Multi-objective dynamic cell formation problem: A stochastic programming approach

•This paper introduces human-related issues in the cell formation problem.•This paper considers a bi-objective dynamic mathematical model.•The paper tackles uncertainty of the cell formation problem using stochastic programming.

This paper addresses dynamic cell formation problem (DCFP) which has been explored vastly for several years. Although a considerable body of literature in this filed, two remarkable aspects have been significantly ignored so far, as uncertainty and human-related issues. In order to compensate such a shortage, this paper develops a bi-objective stochastic model. The first objective function of the developed model seeks to minimize total cost of machine procurement, machine relocation, inter-cell moves, overtime utilization, worker hiring/laying-off, and worker moves between cells; while the second objective function maximizes labor utilization of the cellular manufacturing system. In the developed model, labor utilization, worker overtime cost, worker hiring/laying off, and worker cell assignment are considered to tackle some of the most notable human-related issues in DCFP. Considering the complexity of the proposed model, a hybrid Tabu Search–Genetic Algorithm (TS–GA) is proposed whose strength is validated to obtain optimal and near optimal solutions through conducted experimental results.