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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| Published in: | International journal of advanced manufacturing technology Vol. 67; no. 9-12; pp. 2627 - 2638 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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01.08.2013
Springer Nature B.V |
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| 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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| Keywords | Resource-constrained project scheduling problem Move operator Rank-priority-based presentation Particle swarm optimization Double justification |
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| References | ÖzdamarLUlusoyGA survey on the resourceconstrained project scheduling problemIIE Trans19952757458610.1080/07408179508936773 LuMLamHCDaiFResource-constrained critical path analysis based on discrete event simulation and particle swarm optimizationAutom Constr200817667068110.1016/j.autcon.2007.11.004 HeilmannRA branch-and-bound procedure for the multi-mode resource-constrained project scheduling problem with minimum and maximum time lagsEur J Oper Res2003144234836519362991012.9051310.1016/S0377-2217(02)00136-4 HerroelenWDemeulemeesterEDe ReyckBResource-constrained project scheduling—a survey of recent developmentsComput Oper Res199825427930216608791040.9052510.1016/S0305-0548(97)00055-5 HartmannSKolischRExperimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problemEur J Oper Res200012723944070985.9003610.1016/S0377-2217(99)00485-3 KoneOArtiguesCLopezPMongeauMEvent-based MILP models for resource-constrained project scheduling problemsComput Oper Res201138131326792101231.9020210.1016/j.cor.2009.12.011 BlazewiczJLenstraJKRinooy KanAHGScheduling subject to resource constraints: classification and complexityDiscrete Appl Math1983511246788150516.6803710.1016/0166-218X(83)90012-4 Kolisch R, Padman R (1997) An integrated survey of deterministic project scheduling. 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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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