An Advanced Stochastic Time-Cost Tradeoff Analysis Based on a CPM-Guided Genetic Algorithm
This article presents an advanced stochastic time‐cost tradeoff (ASTCT) method that performs time‐cost tradeoff analysis by identifying optimal set(s) of construction methods for activities, hence reducing the project completion time and cost simultaneously. ASTCT involves a stochastic time‐cost tra...
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| Published in | Computer-aided civil and infrastructure engineering Vol. 30; no. 10; pp. 824 - 842 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Blackwell Publishing Ltd
01.10.2015
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| Online Access | Get full text |
| ISSN | 1093-9687 1467-8667 |
| DOI | 10.1111/mice.12148 |
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| Abstract | This article presents an advanced stochastic time‐cost tradeoff (ASTCT) method that performs time‐cost tradeoff analysis by identifying optimal set(s) of construction methods for activities, hence reducing the project completion time and cost simultaneously. ASTCT involves a stochastic time‐cost tradeoff analysis method based on a critical path method (CPM)‐guided genetic algorithm (GA). It makes use of CPM schedule data exported from a project management software, and alternative construction methods obtained from estimators (i.e., normal and accelerated durations and costs) for each activity. It simulates schedule networks, identifies an optimal set of GA parameters (i.e., population size, crossover rate, mutation rate, and stopping rule), implements several GA cycles, and computes near‐optimal solution(s) exhaustively. This study is of value to practitioners because ASTCT improves the computation time, reliability, and usability of existing GA‐based time‐cost tradeoff methods. The study is also of relevance to researchers because it facilitates experiments using different GA parameters expeditiously. Two test cases verify the usability and validity of the computational methods. |
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| AbstractList | This article presents an advanced stochastic time‐cost tradeoff (ASTCT) method that performs time‐cost tradeoff analysis by identifying optimal set(s) of construction methods for activities, hence reducing the project completion time and cost simultaneously. ASTCT involves a stochastic time‐cost tradeoff analysis method based on a critical path method (CPM)‐guided genetic algorithm (GA). It makes use of CPM schedule data exported from a project management software, and alternative construction methods obtained from estimators (i.e., normal and accelerated durations and costs) for each activity. It simulates schedule networks, identifies an optimal set of GA parameters (i.e., population size, crossover rate, mutation rate, and stopping rule), implements several GA cycles, and computes near‐optimal solution(s) exhaustively. This study is of value to practitioners because ASTCT improves the computation time, reliability, and usability of existing GA‐based time‐cost tradeoff methods. The study is also of relevance to researchers because it facilitates experiments using different GA parameters expeditiously. Two test cases verify the usability and validity of the computational methods. |
| Author | Lee, Dong-Eun Arditi, David Lee, Hyung-Guk Yi, Chang-Yong |
| Author_xml | – sequence: 1 givenname: Hyung-Guk surname: Lee fullname: Lee, Hyung-Guk organization: School of Architecture and Civil Engineering, Kyungpook National University, Daegu, South Korea – sequence: 2 givenname: Chang-Yong surname: Yi fullname: Yi, Chang-Yong organization: School of Architecture and Civil Engineering, Kyungpook National University, Daegu, South Korea – sequence: 3 givenname: Dong-Eun surname: Lee fullname: Lee, Dong-Eun email: dolee@knu.ac.kr organization: School of Architecture and Civil Engineering, Kyungpook National University, Daegu, South Korea – sequence: 4 givenname: David surname: Arditi fullname: Arditi, David organization: Department of Civil and Architectural Engineering, Illinois Institute of Techology, IL, Chicago, USA |
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| References_xml | – reference: Arango, C., Cortés, P., Onieva, L. & Escudero, A. (2013), Simulation-optimization models for the dynamic berth allocation problem, Computer-Aided Civil and Infrastructure Engineering, 28(10), 769-79. – reference: Al-Bazi, A. & Dawood, N. (2010), Developing crew allocation system for the precast industry using genetic algorithms, Computer-Aided Civil and Infrastructure Engineering, 25(8), 581-95. – reference: Blacud, N. A., Bogus, S. M., Diekmann, J. E. & Molenaar, K. R. (2009), Sensitivity of construction activities under design uncertainty, Journal of Construction Engineering and Management, 135(3), 199-206. – reference: Magrab, E. B. (2000), An Engineer's Guide to MATLAB, 10th edn. Prentice Hall, Upper Saddle River, NJ. – reference: Rojas, E. M. & Mukherjee, A. (2005b), Interval temporal logic in general-purpose situational simulations, Journal of Computing in Civil Engineering, 19(1), 83-93. – reference: Lee, D. E., Bae, T. H. & Arditi, D. (2012), Advanced stochastic schedule simulation system, Civil Engineering and Environmental Systems, 29(1), 23-40. – reference: Leu, S. S. & Yang, C. H. (1999), A genetic-algorithm-based resource-constrained construction scheduling system, Construction Management & Economics, 17(6), 767-76. – reference: Li, H., Cao, J. & Love, P. (1999), Using machine learning and GA to solve time-cost tradeoff problems, Journal of Construction Engineering and Management, ASCE, 125(5), 347-53. – reference: Siemens, N. (1971), A simple CPM time-cost tradeoff algorithm, Management Science, 17(6), 354-63. – reference: Adeli, H. & Wu, M. (1998), Regularization neural network for construction cost estimation, Journal of Construction Engineering and Management, ASCE, 124(1), 18-24. – reference: Hegazy, T. (2002), Computer-Based Construction Project Management, Prentice-Hall, Upper Saddle River, NJ. – reference: Lin, D. Y. & Ku, Y. H. 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