Stochastic job-shop scheduling: A hybrid approach combining pseudo particle swarm optimization and the Monte Carlo method

Abstract

Many practical problems with uncertainties can be formulated as stochastic programming problems, and their optimal solutions are useful for decision-making. However, solving problems is generally difficult, and feasible methods for finding analytical solutions are needed. The purpose of this study is to propose a hybrid method that combines pseudo particle swarm optimization in an uncertain environment (PPSOUCE) and the Monte Carlo (MC) method for solving a stochastic programming problem. As an example, we used the proposed hybrid method to solve a stochastic job-shop scheduling problem (SJSSP). We compared our proposed PPSOUCE with the MC method to a hybrid method of a genetic algorithm in an uncertain environment (GAUCE) with the MC method. Numerical experiments illustrate that our method provides better solutions with shorter CPU times than those of the method that combines the GAUCE and the MC method.