Optimal sizing, scheduling and shift policy of the grinding section of a ceramic tile plant

This paper addresses the optimal design of the grinding section of a ceramic tile plant operating in a cyclic mode with the units (mills) following a batch sequence. The optimal design problem of this single product plant is formulated with a fixed time horizon of one week, corresponding to one cycle of production, and using a discrete-time resource task network (RTN) process representation. The size of the individual units is restricted to discrete values, and the plant operates with a set of limited resources (workforce and equipment). The goal is to determine the optimal number and size of the mills to install in the grinding section, the corresponding production schedule, and shift policy. This problem involves labor/semi-labor intensive (LI/SLI) units with a depreciation cost of the same order as that of the operation cost. The optimal design of the grinding section comprises the trade-off between these two costs. The resulting optimization formulation is of the form of a mixed integer linear programming (MILP) problem, solved using a branch and bound solver (CPLEX 9.0.2). The optimal solution is analyzed for various ceramic tile productions and different shift policies.Scope and PurposeThis paper addresses an optimal design case study of the grinding section of a ceramic tile plant with respect to the net capacity to install, the operation scheduling and the shift policy to implement. A mathematical programming model is formulated based on the resource task network (RTN) framework representation. The problem is solved using a branch and bound algorithm. The main goal of this work is to apply optimal design/scheduling general tools to real problems commonly found in the ceramic industry sector, a particular case of labor intensive plants. The application of these methodologies to this case study demonstrates as well the importance of adopting suitable optimization strategies for plant design in order to improve the economical performance of the production lines.