Abstract
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.
Highlights
In the construction field, time saving can be transformed into some kinds of opportunities viz. early occupancy, saving in overhead cost or bonus (Li, Love 1997; Ng et al 2000)
Various TCO models for the construction domain have been developed, and these include a genetic algorithms (GAs) model (Feng et al 1997) which aimed at improving the hybrid linear/integer model put forwarded by Liu et al (1995) earlier; a TCO model utilizing an adaptive weight approach (Gen, Cheng 2000); and a multi-objective time-cost optimization model based on the amalgamation of both the GAs concepts and a modified adaptive weight approach (MAWA) (Zheng et al 2004)
A TCO model based on the ant colony system (ACS) techniques is developed to optimize the time and cost simultaneously for construction projects
Summary
Time saving can be transformed into some kinds of opportunities viz. early occupancy, saving in overhead cost or bonus (Li, Love 1997; Ng et al 2000). Clients and contractors should strive for optimizing both the time and cost are optimized concurrently if they were to maximize their profit under today’s competitive environment This has led to the development of time-cost optimization (TCO) concepts (Zheng, Ng 2005). Various TCO models for the construction domain have been developed, and these include a genetic algorithms (GAs) model (Feng et al 1997) which aimed at improving the hybrid linear/integer model put forwarded by Liu et al (1995) earlier; a TCO model utilizing an adaptive weight approach (Gen, Cheng 2000); and a multi-objective time-cost optimization model based on the amalgamation of both the GAs concepts and a modified adaptive weight approach (MAWA) (Zheng et al 2004). While the above GA-based multi-objective TCO models serve to establish an optimal overall time and total cost concurrently, the problem of premature convergence exists when it comes to searching for the globally non-dominated solutions (Zheng et al 2005). The model is validated through a case study and the results of comparison are summarized
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