الگوریتم های تقریبی دینامیکی پویا برای پروسه های تصمیم گیری مارکوف در مقیاس وسیع بدون تقارن و کاربرد آنها برای بهینه سازی سیستم تولید و توزیع

For numerical comparisons, the optimal control problem of the three-stage JIT-based production and distribution system with stochastic demand and production capacity is formulated as a UMDP. The demand distribution is changed from a shifted binomial distribution in Ohno (2011) to a Poisson distribution and near-optimal policies of the optimal control problems with 35,973,840 states are computed by the SBMPI algorithms and the SBMPIM. The computational result shows that the SBMPI algorithms are at least 100 times faster than the SBMPIM in solving the numerical problems and are robust with respect to initial policies. Numerical examples are solved to show an effectiveness of the near optimal control utilizing the SBMPI algorithms compared with optimized pull systems with optimal parameters computed utilizing the SBOS (simulation-based optimal solutions) from Ohno (2011).