CAREER: New Algorithmic Approaches to Computationally Challenging Stochastic Supply Chain and Revenue Management Models

这个教师早期职业发展（CAREER）项目的研究目标是开发一个统一的计算和理论框架，将弥合多期随机优化模型和计算挑战性的实际大规模应用在供应链和收入管理之间的差距差距。考虑像亚马逊这样的公司，它必须管理超过4000万种不同的商品，以及时满足客户的购买需求。亚马逊每天都要做出许多决策，比如每种商品要库存多少单位，库存在哪里，以及如何将订单运送给客户。这一大型供应链的管理非常具有挑战性，尤其是因为未来客户的需求和供应是不确定的，并随着时间的推移而波动。需求预测是管理未来不确定性的最有效工具之一。然而，如何利用需求预测来设计一个有效的库存控制策略，以满足供应和需求是一个具有挑战性的问题，无论是研究人员和从业人员。另一个例子是劳动力收入管理优化，像IBM这样的公司必须随着时间的推移管理技术工人池来处理多个咨询项目，旨在选择最有利可图的项目。传统的建模工具，如动态规划是有效的，在研究这些模型的最优策略的结构特性。 然而，他们通常不？t导致有效的程序来计算最佳的甚至是好的政策的实际情况。这个研究项目旨在开发一个新的算法框架，以研究这些问题下，捕捉他们的实际方面的一般建模假设。新算法将基于几种新技术，如边际成本核算方案和成本平衡技术，并提供易于实现，但可证明接近最优的政策。一个关键的研究方法是使用理论和计算性能分析来指导新算法的开发。通过与行业合作伙伴的合作，这些算法将在真实的数据上进行测试。该提案涉及供应链和收入管理的几个广泛应用领域。如果成功的话，这项研究的结果将导致一个统一的建模和算法框架，以更广泛的角度研究这些实际问题比目前的最先进的。这将扩大这些具有挑战性的问题的理论和计算的理解。从长远来看，将更复杂的模型与概念上简单有效的算法结合起来，捕捉问题的实际方面，可能会大大提高相应供应链和其他商业环境的绩效和效率。与行业合作伙伴的合作将被用来提高这个研究项目的实际影响，并丰富学生的课堂经验。

The research objective of this Faculty Early Career Development (CAREER) project is the development of a unified computational and theoretical framework that will bridge the gap between multi-period stochastic optimization models and computationally challenging practical large-scale applications in supply-chain and revenue management. Consider a firm like Amazon that has to manage over 40 million different items to satisfy customer purchases in a timely manner. Amazon has to make many daily decisions, such as how many units of each item to stock, where to locate inventories, and how to ship orders to customers. The management of this large supply chain is very challenging especially because the future customer demands and supplies are uncertain and fluctuate over time. Demand forecasts are one of the most effective tools in managing future uncertainties. However, how to use demand forecasts to devise an effective inventory control policy that matches supply and demand is a challenging problem both for researchers and practitioners. Another example is workforce revenue management optimization, where companies like IBM have to manage pools of skilled workers over time to handle multiple consulting projects, aiming at choosing the most profitable ones. Traditional modeling tools like dynamic programming are effective in studying structural properties of the optimal policies of some of these models. However, they typically don?t lead to efficient procedures to compute optimal or even good policies for practical instances. This research project seeks to develop a new algorithmic framework to study these problems under general modeling assumptions that capture their practical aspects. The new algorithms will be based on several new techniques, such as marginal cost-accounting schemes and cost-balancing techniques, and provide simple to implement, yet provably near-optimal policies. A key research methodology is the use of theoretical and computational performance analysis to guide the development of the new algorithms. Through collaborations with industry partners the algorithms will be tested on real data.The proposal addresses several broad application domains in supply-chain and revenue management. If successful, the results of this research will lead to a unified modeling and algorithmic framework to study these practical problems in broader perspectives than the current state-of-the-art. This will expand the theoretical and computational understanding of these challenging problems. In the longer-term, the combination of more sophisticated models that capture the practical aspects of the problems together with conceptually simple and efficient algorithms is likely to lead to significant improvements in the performance and efficiencies of the respective supply chains and other business environments. Collaboration with industry partners will be used to enhance the practical impact of this research project, and to enrich the classroom experience for students.