Dynamic construction site layout planning using max-min ant system

Construction site layout planning (CSLP) is a dynamic multi-objective optimization (MOO) problem as there are different facilities employed in the different construction phases of a construction project. In this study, a new method using continuous dynamic searching scheme to guide the max-min ant s...

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Vydáno v:Automation in construction Ročník 19; číslo 1; s. 55 - 65
Hlavní autoři: Ning, Xin, Lam, Ka-Chi, Lam, Mike Chun-Kit
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier B.V 2010
Elsevier
Témata:
Ant colony optimization algorithms
Applied sciences
Buildings
Buildings. Public works
Commercial building
Construction works
Continuous dynamic searching scheme
Dynamic construction site layout planning
Exact sciences and technology
Max-min ant system
Multi-objective optimization
Project management. Process of design
Site organization
Types of buildings
Max-min ant system
Ant colony optimization algorithms
Continuous dynamic searching scheme
Multi-objective optimization
Dynamic construction site layout planning
Office building
Project management
Multiobjective programming
Ant colony optimisation
Layout problem
Dynamical system
Optimization
Construction industry
Case study
Building site
System description
Planning
ISSN:0926-5805
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Popis
Shrnutí:Construction site layout planning (CSLP) is a dynamic multi-objective optimization (MOO) problem as there are different facilities employed in the different construction phases of a construction project. In this study, a new method using continuous dynamic searching scheme to guide the max-min ant system (MMAS) algorithm, which is one of the ant colony optimization (ACO) algorithms, to solve the dynamic CSLP problem under the two congruent objective functions of minimizing safety concerns and reducing construction cost is proposed. Using weighted sum method the MOO problem can be solved by the proposed MMAS method. An office building case was used to verify the capability of the proposed method to solve dynamic CSLP problem and the results are promising. The approach could be benchmarked by researchers using other advanced optimization algorithms to solve the same problem or expand the applications to other fields.
Bibliografie:ObjectType-Article-2
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ISSN:0926-5805
DOI:10.1016/j.autcon.2009.09.002