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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Vydáno v:International journal of advanced manufacturing technology Ročník 67; číslo 9-12; s. 2627 - 2638
Hlavní autoři: Jia, Qiong, Seo, Yoonho
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.08.2013
Springer Nature B.V
Témata:
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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Shrnutí: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.
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-012-4679-x