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.