Abstract
In any information technology enterprise, resource allocation and project scheduling are two important issues to reduce project duration, cost and risk in multi-project environments. This paper proposes an integrated and efficient computational method based on multi-objective particle swarm optimization to solve these two interdependent problems simultaneously. Minimizing the project duration, cost and maximizing the quality of resource allocation are all considered in our approach. Moreover, we suggest a novel non-dominated sorting vector evaluated particle swarm optimization (NSVEPSO). In order to improve its efficiency, this algorithm first uses a novel method for setting the global best position, and then executes a non-dominated sorting process to select new population. The performance of NSVEPSO is evaluated by comparison with SWTC_NSPSO, VEPSO and NSGA-III. The results of four experiments in the real scenario with small, medium and large data sizes show that NSVEPSO provides better boundary solutions and costs less time than the other algorithms.
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References
Alba, E., Chicano, J.F.: Software project management with GAs. Inf. Sci. 177(11), 2380–2401 (2007)
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)
Koulinas, G., Kotsikas, L., Konstantinos, A.: A particle swarm optimization-based hyper-heuristic algorithm for the classic resource constrained project scheduling problem. Inf. Sci. 277(1), 680–693 (2014)
Man-Im, A., Ongsakul, W., Singh, J.G.: Multi-objective economic dispatch considering wind power penetration using stochastic weight trade-off chaotic NSPSO. Electr. Power Energy Syst. 45(4), 1–18 (2017)
Omkar, S., Mudigere, D., Naik, G.N., Gopalakrishnan, S.: Vector evaluated particle swarm optimization (VEPSO) for multiobjective design optimization of composite structures. Comput. Struct. 86(1–2), 1–14 (2008)
Otero, L.D., Centeno, G., Ruiz-Torres, A.J.: A systematic approach for resource allocation in software projects. Comput. Ind. Eng. 56(4), 1333–1339 (2009)
Sedighizadeh, M., Faramarzi, H., Mahmoodi, M.: Hybrid approach to FACTS devices allocation using multi-objective function with NSPSO and NSGA2 algorithms in fussy framework. Electrical Power and Energy Systems 62(4), 586–598 (2014)
Acknowledgements
This work is supported by Natural Science Foundation of Zhejiang Province of China (Y16G010035, LY15F020036, LY14G010004), the Ningbo science and technology innovative team (2016C11024), and the Zhejiang Provincial Education Department project (Y201636906).
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Guo, Y., Zhang, H., Pang, C. (2020). Scheduling Multi-objective IT Projects and Human Resource Allocation by NSVEPSO. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_2
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DOI: https://doi.org/10.1007/978-981-15-2810-1_2
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