Self-adaptive ant colony optimization for construction time-cost optimization

Kybernetes ◽  
2013 ◽  
Vol 42 (8) ◽  
pp. 1181-1194 ◽  
Author(s):  
Huimin Li ◽  
Peng Li
2012 ◽  
Vol 18 (4) ◽  
pp. 580-589 ◽  
Author(s):  
Yanshuai Zhang ◽  
S. Thomas Ng

Time and cost are the two most important factors to be considered in every construction project. In order to maximize the profit, both the client and contractor would strive to minimize the project duration and cost concurrently. In the past, most of the research studies related to construction time and cost assumed time to be constant, leaving the analyses based purely on a single objective of cost. Acknowledging this limitation, an evolutionary-based optimization algorithm known as an ant colony system is applied in this study to solve the multi-objective time-cost optimization problems. In this paper, a model is developed using Visual Basic for Application™ which is integrated with Microsoft Project™. Through a test study, the performance of the proposed model is compared against other analytical methods previously used for time-cost modeling. The results show that the model based on the ant colony system techniques can generate better solutions without utilizing excessive computational resources. The model, therefore, provides an efficient means to support planners and managers in making better time-cost decisions efficiently.


2010 ◽  
Vol 13 (1) ◽  
pp. 17-30
Author(s):  
Luan Hong Pham ◽  
Nhan Thanh Duong

Time-cost optimization problem is one of the most important aspects of construction project management. In order to maximize the return, construction planners would strive to optimize the project duration and cost concurrently. Over the years, many researches have been conducted to model the time-cost relationships; the modeling techniques range from the heuristic method and mathematical approach to genetic algorithm. In this paper, an evolutionary-based optimization algorithm known as ant colony optimization (ACO) is applied to solve the multi-objective time-cost problem. By incorporating with the modified adaptive weight approach (MAWA), the proposed model will find out the most feasible solutions. The concept of the ACO-TCO model is developed by a computer program in the Visual Basic platforms. An example was analyzed to illustrate the capabilities of the proposed model and to compare against GA-based TCO model. The results indicate that ant colony system approach is able to generate better solutions without making the most of computational resources which can provide a useful means to support construction planners and managers in efficiently making better time-cost decisions.


2015 ◽  
Vol 22 (2) ◽  
pp. 187-198 ◽  
Author(s):  
Zhigang SHEN ◽  
Ashkan HASSANI ◽  
Qian SHI

Existing research on construction time-cost tradeoff issues rarely explore the origin of the crashing cost. Crashing cost function was either assumed without much justification, or came from historical data of some real pro­jects. As a result the conclusions of the papers can hardly be used to guide allocations of labor and equipment resources respectively. The authors believe Cobb-Douglas function provides a much-needed piece to modeling the cost functions in the construction time-cost tradeoff problem during the crashing process. We believe this new perspective fills a gap of existing time-cost tradeoff research by considering project duration, labor and equipment cost as parameters of the Cobb- Douglas production function. A case study was presented to show how the proposed framework works. Our conclusion is that introducing Cobb-Douglas function into time-cost tradeoff problem provides us extra capacity to further identify the optimal allocations of labor and equipment resources during crashing.


2011 ◽  
Vol 20 (8) ◽  
pp. 1193-1203 ◽  
Author(s):  
E. Kalhor ◽  
M. Khanzadi ◽  
E. Eshtehardian ◽  
A. Afshar

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