Assessment of design approaches for reconfigurable manufacturing systems based on forecasted demand data

Abstract

In this paper, the problem of configuration design in Reconfigurable Manufacturing Systems (RMS) is addressed for a scalable production system that can produce different products which belong to the same part family. To satisfy products’ demand with minimum cost, RMS primary configuration must be changed according to demand rate of each product during its lifecycle. A new predictive approach is developed to design the system configuration during all production periods based on estimated demand data. A new and practical integer linear programming (ILP) formulation is proposed that highlights the importance of modular reconfigurable machine tools (RMTs) which can be used for adjusting the production capacity of the system by means of module exchange. The ILP model is verified by solving some of the available RMS design problems in the literature. The obtained results are compared with respect to the total system design and reconfiguration costs. Furthermore, to signify the importance of data accuracy, three different scenarios are designed with stochastic demand data and two other approaches namely, reactive, and predictive-reactive, are presented for drawing more useful and comprehensive conclusions. The obtained results from adopting each approach are theoretically analyzed and valuable managerial insights are provided based on total system design costs and unutilized equipment capacity.