Chaotic vibrating particles system for resource-constrained project scheduling problem

Document Type : Article

Authors

1 Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of ‎Science and Technology, Narmak, Tehran, P.O. Box 16846-13114, Iran‎

2 Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Narmak, Tehran, P.O. Box 16846-13114, Iran.

Abstract

Project scheduling in the resource-constrained situation is one of the key issues of project-oriented organizations. The aim of resource-constrained project scheduling problem (RCPSP) is finding a schedule with minimum makespan by considering precedence and resource constraints. RCPSP is a combinatorial optimization problem and belongs to the class of NP-hard problems. The exact methods search the entire search space and are unable to solve large-sized project networks. Thus metaheuristics are used to solve this problem with less computational time. Due to the probabilistic nature of metaheuristics, it is a challenging problem to balance between exploitation and exploration phases. The literature review shows that embedding with chaos improves both the convergence speed and the local optima avoidance of metaheuristics. This paper presents a Chaotic Vibrating Particles System (CVPS) optimization algorithm for solving the RCPSP. Vibrating Particles System (VPS) is a physic inspired metaheuristic which mimics the free vibration of single degree of freedom systems with viscous damping. The performance and applicability of the CVPS is compared with the standard VPS, and five well known algorithms on three benchmark instances of the RCPSPs Experimental studies reveals that the proposed optimization method is a promising alternative to assist project managers in dealing with RCPSP.

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References

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Scientia Iranica
Volume 27, Issue 4
Transactions on Civil Engineering (A)
July and August 2020
Pages 1826-1842

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History

  • Receive Date: 14 July 2018
  • Revise Date: 24 September 2018
  • Accept Date: 10 December 2018

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