Improvement of PSO algorithm by memory based gradient search - application in inventory management
This work addresses the need for more effective optimization in complex inventory management, though it appears incremental as it builds on existing PSO and gradient methods.
The paper tackled the problem of improving Particle Swarm Optimization (PSO) for nonlinear optimization in stochastic systems by developing a memory-based gradient estimation method using stored particle evaluations, which was verified on benchmark problems and a supply chain application to determine optimal reorder points.
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.