Time–cost optimization: using GA and fuzzy sets theory for uncertainties in cost

Uncertainties should be considered in any time–cost trade‐off problems when minimizing project cost and duration, which leads to the so‐called stochastic time–cost trade‐off problem. A new approach to investigate stochastic time–cost trade‐off problems employing fuzzy logic theory is presented. The proposed approach fully embeds the fuzzy structure of the uncertainties in total direct cost into the model. An appropriate GA is used to develop a solution to the multi‐objective fuzzy time cost model. The accepted risk level of the project manager is defined through α cut approach for which a separate Pareto front with set of non‐dominated solutions has been developed. To compare the alternative set of options for any assumed project duration, associated fuzzy costs for different values of α cut are ranked employing two appropriate approaches for fuzzy costs comparison. The proposed models are applied to solve two benchmark test problems. It is shown that the models facilitate the decision‐making process by selecting specified risk levels and employing the associated Pareto front.

[1]  Chung-Wei Feng,et al.  Stochastic construction time-cost trade-off analysis , 2000 .

[2]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Robert I. Carr,et al.  TIME-COST TRADE-OFF AMONG RELATED ACTIVITIES , 1989 .

[5]  Chung-Wei Feng,et al.  Construction Time-Cost Trade-Off Analysis Using LP/IP Hybrid Method , 1995 .

[6]  J W Fondahl,et al.  A non-computer approach to the critical path method , 1962 .

[7]  Chung-Wei Feng,et al.  Using genetic algorithms to solve construction time-cost trade-off problems , 1997 .

[8]  Nicolai Siemens.  A Simple CPM Time-Cost Tradeoff Algorithm , 1971 .

[9]  Hong Zhang,et al.  Application of Fuzzy Logic to Simulation for Construction Operations , 2003 .

[10]  Osama Moselhi,et al.  Project-network analysis using fuzzy sets theory , 1996 .

[11]  S. Thomas Ng,et al.  Fuzzy membership functions of procurement selection criteria , 2002 .

[12]  Osman Coşkunog¯lu,et al.  Optimal Probabilistic Compression of PERT Networks , 1984 .

[13]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[14]  Richard A. Watson,et al.  Reducing Local Optima in Single-Objective Problems by Multi-objectivization , 2001, EMO.

[15]  Hojjat Adeli,et al.  Resource Scheduling Using Neural Dynamics Model of Adeli and Park , 2001 .

[16]  Osama Moselhi,et al.  Schedule compression using the direct stiffness method , 1993 .

[17]  Peter E.D. Love,et al.  Using improved genetic algorithms to facilitate time-cost optimization , 1997 .

[18]  Heng Li,et al.  Using machine learning and GA to solve time-cost trade-off problems , 1999 .

[19]  S. Thomas Ng,et al.  Stochastic Time–Cost Optimization Model Incorporating Fuzzy Sets Theory and Nonreplaceable Front , 2005 .

[20]  Ka Chi Lam,et al.  A conceptual case-based decision model for mitigating construction delays , 2000 .

[21]  Mohan M. Kumaraswamy,et al.  Applying a Genetic Algorithm-Based Multiobjective Approach for Time-Cost Optimization , 2004 .

[22]  Mohan M. Kumaraswamy,et al.  Applying genetic algorithm techniques for time-cost optimization , 2002 .

[23]  David K. H. Chua,et al.  A MODEL FOR CONSTRUCTION PROJECT BUDGET AND SCHEDULE PERFORMANCES USING FUZZY DATA , 2001 .

[24]  Sou-Sen Leu,et al.  A GA-based fuzzy optimal model for construction time-cost trade-off , 2001 .