decision support system in diagnosing pneumonia in adult patients. The system is designed as a tool for medical personnel in diagnosing pneumonia patients.Group Decision Support System (GDSS) is developed by using Fuzzy Simple Additive weighting methods. The preference scoring of three experts i.e. a pulmonary specialist, an internist and a pharmacist is carried out by applying triangular fuzzy numbers. In the aggregation stage, preferences makes use of Fuzzy Linguistic quantifier, stage rangking employs Simple Additive Weighting while Forward Chaining is employed in the inference process. The system is tested by inputting the symptoms of pneumonia without the involvement of an expert. The results shows that the system is capable in diagnosing pneumonia.Keywords: GDSS, Fuzzy, Simple Additive Weighting, Pneumonia Abstrak. Pneumonia adalah infeksi paru-paru yang disebabkan oleh bakteri, virus, jamur, atau parasit. Hal ini ditandai terutama oleh peradangan alveoli di paru-paru atau alveoli yang berisi cairan (alveoli adalah kantung mikroskopis di paru-paru yang menyerap oksigen), terbatasnya tenaga medis di puskesmas disbanding dengan jumlah penduduk berakibat sering terlambatnya pelayanan terhadap pasien pneumonia. Tujuan penelitian ini adalah membuat pemodelan sistem pendukung keputusan kelompok untuk mendiagnosis pasien pneumonia pada orang dewasa. Sistem ini dirancang sebagai alat bantu tenaga medis dalam mendiagnosis pasien pneumonia. Sistem Pendukung Keputusan Kelompok (SPKK) dikembangkan menggunakan metode Fuzzy Simple Additive Weighting. Pemberian nilai preferensi tiga orang pakar antara lain ahli paru-paru, ahli internis dan ahli farmasi menggunakan bilangan fuzzy segitiga. Pada tahap agregasi preferensi digunakan Fuzzy Linguistic Quantifier, tahap perangkingan menggunakan Simple Additive Weighting dan proses inferensi menggunakan Forward Chaining. Sistem diuji dengan cara memasukkan gejala-gejala pneumonia tanpa melibatkan seorang pakar. Hasil penelitian menunjukkan bahwa sistem dapat mendiagnosis penyakit pneumonia.Kata Kunci: SPKK, Fuzzy, Simple Additive Weighting, Pneumonia