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Research on Maximum Return Evaluation of Human Resource Allocation Based on Multi-Objective Optimization

机译：基于多目标优化的人力资源分配最大回报评价研究

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摘要

In this paper, a human resource allocation method based on the multi-objective hybrid genetic algorithm is proposed, which uses the multi-stage decision model to resolve the problem. A task decision is the result of an interaction under a set of conditions. There are some available decisions in each stage, and it is easy to calculate their immediate effects. In order to give a set of optimal solutions with limited submissions, a multi-objective hybrid genetic algorithm is proposed to solve the combinatorial optimization problems, i.e. using the multi-objective hybrid genetic algorithm to find feasible solutions at all stages and the bilateral matching of the scientific research projects and participants. First, the mathematical description of the bilateral matching problem supporting members grouping is given. On this basis, a bilateral matching multi-objective decision-making model is established with the objective of optimizing three actual indexes of reasonable grouping. According to the characteristics of the model, a multi-objective genetic algorithm-based solution method is designed. Based on the matching model, a human resource management system based on a browser/server architecture is designed to improve the practicability. Finally, an example is given to demonstrate the effectiveness and feasibility of the model.
机译：本文提出了一种基于多目标混合遗传遗传算法的人力资源分配方法，它使用了多阶段决策模型来解决问题。任务决定是在一系列条件下的交互的结果。每个阶段都有一些可用的决策，很容易计算立即效果。为了给出有限的提交有限的最佳解决方案，提出了一种多目标混合遗传算法来解决组合优化问题，即使用多目标混合遗传算法在所有阶段找到可行的解决方案和双边匹配科研项目与参与者。首先，给出了支持成员分组的双边匹配问题的数学描述。在此基础上，建立了双边匹配的多目标决策模型，其目的是优化合理分组的三个实际指标。根据该模型的特征，设计了一种基于多目标遗传算法的解决方案方法。基于匹配模型，旨在提高基于浏览器/服务器架构的人力资源管理系统以提高实用性。最后，给出了一个例子来证明模型的有效性和可行性。

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