A combined fuzzy linear regression and fuzzy multiple objective programming approach for setting target levels in quality function deployment |
| |
| Authors: | Zeynep Sener E Ertugrul Karsak |
| |
| Affiliation: | 1. Interventional Pulmonology, Virginia Commonwealth University Medical Center, Richmond, VA;2. Interventional Pulmonology, Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD;3. Department of Pulmonary, Allergy and Critical Care Medicine, Cleveland Clinic, Cleveland, OH;4. Interventional Pulmonology Program, Emory University School of Medicine, Atlanta, GA;5. Pulmonary Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY;6. Medicine/Pulmonary Service, Memorial Sloan-Kettering Cancer Center, New York, NY;7. Division of Thoracic Surgery and Interventional Pulmonology Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA;8. Section of Interventional Pulmonology and Thoracic Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA;9. Northwestern University, Evanston, IL;10. National Jewish Health, Denver, CO;11. Internal Medicine-Pulmonary, Yale University, New Haven, CT;1. Laboratory of Probability and Statistics, Faculty of Sciences of Sfax, University of Sfax, Tunisia;2. LINA Computer Science Lab UMR 6241, Knowledge and Decision Team, University of Nantes, France;1. Department of Mathematics, Kharazmi University, Tehran, Iran;2. Dept. of Industrial Management, University of Seville, Spain |
| |
| Abstract: | Quality function deployment (QFD) is a systematic process for translating customer needs into engineering characteristics, and then communicating them throughout the enterprise in a way to ensure that details are quantified and controlled. The inherent fuzziness of relationships in QFD modeling justifies the use of fuzzy regression for estimating the relationships between both customer needs and engineering characteristics, and among engineering characteristics. Albeit QFD aims to maximize customer satisfaction, requirements related to enterprise satisfaction such as cost budget, extendibility, and technical difficulty also need to be considered. This paper presents a fuzzy multiple objective decision framework that includes not only fulfillment of engineering characteristics to maximize customer satisfaction, but also maximization of extendibility and minimization of technical difficulty of engineering characteristics as objectives subject to a financial budget constraint to determine target levels of engineering characteristics in product design. A real-world quality improvement problem is presented to illustrate the application of the decision approach. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
