Integrated optimization of dynamic cell formation and hierarchical production planning problems

•Present a simpler integrated optimization model for HPP and DCF, easier to solve.•Validate the influence of the number of cells on the effectiveness of the model.•Replace traditional “family” with “group” sharing similar setups in HPP literature.•The model is insensitive to demand volatility but saves more for more cell numbers. The paper presents a non-linear mixed integer programming (MIP) model that integrates dynamic cell formation (DCF) and hierarchical production planning (HPP). In the model, the DCF problem optimizes reconfiguration of machine cells, with varying production quantities in different periods determined by the HPP model. The HPP problem, formulated as an integrated model, determines the optimal production plans that meet forecast demands in the planning horizon, with capacity limitations of the machine cells formed through the DCF model. Compared with prior studies that integrate DCF and production planning (PP) problems, this paper provides the most comprehensive options needed to meet demands in dynamic cellular manufacturing systems (DCMS). With the introduction of HPP, the model could incorporate more decision variables such as inventory, internal production, subcontracting and backlogging costs, inter- and intra-cell material handling costs, yet with less solution complexity. The model is solved with branch-and-bound method, and its complexity and solution results are analyzed using empirical data from a mold manufacturing plant, with the results of the model compared with those in the literature. Our analyses show that the proposed model is simpler and easier to solve, and that the total cost is insensitive to demand volatility and decreases inversely with the number of cells.