Genetic algorithm compared to nonlinear optimization for labour and equipment assignment

Heng Li, Peter E.D. Love, Stephen Ogunlana

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)

Abstract

The genetic algorithm is a technique based on evolutionary optimization. A methodology for optimizing labour and equipment assignment using the genetic algorithm is presented. A number of modifications are introduced to the three operators of the genetic algorithm, namely, reproduction, crossover and mutation. Results from the genetic algorithm are compared to the nonlinear optimization technique in solving the labour and equipment assignment problem. A comparison of the two techniques indicates that the genetic algorithm has the capacity to ensure a global optimal solution. However, its computational operations take longer than the nonlinear optimization technique in obtaining near-optimal or optimal solutions.
Original languageEnglish
Pages (from-to)322-329
Number of pages8
JournalBuilding Research and Information
Volume26
Issue number6
DOIs
Publication statusPublished - 1 Jan 1998

Keywords

  • Genetic algorithms
  • Labour and equipment assignment
  • Nonlinear optimization

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction

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