  Advanced inventory management in complex supply chains requires effective and
robust nonlinear optimization due to the stochastic nature of supply and demand
variations. Application of estimated gradients can boost up the convergence of
Particle Swarm Optimization (PSO) algorithm but classical gradient calculation
cannot be applied to stochastic and uncertain systems. In these situations
Monte-Carlo (MC) simulation can be applied to determine the gradient. We
developed a memory based algorithm where instead of generating and evaluating
new simulated samples the stored and shared former function evaluations of the
particles are sampled to estimate the gradients by local weighted least squares
regression. The performance of the resulted regional gradient-based PSO is
verified by several benchmark problems and in a complex application example
where optimal reorder points of a supply chain are determined.
