Genetic algorithm and Monte Carlo simulation for a stochastic capacitated disassembly lot-sizing problem under random lead times

The purpose of this research is to propose several optimization methods for the stochastic multi-period disassembly lot-sizing problem. case one type end-of-life product and a two-level system studied. lead times are discrete random variables with known bounded probability distribution. objective optimize expected value total cost, which sum setup overload inventory holding cost backlogging cost. Three approaches were developed solve studied problem: (i) two-stage mixed-integer linear programming model based on all possible scenarios small instances, (ii) sample average approximation approach Monte Carlo simulation medium-scale instances and, (iii) an genetic algorithm large-scale instances. Experimental results show effectiveness proposed models can be used support decision-making replenishment plans.