SIMULATION–BASED BENDERS CUTS: A NEW CUTTING APPROACH TO APPROXIMATELY SOLVE SIMULATION–OPTIMIZATION PROBLEMS

摘要

Large solution space is one of the main features of simulation-optimization problems. Reducing the cardinality of the set of alternatives is a key point for increasing the efficiency of simulation-optimization methods. In this work, a new cutting approach is proposed for this purpose. The approach exploits the Benders Decomposition framework that can be effectively applied when the simulation-optimization problems are represented using Discrete Event Optimization models. Benders Decomposition subproblems represent the simulation components, hence, cuts can be easily generated observing the values of the variables while a system alternative is simulated, without solving any subproblem. The cut generation procedure is proposed to approximately solve the Server Allocation Problem in a tandem queueing system. Results on randomly generated instances show its effectiveness in decreasing the computational effort by reducing the solution space.