Comparison of spreadsheet-based optimization tools applied to construction site layout problem

Abstract This paper compares different spreadsheet-based optimization tools applied to a practical example of a construction site layout problem. The objective of the optimization is to minimize the total time of material transportation by optimal positioning of tower crane and work facilities on the construction site with repetitive operations. Computer programs, such as MS Excel, LibreOffice Calc, and Google Sheets can be applied as modeling tools for a variety of construction optimization problems in addition to their usual functionalities. In this study, LibreOffice Calc Solver, MS Excel Solver, along with other MS Excel add-ins, i.e., OpenSolver, Evolver, and What’sBest, are analyzed. The capabilities of optimization tools mentioned above are examined on the problem of optimal positioning of tower crane and work facilities on the construction site. The results obtained by optimization tools are noted and discussed. The paper ends with conclusions and recommendations for further research.

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A Comparison of Mixed Integer Programming Models for the Construction Site Layout Problem

Proceedings of the 32nd International Symposium on Automation and Robotics in Construction and Mining (ISARC 2015) ◽

10.22260/isarc2015/0109 ◽

2015 ◽

Author(s):

Ahmed W. A. Hammad ◽

Ali Akbarnezhad ◽

David Rey

Keyword(s):

Integer Programming ◽

Mixed Integer Programming ◽

Programming Models ◽

Mixed Integer ◽

Construction Site ◽

Layout Problem ◽

Site Layout ◽

Construction Site Layout

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Bilevel Construction Site Layout Optimization Based on Hazardous-Material Transportation

Journal of Infrastructure Systems ◽

10.1061/(asce)is.1943-555x.0000303 ◽

2016 ◽

Vol 22 (3) ◽

pp. 04016014 ◽

Cited By ~ 10

Author(s):

Jiuping Xu ◽

Siwei Zhao ◽

Zongmin Li ◽

Ziqiang Zeng

Keyword(s):

Layout Optimization ◽

Construction Site ◽

Hazardous Material ◽

Site Layout ◽

Hazardous Material Transportation ◽

Material Transportation ◽

Construction Site Layout

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Complementing the Two Intelligent Bionic Optimization Algorithms to Solve Construction Site Layout Problem

International Journal of Advancements in Computing Technology ◽

10.4156/ijact.vol4.issue1.1 ◽

2012 ◽

Vol 4 (1) ◽

pp. 1-14

Author(s):

Xin Ning ◽

Chao Chen

Keyword(s):

Optimization Algorithms ◽

Construction Site ◽

Layout Problem ◽

Site Layout ◽

Construction Site Layout

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Efficient multi-objective optimization algorithms for construction site layout problem

Scientia Iranica ◽

10.24200/sci.2017.4216 ◽

2017 ◽

Vol 0 (0) ◽

pp. 0-0 ◽

Cited By ~ 4

Author(s):

A. Kaveh ◽

M. Rastegar Moghaddam ◽

M. Khanzadi

Keyword(s):

Optimization Algorithms ◽

Construction Site ◽

Multi Objective Optimization ◽

Layout Problem ◽

Multi Objective ◽

Site Layout ◽

Construction Site Layout

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A Hybrid Tabu Search-Based Artificial Immune Algorithm for Construction Site Layout Optimization

Research and Development on Information and Communication Technology ◽

10.32913/rd-ict.vol2.no15.470 ◽

2018 ◽

Author(s):

Vu Duc Quang ◽

Nguyen Van Truong ◽

Vu Thi Thuy ◽

Hoang Xuan Huan

Keyword(s):

Tabu Search ◽

Construction Management ◽

Hybrid Algorithm ◽

Construction Site ◽

Artificial Immune ◽

Layout Problem ◽

Complex Issue ◽

Site Layout ◽

Unique Nature ◽

Construction Site Layout

Layout of temporary facilities on a construction site is essential to enhance productivity and safety. It is a complex issue due to the unique nature of construction. This problem is validated as an NP-hard and one of the challenging problems in the field of construction management. In this paper, we proposed a hybrid algorithm, named topt-aiNet, to solve the construction site layout problem by combining the aiNet algorithm with Tabu search. Experimental results showed that the proposed algorithm outperformed the stateof-the-art ones. DOI: 10.32913/rd-ict.vol2.no15.470

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Genetic algorithm optimization for dynamic construction site layout planning

Organization Technology and Management in Construction An International Journal ◽

10.1515/otmcj-2016-0026 ◽

2018 ◽

Vol 10 (1) ◽

pp. 1655-1664 ◽

Cited By ~ 8

Author(s):

Panagiotis M. Farmakis ◽

Athanasios P. Chassiakos

Keyword(s):

Genetic Algorithm ◽

Optimization Problems ◽

Solution Space ◽

Time Varying ◽

Construction Site ◽

Large Solution ◽

Layout Planning ◽

Combinatorial Optimization Problems ◽

Site Layout ◽

Construction Site Layout

AbstractThe 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.

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