Application of fuzzy optimisation in forecasting and planning of construction industry

This chapter proposes a new method to obtain optimal solution using satisfactory approach in uncertain environment. The optimal solution is obtained by using possibilistic linear programming approach and intelligent computing by MATLAB?. The optimal solution for profit function, index quality and wo...

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Main Authors: Vasant, P., Barsoum, Nader, Kahraman, C., Dimirovski, G.
Other Authors: Dimitris Vrakas
Format: Book Chapter
Published: Idea Group 2008
Online Access:http://hdl.handle.net/20.500.11937/7124
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author Vasant, P.
Barsoum, Nader
Kahraman, C.
Dimirovski, G.
author2 Dimitris Vrakas
author_facet Dimitris Vrakas
Vasant, P.
Barsoum, Nader
Kahraman, C.
Dimirovski, G.
author_sort Vasant, P.
building Curtin Institutional Repository
collection Online Access
description This chapter proposes a new method to obtain optimal solution using satisfactory approach in uncertain environment. The optimal solution is obtained by using possibilistic linear programming approach and intelligent computing by MATLAB?. The optimal solution for profit function, index quality and worker satisfaction index in construction industry is considered. Decision maker and implementer tabulate the final possibilistic and realistic outcome for objective functions respect to level of satisfaction and vagueness for forecasting and planning. When the decision maker finds the optimum parameters with acceptable degree of satisfaction, he/she can apply the confidence of gaining much profit in terms of helping the public with high quality and least cost products. The proposed fuzzy membership function allows the implementer to find a better arrangement for the equipments in the production line to fulfill the wanted products in an optimum way.
first_indexed 2025-11-14T06:14:47Z
format Book Chapter
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T06:14:47Z
publishDate 2008
publisher Idea Group
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-71242017-09-13T14:40:30Z Application of fuzzy optimisation in forecasting and planning of construction industry Vasant, P. Barsoum, Nader Kahraman, C. Dimirovski, G. Dimitris Vrakas This chapter proposes a new method to obtain optimal solution using satisfactory approach in uncertain environment. The optimal solution is obtained by using possibilistic linear programming approach and intelligent computing by MATLAB?. The optimal solution for profit function, index quality and worker satisfaction index in construction industry is considered. Decision maker and implementer tabulate the final possibilistic and realistic outcome for objective functions respect to level of satisfaction and vagueness for forecasting and planning. When the decision maker finds the optimum parameters with acceptable degree of satisfaction, he/she can apply the confidence of gaining much profit in terms of helping the public with high quality and least cost products. The proposed fuzzy membership function allows the implementer to find a better arrangement for the equipments in the production line to fulfill the wanted products in an optimum way. 2008 Book Chapter http://hdl.handle.net/20.500.11937/7124 10.4018/978-1-59904-705-8.ch010 Idea Group restricted
spellingShingle Vasant, P.
Barsoum, Nader
Kahraman, C.
Dimirovski, G.
Application of fuzzy optimisation in forecasting and planning of construction industry
title Application of fuzzy optimisation in forecasting and planning of construction industry
title_full Application of fuzzy optimisation in forecasting and planning of construction industry
title_fullStr Application of fuzzy optimisation in forecasting and planning of construction industry
title_full_unstemmed Application of fuzzy optimisation in forecasting and planning of construction industry
title_short Application of fuzzy optimisation in forecasting and planning of construction industry
title_sort application of fuzzy optimisation in forecasting and planning of construction industry
url http://hdl.handle.net/20.500.11937/7124