12,558 research outputs found
Genetic algorithm optimization for dynamic construction site layout planning
The dynamic construction site layout planning
(DCSLP) problem refers to the efficient placement and relocation
of temporary construction facilities within a dynamically
changing construction site environment considering
the characteristics of facilities and work interrelationships,
the shape and topography of the construction site, and the
time-varying project needs. A multi-objective dynamic optimization
model is developed for this problem that considers
construction and relocation costs of facilities, transportation
costs of resources moving from one facility to another or to
workplaces, as well as safety and environmental considerations
resulting from facilities’ operations and interconnections.
The latter considerations are taken into account in
the form of preferences or constraints regarding the proximity
or remoteness of particular facilities to other facilities
or work areas. The analysis of multiple project phases and
the dynamic facility relocation from phase to phase highly
increases the problem size, which, even in its static form,
falls within the NP (for Nondeterministic Polynomial time)-
hard class of combinatorial optimization problems. For this
reason, a genetic algorithm has been implemented for the
solution due to its capability to robustly search within a large
solution space. Several case studies and operational scenarios
have been implemented through the Palisade’s Evolver
software for model testing and evaluation. The results indicate
satisfactory model response to time-varying input data
in terms of solution quality and computation time. The model
can provide decision support to site managers, allowing
them to examine alternative scenarios and fine-tune optimal
solutions according to their experience by introducing desirable
preferences or constraints in the decision process
The Incremental Cooperative Design of Preventive Healthcare Networks
This document is the Accepted Manuscript version of the following article: Soheil Davari, 'The incremental cooperative design of preventive healthcare networks', Annals of Operations Research, first published online 27 June 2017. Under embargo. Embargo end date: 27 June 2018. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-017-2569-1.In the Preventive Healthcare Network Design Problem (PHNDP), one seeks to locate facilities in a way that the uptake of services is maximised given certain constraints such as congestion considerations. We introduce the incremental and cooperative version of the problem, IC-PHNDP for short, in which facilities are added incrementally to the network (one at a time), contributing to the service levels. We first develop a general non-linear model of this problem and then present a method to make it linear. As the problem is of a combinatorial nature, an efficient Variable Neighbourhood Search (VNS) algorithm is proposed to solve it. In order to gain insight into the problem, the computational studies were performed with randomly generated instances of different settings. Results clearly show that VNS performs well in solving IC-PHNDP with errors not more than 1.54%.Peer reviewe
2D multi-objective placement algorithm for free-form components
This article presents a generic method to solve 2D multi-objective placement
problem for free-form components. The proposed method is a relaxed placement
technique combined with an hybrid algorithm based on a genetic algorithm and a
separation algorithm. The genetic algorithm is used as a global optimizer and
is in charge of efficiently exploring the search space. The separation
algorithm is used to legalize solutions proposed by the global optimizer, so
that placement constraints are satisfied. A test case illustrates the
application of the proposed method. Extensions for solving the 3D problem are
given at the end of the article.Comment: ASME 2009 International Design Engineering Technical Conferences &
Computers and Information in Engineering Conference, San Diego : United
States (2009
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