Optimization of production systems through integration of computer simulation, design of experiment, and Tabu search: the case of a large steelmaking workshop

Abstract

The objective of this study is to optimize the performance of discrete production systems by integration of computer simulation, design of experiment (DOE), and Tabu search (TS). Optimizing performance of a steelmaking workshop was considered as the case of this study, but it could be used to optimize the throughput of other production system. The simulation model is built by considering all major and detailed operations and interacting systems of the workshop. The results and the structure of the integrated simulation model are verified and validated by t test. To integrate simulation outputs with DOE, decision making parameters are defined as number of machines, operators, etc. (k factors). To estimate and assess the effects of each of the factors and their two-way interactions on response variable, a complete 3 k factorial design with lower and upper limits and a center point is considered. Furthermore, response surface methodology (RSM) is used to optimize the response variable. Because a first-order model may not be adequate for the RSM, a polynomial order regression equation is developed by least square method. By steepest ascent, the local optimum is identified. However, the global optimal solution is computed by Tabu search which uses a metaheuristic approach. Previous studies use integration of DOE and simulation to find optimum alternative. This is usually conducted by RSM and steepest ascent which locates local optimum solution. However, integration of DOE and TS locates global optimum solution.