GOALI: Inventory Management in Assemble-to-Order Systems: Analysis, Policies, and Asymptotic Optimality

GOALI：按订单组装系统中的库存管理：分析、策略和渐近最优性

• 批准号: 1363314
• 负责人: Qiong Wang
• 金额: $ 35万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1363314&HistoricalAwards=false
• 关键词: GOALI; Inventory; Management; Assemble; Order

The objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) award is to develop an optimization framework for inventory management in Assemble-to-Order manufacturing systems with general structures and parameter values. The basic approach is to employ a stochastic program to transform an intractable control problem into a much simpler solvable optimization problem. The optimal solution of the stochastic program, which sets a provable lower bound on the long-run average expected total inventory cost, will be used to inspire a family of inventory control policies for managing dynamic systems. These policies will be tested by the criterion of asymptotic optimality on the diffusion scale. In other words, when component lead times increase, thus making the system more costly, the proposed policies will drive the percentage difference between the inventory cost and its lower bound to zero. Performance of these policies in various parameter regions will also be evaluated empirically by numerical experiments, simulations, and case studies, including comparisons with other approaches and stress-tests under the least favorable conditions. Algorithms involving approximations will be developed to allow the approach to be implementable on an industrial scale.If successful, the results of this research will help manufacturing companies to achieve significant savings in inventory cost, thus improving the efficiency of the widely-adopted Assemble-to-Order manufacturing strategy. Because the policies are particularly effective for systems with long lead times, they will mitigate negative impacts on the inventory system of long transportation delays between manufacturers and their oversea suppliers and thus lead to changes in the cost-benefit calculation that favor insourcing, which will contribute to the US manufacturing sector's revitalization. The combined use of stochastic programs and asymptotic analysis also points to a promising new approach for inventory management optimization, which involves many notoriously hard problems.

该奖项的目标是为具有一般结构和参数值的按订单装配制造系统中的库存管理开发一个优化框架。基本的方法是采用随机规划将一个棘手的控制问题转化为一个简单得多的可解优化问题。随机规划的最优解，它设置了一个可证明的长期平均预期总库存成本的下限，将被用来激发一个家庭的库存控制策略管理动态系统。这些政策将通过扩散尺度上的渐近最优性标准进行测试。换句话说，当组件交付时间增加，从而使系统成本更高时，所提出的策略将使库存成本与其下限之间的百分比差异为零。这些政策在不同参数区域的表现也将通过数值实验、模拟和案例研究进行经验评估，包括与其他方法的比较和最不利条件下的压力测试。如果成功的话，这项研究的结果将帮助制造企业实现库存成本的显着节省，从而提高广泛采用的按订单装配制造策略的效率。 由于这些政策对交货期长的系统特别有效，它们将减轻制造商与其海外供应商之间长时间运输延迟对库存系统的负面影响，从而导致有利于内包的成本效益计算的变化，这将有助于美国制造业的振兴。随机规划和渐近分析的结合使用也指出了一个有前途的新方法，库存管理优化，其中涉及许多臭名昭著的困难问题。