Don't Fire Me, a Kernel Autoregressive Hybrid Model for Optimal Layoff Plan

Job cutting occurs when a modern service enterprise reduces the employing labour cost by firing some staffs. Making an appropriate layoff plan is always quite difficult since a bad job cutting has a serious impact on not only the organization but also the business process executing efficiency. Therefore, in this paper, we address the problem of making an optimal layoff plan with the least influence on the executing of the business process. The key challenge is estimating the process throughput under a layoff plan. We overcome this challenge by two steps: regressing the activity throughput by the stuff number and inferring process throughput by the maximum flow or minimum cut algorithm on the Directed Acyclic Graph of process. In the regressing step, a kernel autoregressive hybrid model is proposed, whose MSE is 30% lower than SVM. After that, an augmenting path based algorithm is introduced to make an optimal layoff plan. To evaluate the accuracy of our model, we conduct an external experiment on a real dataset from the workflow system employed in the government of Hangzhou City in China, which results in 9750969 logs from 2050 activities and 16295 employees in two years.