SBIR PHASE II: A Neuro-Dynamic Programming Approach to Stochastic Control

* 李9704090 这个小企业创新研究第二阶段项目将开发神经动态规划（NDP）方法，以解决商业上重要的复杂随机控制问题-供应链管理。第一阶段的研究成功地建立了NDP算法可以导致显着节省了广泛接受的物流在一类供应链上被用作测试平台。该第二阶段计划将进一步发展和简化NDP方法。此外，算法将被推广，使它们适用于一个非常广泛的类的现实供应链，包括制造和分销网络。对这些新问题的性能将通过广泛的实验和比较，与当前国家的最先进的ecologistics进行评估。一旦国家发展计划方法得到充分发展，将寻求许可安排，将国家发展计划为基础的优化模块纳入目前广泛使用的许多供应链管理产品。 一种改进的解决供应链管理物流问题的方法将对所有行业的公司产生巨大的商业利益。将这项技术整合到现有的商业供应链管理软件中，可以提高许多美国公司的效率。此外，在这项研究中开发的一般NDP方法具有更广泛的潜在范围，因为它们可以用来解决其他复杂的随机控制问题，出现在许多领域的国家的重要性，包括过程控制，排队和调度，数据网络优化。 * p：/sbir/yhashimi/abstracts/9704090.doc

*** Lee 9704090 This Small Business Innovation Research Phase II project will develop neuro-dynamic programming (NDP) methods to address a commercially important complex stochastic control problem--that of supply-chain management. The Phase I research successfully established that NDP algorithms could lead to significant savings over well-accepted heuristics on a class of supply chains that was used as a testbed. This Phase II program will further develop and streamline the NDP methodology. Furthermore, algorithms will be generalized so that they apply to a very broad class of realistic supply chains, including manufacturing and distribution networks. Performance on these new problems will be assessed through extensive experimentation and comparison with current state-of-the-art heuristics. Once the NDP methodology is fully developed, licensing arrangements will be sought to integrate NDP-based optimization modules into the many supply-chain management products currently in widespread use. An improved approach for addressing the logistics of supply-chain management will be of great commercial interest to companies across all industries. Integration of the technology into existing commercial supply-chain management softuare products widely used in manufacturing can enhance the efficiency of numerous U.S. corporations. Furthermore, the general NDP methods developed in this research have a much broader potential scope, in that they can be used to address other complex stochastic control problems that arise in many areas of national importance, including process control, queuing and scheduling, and data network optimization. *** p:/sbir/yhashimi/abstracts/9704090.doc