Asas Journal

Asas Journal

A meta-heuristic algorithm for solving the time-cost optimization problem with multiple execution modes in construction projects.

Document Type : Original Article

Authors
Mohammad Javad Taheri Amiri 1
Farshidreza Haghighi 2
Ehsan Eshtehardian 3
Milad Hematian 4
Maedeh Javaheri Barforooshi 5
1 Department of Construction Management, Babol University of Technology
2 Faculty of Civil Engineering, Babol University of Technology
3 Department of project management, Tarbiat Modarres University
4 Department of Industrial engineering, Mazandaran University of Science and TeCHNOLOGY
5 Department of civil engineering, Islamic Azad University, Arak Branch
Abstract
In how to balance time and cost is crucial to construction project management. The main specification of time-cost trade off problem is discretization of the decision space to limited and accountable decision variables. In this situation the efficiency of the traditional methods decrease and applying of the evolutionary algorithm is necessary. The clever use of evolutionary algorithms allows us to solve many interesting and difficult problems and our ability to model complex problems increases tremendously. In this article use multi objective particle swarm optimization to solve two construction project management problems. These two projects include 7 and 70 activities with multiple modes. These problems are both in discrete domain including two objectives, separately. In this regard, some procedures has been suggested with solving by optimization algorithm. The developed optimization algorithm has been designed to find the best sequence and mode of operation, making it the shortest time and cost. Results show the advantages and effectiveness of the used procedures in repeating the optimal Pareto for optimization problems.
Keywords
Project management
Time and Cost trade-off
Multi-Objective Optimization
Multi Objective Particle Swarm Optimization
Pareto
Asas Journal
Volume 20, Issue 51
Summer 2018
Pages 16-29

  • Receive Date 28 February 2018
  • Revise Date 25 August 2018
  • Accept Date 13 September 2018