Using genetic algorithms to facilitate schedule optimization
Abstract. The common approach used in solving a principal subset of the time-cost trade-off problems in project management is through network compression. Network compression is an essential tool for the effective and efficient implementation of a project. However, for large projects with thousands...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2000
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_honors/138 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Abstract. The common approach used in solving a principal subset of the time-cost trade-off problems in project management is through network compression. Network compression is an essential tool for the effective and efficient implementation of a project. However, for large projects with thousands of activities as is normal for most private commercial and industrial projects and major government infrastructure projects, this approach becomes unfeasible, whether done manually, with or without the aid of the available commercial project management software, or through currently available computational approaches. This paper uses genetic algorithms (GAs), a set of tools based on natural selection and the mechanisms of population genetics, to solve the problem of network compression. A different perspective on the problem from that used in network compression, however, is taken and the problem is termed as schedule optimization. The approach presented in this paper allowed a powerful and user-friendly program to be developed for solving the problem of schedule optimization that is suitable for practical and commercial purposes. |
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