An improved particle swarm optimization for the resource-constrained project scheduling problem

Qiong Jia, Yoon Ho Seo

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

In this paper, an improved particle swarm optimization (PSO) algorithm is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The algorithm treats the solutions of RCPSP as particle swarms and employs a double justification skill and a move operator for the particles, in association with rank-priority-based representation, greedy random search, and serial scheduling scheme, to execute the intelligent updating process of the swarms to search for better solutions. The integration combines and overhauls the characteristics of both PSO and RCPSP, resulting in enhanced performance. The computational experiments are subsequently conducted to set the adequate parameters and compare the proposed algorithm with other approaches. The results suggest that the proposed PSO algorithm augments the performance by 9.26, 16.17, and 10.45 % for the J30, J60, and J120 instances against the best lower bound-based PSO currently available, respectively. Moreover, the proposed algorithms demonstrate obvious advantage over other proposals in exploring solutions for large-scale RCPSP problems such as the J60 and J120 instances.

Original languageEnglish
Pages (from-to)2627-2638
Number of pages12
JournalInternational Journal of Advanced Manufacturing Technology
Volume67
Issue number9-12
DOIs
Publication statusPublished - 2013 Jan 11

Fingerprint

Particle swarm optimization (PSO)
Scheduling
Project management
Planning
Experiments

Keywords

  • Double justification
  • Move operator
  • Particle swarm optimization
  • Rank-priority-based presentation
  • Resource-constrained project scheduling problem

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software
  • Mechanical Engineering

Cite this

An improved particle swarm optimization for the resource-constrained project scheduling problem. / Jia, Qiong; Seo, Yoon Ho.

In: International Journal of Advanced Manufacturing Technology, Vol. 67, No. 9-12, 11.01.2013, p. 2627-2638.

Research output: Contribution to journalArticle

@article{0118a6d9ad2842bba3d7ac9f72242c4e,
title = "An improved particle swarm optimization for the resource-constrained project scheduling problem",
abstract = "In this paper, an improved particle swarm optimization (PSO) algorithm is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The algorithm treats the solutions of RCPSP as particle swarms and employs a double justification skill and a move operator for the particles, in association with rank-priority-based representation, greedy random search, and serial scheduling scheme, to execute the intelligent updating process of the swarms to search for better solutions. The integration combines and overhauls the characteristics of both PSO and RCPSP, resulting in enhanced performance. The computational experiments are subsequently conducted to set the adequate parameters and compare the proposed algorithm with other approaches. The results suggest that the proposed PSO algorithm augments the performance by 9.26, 16.17, and 10.45 {\%} for the J30, J60, and J120 instances against the best lower bound-based PSO currently available, respectively. Moreover, the proposed algorithms demonstrate obvious advantage over other proposals in exploring solutions for large-scale RCPSP problems such as the J60 and J120 instances.",
keywords = "Double justification, Move operator, Particle swarm optimization, Rank-priority-based presentation, Resource-constrained project scheduling problem",
author = "Qiong Jia and Seo, {Yoon Ho}",
year = "2013",
month = "1",
day = "11",
doi = "10.1007/s00170-012-4679-x",
language = "English",
volume = "67",
pages = "2627--2638",
journal = "International Journal of Advanced Manufacturing Technology",
issn = "0268-3768",
publisher = "Springer London",
number = "9-12",

}

TY - JOUR

T1 - An improved particle swarm optimization for the resource-constrained project scheduling problem

AU - Jia, Qiong

AU - Seo, Yoon Ho

PY - 2013/1/11

Y1 - 2013/1/11

N2 - In this paper, an improved particle swarm optimization (PSO) algorithm is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The algorithm treats the solutions of RCPSP as particle swarms and employs a double justification skill and a move operator for the particles, in association with rank-priority-based representation, greedy random search, and serial scheduling scheme, to execute the intelligent updating process of the swarms to search for better solutions. The integration combines and overhauls the characteristics of both PSO and RCPSP, resulting in enhanced performance. The computational experiments are subsequently conducted to set the adequate parameters and compare the proposed algorithm with other approaches. The results suggest that the proposed PSO algorithm augments the performance by 9.26, 16.17, and 10.45 % for the J30, J60, and J120 instances against the best lower bound-based PSO currently available, respectively. Moreover, the proposed algorithms demonstrate obvious advantage over other proposals in exploring solutions for large-scale RCPSP problems such as the J60 and J120 instances.

AB - In this paper, an improved particle swarm optimization (PSO) algorithm is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The algorithm treats the solutions of RCPSP as particle swarms and employs a double justification skill and a move operator for the particles, in association with rank-priority-based representation, greedy random search, and serial scheduling scheme, to execute the intelligent updating process of the swarms to search for better solutions. The integration combines and overhauls the characteristics of both PSO and RCPSP, resulting in enhanced performance. The computational experiments are subsequently conducted to set the adequate parameters and compare the proposed algorithm with other approaches. The results suggest that the proposed PSO algorithm augments the performance by 9.26, 16.17, and 10.45 % for the J30, J60, and J120 instances against the best lower bound-based PSO currently available, respectively. Moreover, the proposed algorithms demonstrate obvious advantage over other proposals in exploring solutions for large-scale RCPSP problems such as the J60 and J120 instances.

KW - Double justification

KW - Move operator

KW - Particle swarm optimization

KW - Rank-priority-based presentation

KW - Resource-constrained project scheduling problem

UR - http://www.scopus.com/inward/record.url?scp=84891488120&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84891488120&partnerID=8YFLogxK

U2 - 10.1007/s00170-012-4679-x

DO - 10.1007/s00170-012-4679-x

M3 - Article

VL - 67

SP - 2627

EP - 2638

JO - International Journal of Advanced Manufacturing Technology

JF - International Journal of Advanced Manufacturing Technology

SN - 0268-3768

IS - 9-12

ER -