Optimization of construction time-cost trade-off analysis using genetic algorithms

1999 ◽  
Vol 26 (6) ◽  
pp. 685-697 ◽  
Author(s):  
Tarek Hegazy
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Computer Application ◽  
Time Cost ◽  
Construction Time ◽  
Trade Off ◽  
Project Duration ◽  
Combinatorial Optimization Problems ◽  
Management Procedures ◽  
Optimization Search

In the management of a construction project, the project duration can often be compressed by accelerating some of its activities at an additional expense. This is the so-called time-cost trade-off (TCT) problem, which has been studied extensively in the project management literature. TCT decisions, however, are complex and require planners to select appropriate resources for each project task, including crew size, equipment, methods, and technology. As combinatorial optimization problems, finding optimal decisions is difficult and time consuming considering the number of possible permutations involved. In this paper, a practical model for TCT optimization is developed using the principle of genetic algorithms (GAs). With its robust optimization search, the GAs model minimizes the total project cost as an objective function and accounts for project-specific constraints on time and cost. To maximize its benefits, the model has been implemented as a VBA macro program. This automates TCT analysis and combines it with standard resource-management procedures. Details of the proposed TCT model are described and several experiments conducted to demonstrate its benefits. The developments made in this paper provide guidelines for designing and implementing practical GA applications in the civil engineering domain.Key words: computer application, time-cost trade-off, construction management, genetic algorithms, and optimization.

scholarly journals Utility of Genetic Algorithms for Solving Large-Scale Construction Time-Cost Trade-Off Problems

2018 ◽  
Vol 32 (1) ◽  
pp. 04017072 ◽  
Author(s):  
Duzgun Agdas ◽  
David J. Warne ◽  
Jorge Osio-Norgaard ◽  
Forrest J. Masters
Keyword(s):  
Genetic Algorithms ◽  
Large Scale ◽  
Scale Construction ◽  
Time Cost ◽  
Construction Time ◽  
Trade Off

scholarly journals AN ANT COLONY SYSTEM BASED DECISION SUPPORT SYSTEM FOR CONSTRUCTION TIME-COST OPTIMIZATION

2012 ◽  
Vol 18 (4) ◽  
pp. 580-589 ◽  
Author(s):  
Yanshuai Zhang ◽  
S. Thomas Ng
Keyword(s):  
Optimization Problems ◽  
Cost Optimization ◽  
Ant Colony ◽  
Ant Colony System ◽  
Time Cost ◽  
Construction Time ◽  
Project Duration ◽  
Test Study ◽  
Proposed Model ◽  
Computational Resources

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.

scholarly journals AN IMPROVED TIME-COST TRADE-OFF MODEL WITH OPTIMAL LABOR PRODUCTIVITY

2020 ◽  
Vol 26 (2) ◽  
pp. 113-130
Author(s):  
Chien-Liang Lin ◽  
Yu-Che Lai
Keyword(s):  
Genetic Algorithms ◽  
Labor Productivity ◽  
Real World ◽  
Future Research ◽  
Illustrative Case ◽  
Time Cost ◽  
Trade Off ◽  
Project Duration ◽  
Liquidated Damages ◽  
Real World Applications

Optimization of the time-cost trade off (TCT) has received considerable attention for several decades. However, few studies have considered improving performance/productivity of existing crews. To shorten the gap to real-world applications, this study presents an improved TCT model that considers variable productivity using genetic algorithms (GAs). Through an illustrative case and a real world case, the results demonstrate that improving labor productivity of selected activities by allocating existing crews and management can yield an optimized solution. As such, a decision maker can implement a better optimized technique to reduce a project duration under budget while reducing the risk of liquidated damages. The main contribution of this study is to apply managerial improvement of labor productivity to TCT optimization, the project duration can be reduced owing to improved productivity of existing crews rather than inefficient overmanning, overlapping or costly substitution. In the end, three important managerial insights are presented and future research is recommended.

