Two stage stochastic integer programming model for workforce cross training

Uncertainty of product demand in the semiconductor manufacturing drives the need for decision makers to have robust plans about their workforce requirements over a finite time horizon. In today's competitive business environment uncertainty in labor costs and operator productivity levels can affect the validity of production plans. This paper gives a stochastic integer programming formulation of workforce planning activities (hiring, crosstraining and firing) under demand uncertainty and considers the stochastic nature of worker productivity when they are cross-trained. The objective of the model is to minimize the total workforce related costs under the condition of satisfying product demands. Benders Decomposition is used to solve the two stage stochastic programming model by relaxing second stage integrality and preliminary results are presented.