Abstracts – Browse Results
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Boussabaine, A H and Kirkham, R J (2004) Simulation of maintenance costs in UK local authority sport centres. Construction Management and Economics, 22(10), 1011-20.
Cheng, E W L and Li, H (2004) Contractor selection using the analytic network process. Construction Management and Economics, 22(10), 1021-32.
Clarke, L and Herrmann, G (2004) Cost vs. production: labour deployment and productivity in social housing construction in England, Scotland, Denmark and Germany. Construction Management and Economics, 22(10), 1057-66.
Lam, P T I, Kumaraswamy, M M and Ng, S (2004) The use of construction specifications in Singapore. Construction Management and Economics, 22(10), 1067-79.
Lingard, H and Saunders, A (2004) Occupational rehabilitation in the construction industry of Victoria. Construction Management and Economics, 22(10), 1091-101.
Phua, F T T (2004) The antecedents of co-operative behaviour among project team members: an alternative perspective on an old issue. Construction Management and Economics, 22(10), 1033-45.
Shohet, I M and Paciuk, M (2004) Service life prediction of exterior cladding components under standard conditions. Construction Management and Economics, 22(10), 1081-90.
Wang, R-C and Liang, T-F (2004) Project management decisions with multiple fuzzy goals. Construction Management and Economics, 22(10), 1047-56.
- Type: Journal Article
- Keywords: Project management decisions; multiple fuzzy goals programming; project cost and duration; fuzzy sets theory
- ISBN/ISSN: 0144-6193
- URL: https://doi.org/10.1080/0144619042000241453
- Abstract:
In real-world situations, the project managers must handle conflicting goals that govern the use of the resources within organizations. These conflicting goals must are required to be optimized simultaneously by the project managers in the framework of fuzzy aspiration levels. The multiple fuzzy goals programming model proposed herein helps project managers to minimize project total costs, total completion time, and total crashing costs considering direct costs, indirect costs, contractual penalty costs, duration of activities and total budget constraint. A numerical example illustrates the feasibility of applying the proposed model to a project management decision problem. Consequently, the proposed model yields an efficient compromise solution and the decision maker’s overall degree of satisfaction with determined multiple fuzzy goals values. Moreover, the proposed model effectively provides a systematic decision-making framework, enabling a decision maker to interactively modify the fuzzy data and model parameters until a satisfactory solution is obtained. The significant characteristics that differentiate the proposed model from the other models include flexible decision-making processes, multiple objective functions, and wide-ranging decision information.