A scenario decomposition-genetic algorithm method for solving stochastic air cargo container loading problems

In this study, we develop a solution method for solving air express cargo loading problems under stochastic demands. The method is designed by combining the scenario decomposition and genetic algorithm (GA) techniques. Numerical tests are performed to evaluate the performance of the solution method using data for the Asian operations of an international express carrier. The results show that the solutions obtained from our method are very close to the optimal solutions. Furthermore, our method has an advantage in terms of computation time over the CPLEX optimization algorithm.

Research highlights
► A solution method that combines scenario decomposition and the genetic algorithm.
► The method successfully solves stochastic air cargo container loading problems.
► The method has the advantage of less computation time compared with CPLEX.
► 3 is found to be the most suitable number of solutions from each sub-problem.