Affiliations: School of Management Science and Engineering, Zhengzhou University of Aeronautics, Collaborative Innovation Center for Aeronautical Economic Development of Henan Province, Zhengzhou, Henan, China E-mail: [email protected]
Abstract: Many manufacturing and service firms operate with a
workforce that is partitioned into two or more work categories (skills) to
satisfy demands and reduce worker costs. In this paper, we describe a
multi-skill, cross-utilization problem in many departments, and a general
framework which may help in trade-offs between worker cost and met demands
is developed by a two-stage stochastic programming model. To reduce the cost
for total workers, the unmet demands are permitted and will be penalized.
For optimizing the number of workers staffed in many departments, factors
such as the worker cost coefficient, the penalty factor and the worker
efficiency are considered. In Stage 1, optimization model considered in this
paper is to minimize the sum of the cost for total workers and penalty, and
in Stage 2 the model seeks to minimize the penalty, which is quantified by
the unmet demands during assigning multi-skilled workers across departments.
We propose a nested genetic algorithm to solve the proposed two-stage
stochastic programming model. Finally, numerical studies are performed to
examine the efficiency of the proposed algorithm.
