| Title |
Developing the Quality and Cost Optimization System for Metal Curtain Wall using Genetic Algorithm and Quality Function Deployment |
| Authors |
Lim, Tae-Kyung ; LeeDong-Eun |
| Keywords |
Curtain-wall ; Quality Function Deployment(QFD) ; Cost ; Quality ; Optimization ; Multi-objective Genetic Algorithm |
| Abstract |
This paper presents a tool called Quality-Cost optimization system, which integrates Genetic Algorithm (GA) and Quality Function Deployment (QFD), for tradeoff between quality and cost of the unitized metal curtain-wall unit. A construction owner as the external customer pursues to maximize the quality of the curtain-wall unit. However, the contractor as the internal customer pursues to minimize the cost involved in designing, manufacturing and installing the curtain-wall unit. It is crucial for project manager to find the tradeoff point which satisfies the conflicting interests pursued by the both parties. The system would be beneficial to establish a quality plan satisfying the both parties. Survey questionnaires were administered to the external customer who have an experience of project installing curtain-wall, the architects who are the independent assessor to obtain, and the internal customer who were involved in curtain-wall design and installation. The Customer Requirements (CRs) and their importance weights, the relationship between Customer Requirements and Technical Attributes (TAs) consisting of a curtain-wall unit, and the cost ratios of each components consisting curtain-wall unit are obtained from the three groups mentioned previously. The data obtained from the surveys were used as the QFD input data to compute the Owner Satisfaction (OS) and Contractor Satisfaction(CS). Multi-objective optimization method using GA is applied to optimize resource allocation under limited budget when multi-objectives, OS and CS, are pursued at the same time. The deterministic multi-objective optimization method using GA and QFD is extended to stochastic model to better deal with the uncertainties of QFD input data and the variability of QFD output data. A case study demonstrates the system and verifies the system conformance. |