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Using Genetic Algorithms to Solve Industrial Time-cost Trade-off Problems


Affiliations
1 Industrial Engineering Department, College of Engineering, Shahid Bahonar University of Kerman, Iran, Islamic Republic of
 

Time-cost trade-off analysis is one of the most important aspects of industrial project planning and control. There are trade-offs between time and cost to complete the activities of a project; in general, the less expensive the resources used, the longer it takes to complete an activity. Existing methods for time-cost trade-off problems focus on using heuristics or mathematical programming. These methods, however, are not efficient enough to solve large scale CPM problems. This paper presents a Multi-Objective Genetic Algorithm (MOGA) approach to time-cost trade-off problems (TCTP). Finding optimal decisions is difficult and time-consuming considering the numbers of permutations involved. This type of problem is NP-hard, hence attainment of IP/LP solutions, or solutions via Total Enumeration (TE) is computationally prohibitive. The MOGA approach searches for locally Pareto-optimal or locally non-dominated frontier where simultaneously optimization of time-cost is desired. The application of the proposed algorithm is demonstrated through an example project a real life case. The results illustrate the promising performance of the proposed algorithm.

Keywords

Time-cost Trade-off, Genetic Algorithms, Project Management
User

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  • Using Genetic Algorithms to Solve Industrial Time-cost Trade-off Problems

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Authors

Ghorbanali Mohammadi
Industrial Engineering Department, College of Engineering, Shahid Bahonar University of Kerman, Iran, Islamic Republic of

Abstract


Time-cost trade-off analysis is one of the most important aspects of industrial project planning and control. There are trade-offs between time and cost to complete the activities of a project; in general, the less expensive the resources used, the longer it takes to complete an activity. Existing methods for time-cost trade-off problems focus on using heuristics or mathematical programming. These methods, however, are not efficient enough to solve large scale CPM problems. This paper presents a Multi-Objective Genetic Algorithm (MOGA) approach to time-cost trade-off problems (TCTP). Finding optimal decisions is difficult and time-consuming considering the numbers of permutations involved. This type of problem is NP-hard, hence attainment of IP/LP solutions, or solutions via Total Enumeration (TE) is computationally prohibitive. The MOGA approach searches for locally Pareto-optimal or locally non-dominated frontier where simultaneously optimization of time-cost is desired. The application of the proposed algorithm is demonstrated through an example project a real life case. The results illustrate the promising performance of the proposed algorithm.

Keywords


Time-cost Trade-off, Genetic Algorithms, Project Management

References





DOI: https://doi.org/10.17485/ijst%2F2011%2Fv4i10%2F30171