Discrete particle swarm optimization method for the large-scale discrete time–cost trade-off problem |
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| Affiliation: | 1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China;2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China;1. Department of Economics and Business Economics, Aarhus University, Denmark;2. Department of Civil Engineering, The University of Hong Kong, Hong Kong, China;1. Department of Mathematics, University of Osijek, Trg Lj. Gaja 6, HR, Osijek 31000, Croatia;2. Faculty of Electrical Engineering, University of Osijek, Cara Hadrijana 10b, HR, Osijek 31000, Croatia;1. Department of Electrical Engineering, University of Huelva, Carretera Palos-Huelva, s/n., 21071 Palos de la Frontera, Huelva, Spain;2. Research Group in Electrical Technologies for Sustainable and Renewable Energy (PAIDI-TEP-023), Department of Electrical Engineering, EPS Algeciras, University of Cádiz, Avda. Ramón Puyol, s/n., 11202 Algeciras, Cádiz, Spain;3. Research Group in Research and Electrical Technology (PAIDI-TEP-152), Department of Electrical Engineering, EPS Linares, University of Jaén, C/ Alfonso X, nº 28., 23700 Linares, Jaén, Spain |
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| Abstract: | Despite many research studies have concentrated on designing heuristic and meta-heuristic methods for the discrete time–cost trade-off problem (DTCTP), very little success has been achieved in solving large-scale instances. This paper presents a discrete particle swarm optimization (DPSO) to achieve an effective method for the large-scale DTCTP. The proposed DPSO is based on the novel principles for representation, initialization and position-updating of the particles, and brings several benefits for solving the DTCTP, such as an adequate representation of the discrete search space, and enhanced optimization capabilities due to improved quality of the initial swarm. The computational experiment results reveal that the new method outperforms the state-of-the-art methods, both in terms of the solution quality and computation time, especially for medium and large-scale problems. High quality solutions with minor deviations from the global optima are achieved within seconds, for the first time for instances including up to 630 activities. The main contribution of the proposed particle swarm optimization method is that it provides high quality solutions for the time–cost optimization of large size projects within seconds, and enables optimal planning of real-life-size projects. |
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