Multi-criteria Human Resource Allocation for Optimization Problems Using Multi-objective Particle Swarm Optimization Algorithm
Multi-criteria Human Resource Allocation for Optimization Problems Using Multi-objective Particle Swarm Optimization Algorithm
复制标题
使用多目标粒子群优化算法解决优化问题的多准则人力资源分配
DOI：
10.1109/csse.2008.1506
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Lihua Gong
中科院分区：
文献类型：
--
作者：
Zhengyuan Jia;Lihua Gong
多标准人力资源配置涉及到如何在多个需求之间分配有限的人力资源，以优化当前目标。本文旨在利用多目标粒子群优化算法（MOPSO）解决人力资源多准则优化配置问题。在本文中，我们解决了这个问题，通过多目标决策模型，使用多目标粒子群算法。利用胜任力模型理论建立了人力资源优化配置的数学模型，并在此基础上提出了求解组合优化问题的多目标粒子群算法（MOPSO）。根据所提出的方法，我们应用MOPSO寻求问题的可行解。通过一个多目标资源分配问题的算例，验证了该算法的有效性。
Multi-criteria human resource allocation involves deciding how to divide human resource of limited availability among multiple demands in a way that optimizes current objectives. This paper aims to solve the multi-criteria optimal allocation of human resources issues using multi-objective particle swarm optimization (MOPSO). In this paper, we tackled this problem via a multi-objective decision-making model using a multi-objective PSO. We developed the Mathematical model of human resource optimization allocation using the competency model theory, and then in order to obtain a set of Pareto solutions efficiently, we proposed the multi-objective PSO (MOPSO) approach based on the decision-making model for solving combinatorial optimization problems. According to the proposed method, we applied the MOPSO to seek feasible solutions for the problem. The effectiveness of the proposed algorithm was validated by its application to an illustrative example dealing with multi-objective resource allocation problem.