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