Training Investment Decision Research Under The Influence Of The Trained Staff Turnover Risk

We all know that we live in an age whose topic is global and knowledge economy. In future, the competition of firm means the competition of talents. For winning, firms need more talented persons full of knowledge. Therefore, we should improve employees’ ability and working efficiency by training. However, by doing this employees may have higher requirement about both themselves and the firms. For example, they may feel dissatisfied with the enterprise that they’re in. All these may bring the problem of the outflow of talent. Trained staffs loss gives rise to the loss of human capital and the core competitive of the firm will be weak, and it’s one of the biggest problem that troubles boss. This paper try to found a training decision model including firm’s single phase and period situation, so that we can make our decision that based on it.This paper conducted the following major works and innovations:1) Propose a model about single phase training investment decision problem that influenced by the preferences of trained staffs risk. First, describing the problem, formulating hypotheses that the firm do training only once, then establishing a multi-objective programming model, using Pareto multi-objective genetic algorithm to solve the model, at last analyzing a numerical example. The result shows that, in staff training when the investment allocation to risk preferential workers is more than the risk-adverse workers, risk preferential employees’ output is higher than the risk-averse employees’. Accordingly, the corporate may choose to train more preference typed employees than averse employees. And this is the optimal training program. This shows that when the enterprise has risk preferential and risk averse employees at the same time, it is inclined to train the former than training the latter.2) Propose a model about multi-phase training investment decision problem that influenced by the preferences of trained staffs risk. First, describing the problem, formulating hypotheses that the firm do training more than one time, then establishing a multi-objective programming model, using particle swarm optimization algorithm to solve the model, at last analyzing a numerical example. The result shows that, the enterprise may give risk preferential employees a longer-term training; the training distribution ratio of risk-neutral employees is always the smallest. According to the calculation results of the study, under the fixed budget, we are able to determine the income levels, training time and investment allocation level of different types of staff, then provide corporation with a certain training scheme about investment and salary adjustment, so that we can reduce the turnover rate of trained staff effectively.