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Multi-period Decision for HRP of an Employee Leasing Center with One Type of Employees

机译:具有一种员工员工租赁中心HRP的多期决定

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This paper studies the issue of human resource plan in an employee leasing center. The center is engaged in the business of leasing its own contract employees and acting as an agent for flexible employees outside the center. The employees considered in this paper belong to one type of employees that are high-ranked and important for the firms. But this type of employees can be classified into two parts: the contract employees in the employee leasing center and the temporary employees in the manpower market. At first we take into account recruitment/dismissal factors and propose stochastically optimal Multi-period Decision model of the problem. In the model there are stochastic constraint conditions and the decision (control) in each period is the number of the contract employees that are recruited or dismissed by the center and the objective is to maximize the overall expected net revenue of the center over planning horizon. Then we transform the stochastic optimal control problem into the determined optimal control problem by transforming stochastic constraint conditions into determined constraint conditions. Furthermore we analyze the limits of the states in each period, develop a dynamic programming algorithm to search for an optimal solution to the problem and a heuristic approximate algorithm to find out an approximate optimal solution. Analyzing reveals that the heuristic algorithm is superior to the dynamic programming in time complexity. Finally, we use two algorithms to solve some simulating data case. The results reveal that the error between the two solutions is no more than 5percent for each case.
机译:本文研究的人力资源计划的雇员租赁中心的问题。该中心是从事租赁自己的合同制员工,并充当中心以外灵活就业人员的代理业务。本文认为属于一个类型的高排名和重要的是,企业员工的员工。但这种类型的员工可以分为两个部分:在人才租赁中心的合同制员工,并在人力资源市场中的临时雇员。首先,我们考虑到招聘/解雇因素和随机提出优化问题的多期决策模型。在模型中,有随机的约束条件,在每个时期的决策(控制)是由中央招募或驳回,目的是最大限度地提高中心在规划期内的整体预期净营收将合同的员工数量。然后,我们通过将随机约束条件为确定约束条件转变随机最优控制问题转化为确定的最优控制问题。此外,我们分析了国家的界限在每一个时期,发展动态规划算法寻找最佳的解决问题的方法和启发式近似算法找出一种近似最优解。分析表明,启发式算法是优于时间复杂度的动态规划。最后,我们使用两种算法来解决一些模拟数据的情况。结果表明,上述两种溶液之间的误差不超过5percent每个案例。

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