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

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 s...

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Veröffentlicht in:	International journal of advanced manufacturing technology Jg. 67; H. 9-12; S. 2627 - 2638
Hauptverfasser:	Jia, Qiong, Seo, Yoonho
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.08.2013 Springer Nature B.V
Schlagworte:	Algorithms CAE) and Design Computer-Aided Engineering (CAD Engineering Industrial and Production Engineering Lower bounds Mechanical Engineering Media Management Original Article Particle swarm optimization Performance enhancement Production planning Production scheduling Project management Resource scheduling Resource-constrained project scheduling problem Move operator Rank-priority-based presentation Particle swarm optimization Double justification
ISSN:	0268-3768, 1433-3015
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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 J 30, J 60, and J 120 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 J 60 and J 120 instances.
AbstractList	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 J 30, J 60, and J 120 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 J 60 and J 120 instances. 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.
Author	Jia, Qiong Seo, Yoonho
Author_xml	– sequence: 1 givenname: Qiong surname: Jia fullname: Jia, Qiong organization: Department of Information Management Engineering, Korea University – sequence: 2 givenname: Yoonho surname: Seo fullname: Seo, Yoonho email: yoonhoseo@korea.ac.kr organization: Department of Information Management Engineering, Korea University
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Cites_doi	10.1007/978-1-4615-4629-0 10.1016/j.ejor.2006.12.033 10.1109/ICNN.1995.488968 10.1287/mnsc.16.1.93 10.1016/j.autcon.2007.11.004 10.1016/j.autcon.2004.08.006 10.1016/S0305-0548(97)00055-5 10.1287/mnsc.41.10.1693 10.1016/j.ejor.2005.01.065 10.1016/j.ejor.2004.04.008 10.1016/S0377-2217(02)00761-0 10.1016/S0377-2217(02)00136-4 10.1108/01443579310046454 10.1016/S0377-2217(98)00204-5 10.1016/0166-218X(83)90012-4 10.1016/j.ijproman.2005.06.006 10.1007/s00170-009-2483-z 10.1016/j.eswa.2009.07.024 10.1016/j.cor.2009.12.011 10.1007/978-1-4615-5533-9_7 10.1016/0377-2217(95)00357-6 10.1016/S0020-0190(02)00447-7 10.1080/07408179508936773 10.1007/s00170-006-0631-2 10.1057/palgrave.jors.2601563 10.1016/S0377-2217(99)00485-3 10.1007/978-1-4615-1507-4_25 10.1016/S0377-2217(97)00335-4 10.1023/B:ANOR.0000039524.09792.c9 10.1002/nav.10029 10.1007/s00291-003-0158-y 10.1002/(SICI)1520-6750(200004)47:3<201::AID-NAV2>3.0.CO;2-L 10.1287/mnsc.44.5.714 10.1016/j.amc.2007.04.096
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Copyright	Springer-Verlag London 2013 The International Journal of Advanced Manufacturing Technology is a copyright of Springer, (2013). All Rights Reserved.
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Keywords	Resource-constrained project scheduling problem Move operator Rank-priority-based presentation Particle swarm optimization Double justification
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Snippet	In this paper, an improved particle swarm optimization (PSO) algorithm is proposed for the resource-constrained project scheduling problem (RCPSP) which is...
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SubjectTerms	Algorithms CAE) and Design Computer-Aided Engineering (CAD Engineering Industrial and Production Engineering Lower bounds Mechanical Engineering Media Management Original Article Particle swarm optimization Performance enhancement Production planning Production scheduling Project management Resource scheduling
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Title	An improved particle swarm optimization for the resource-constrained project scheduling problem
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