A new framework for time-cost trade-off considering float loss impact

2017 ◽  
Vol 22 (1) ◽  
pp. 20-36 ◽  
Author(s):  
Sameh Monir El-Sayegh ◽  
Rana Al-Haj
Keyword(s):  
Optimization Technique ◽  
Time Cost ◽  
Trade Off ◽  
Deterministic Optimization ◽  
Content Type ◽  
Project Duration ◽  
Trade Offs ◽  
Project Objectives ◽  
The Impact ◽  
New Framework

Purpose The purpose of this paper is to propose a new framework for time–cost trade-off. The new framework provides the optimum time–cost value taking into account the float loss impact. Design/methodology/approach The stochastic framework uses Monte Carlo Simulation to calculate the effect of float loss on risk. This is later translated into an added cost to the trade-off problem. Five examples, from literature, are solved using the proposed framework to test the applicability of the developed framework. Findings The results confirmed the research hypothesis that the new optimum solution will be at a higher duration and cost but at a lower risk compared to traditional methods. The probabilities of finishing the project on time using the developed framework in all five cases were better than those using the classical deterministic optimization technique. Originality/value The objective of time–cost trade-off is to determine the optimum project duration corresponding to the minimum total cost. Time–cost trade-off techniques result in reducing the available float for noncritical activities and thus increasing the schedule risks. Existing deterministic optimization technique does not consider the impact of the float loss within the noncritical activities when the project duration is being crashed. The new framework allows project managers to exercise new trade-offs between time, cost and risk which will ultimately improve the chances of achieving project objectives.

Incentive genetic algorithm based time–cost trade-off analysis across a build–operate–transfer project concession period

2011 ◽  
Vol 38 (2) ◽  
pp. 166-174 ◽  
Author(s):  
Hongxian Li ◽  
Mohamed Al-Hussein ◽  
Zhen Lei
Keyword(s):  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Genetic Algorithms ◽  
Construction Cost ◽  
Methodological Framework ◽  
Time Cost ◽  
Trade Off ◽  
Operation Period ◽  
Concession Period ◽  
New Infrastructure

The build–operate–transfer (BOT) scheme is widely applied to finance new infrastructure projects with private sector (concessionaire) participation. For a predetermined concession period (CP), assuming that CP consists of the construction duration (CD) and the concession operation period (OP), different construction durations result in different profits for the concessionaire. Meanwhile, according to the time–cost trade-off (TCT) principle, shortening the CD increases the construction cost; shortening the CD also prolongs the OP, which could increase the total benefit of BOT projects. Hence, how to arrange construction reasonably to maximize the whole profit is a key issue for a concessionary. This paper proposes a methodological framework including optimization, sensitivity analysis, and improved (incentive) genetic algorithms (GA) for BOT projects. Through the proposed methodological framework, the reasonable construction duration of a BOT project can be obtained. A numerical example is used to verify the proposed methodology.

Effective Memetic Algorithms for VLSI Design = Genetic Algorithms + Local Search + Multi-Level Clustering

2004 ◽  
Vol 12 (3) ◽  
pp. 327-353 ◽  
Author(s):  
Shawki Areibi ◽  
Zhen Yang
Keyword(s):  
Genetic Algorithms ◽  
Local Search ◽  
Optimization Problems ◽  
Memetic Algorithms ◽  
Vlsi Circuit ◽  
Circuit Partitioning ◽  
Placement Problem ◽  
Solution Quality ◽  
Combinatorial Optimization Problems ◽  
Cell Placement

Combining global and local search is a strategy used by many successful hybrid optimization approaches. Memetic Algorithms (MAs) are Evolutionary Algorithms (EAs) that apply some sort of local search to further improve the fitness of individuals in the population. Memetic Algorithms have been shown to be very effective in solving many hard combinatorial optimization problems. This paper provides a forum for identifying and exploring the key issues that affect the design and application of Memetic Algorithms. The approach combines a hierarchical design technique, Genetic Algorithms, constructive techniques and advanced local search to solve VLSI circuit layout in the form of circuit partitioning and placement. Results obtained indicate that Memetic Algorithms based on local search, clustering and good initial solutions improve solution quality on average by 35% for the VLSI circuit partitioning problem and 54% for the VLSI standard cell placement problem.

scholarly journals Focusing on the Golden Ball Metaheuristic: An Extended Study on a Wider Set of Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
E. Osaba ◽  
F. Diaz ◽  
R. Carballedo ◽  
E. Onieva ◽  
A. Perallos
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Golden Ball ◽  
The One

Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB). The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem) and one combinatorial design problem (the one-dimensional bin packing problem) have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results.

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