基于记忆梯度搜索的PSO算法改进--在库存管理中的应用
Improvement of PSO algorithm by memory based gradient search - application in inventory management
摘要：
由于供需变化的随机性，复杂供应链中的高级库存管理需要有效和鲁棒的非线性优化。粒子群优化（PSO）算法中梯度估计的应用可以提高算法的收敛性，但经典的梯度计算不能应用于随机和不确定系统。在这些情况下，可以应用蒙特卡罗（MC）模拟来确定梯度。我们开发了一种基于记忆的算法，其中不是生成和评估新的模拟样本，而是对粒子的存储和共享的前函数评估进行采样，以通过局部加权最小二乘回归来估计梯度。通过几个基准问题和一个复杂的供应链最优再订货点确定的应用实例，验证了基于区域梯度的粒子群优化算法的性能。
英文摘要：
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.
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