A Model Predictive Control-Based Scheduling for Optimizing both Quality and Productivity in Multistage Manufacturing Process

In this paper, we present a model predictive control (MPC) based approach for scheduling multiple lots in multistage manufacturing process (MMP) to optimize both productivity and quality. We design a mixed integer linear programming (MILP) problem for hybrid flow shop MMP environment, and employ an MPC strategy which can repeatedly solves a subproblem of immediate timestep, rather than solving the scheduling problem over the entire range at once. Furthermore, we consider scenarios where a machine is temporarily unavailable or urgent lot arrivals with specific earliest starting times, which is frequently happening in real industrial settings. Simulation results demonstrate that the proposed approach can optimize both makespan and yield at the same time, which is practically applicable in real-time decision making environments such as semiconductor fabrication plant.