Simulation and mathematical programming for a multi-objective configuration problem in a hybrid flow shop

This paper introduces an application of simulation-based multi-objective optimization to solve a system configuration problem in a hybrid flow shop system. The test case is provided by a firm that manufactures mechanical parts for the automotive sector. We present an architecture that uses both discrete-event simulation and mathematical programming tools in order to solve the problem. The multiple-objective nature of the problem is preserved throughout the proposed approach, using Pareto-dominance concepts both to eliminate inefficient solutions within the proposed solution algorithm and to provide the user with efficient solutions. Mathematical programming is used to cull the required number of simulation runs. Computational results obtained using a real-world case study are reported. The proposed approach is benchmarked against a general purpose simulation-optimization engine in order to prove its effectiveness.