Investigating the impact of operational variables on manufacturing cost by simulation optimization

In this paper, we focus on the relationship between operations-based variables (specifically, production speed, scrap rate and maintenance speed) and the manufacturing cost. These variables usually produce opposite influences on the variable cost and the fixed cost. For example, setting the production speed at a high level is beneficial for reducing the variable cost. However, maintaining the high speed incurs considerable fixed costs at the same time. Therefore, an optimization approach is necessary to determine the optimal values of the operational variables for minimizing the average cost. First, a discrete-event simulation procedure is designed for describing the stochastic production environment and for evaluating the settings. Then, an optimization approach based on the ordinal optimization (OO) philosophy and particle swarm optimization (PSO) is used to search in the continuous space of the operational variables. In this process, the optimal computing budget allocation technique is applied so as to fully utilize the computational resource and potentially save the computational time. Finally, numeric computations are conducted for verifying the effectiveness of the proposed algorithm. Sensitivity analysis and discussions are also presented.