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.
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