Forecasting and Stochastic Optimization: Applications to Capacity, Inventory and Revenue Management Problems.

预测和随机优化：在容量、库存和收入管理问题中的应用。

• 批准号: RGPIN-2019-04972
• 负责人: Nagarajan, Mahesh
• 金额: $ 3.79万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764605
• 关键词: Forecasting; Stochastic; Optimization; Applications; Capacity

Inventory, capacity and revenue management are some of the fundamental areas of research and practice in operations management. The main research questions of interest in this field are about determining optimal inventory and capacity related decisions as well as polices to increase the revenue of a profit maximizing entity in a wide set of instances which are of great significance to the Canadian economy. Examples include Health care, manufacturing, retail and financial service organizations such as banks and other investment firms. The settings of the above problems have certain common themes. These include uncertainty of some kind , limited capacity, information evolving over time and the need for dynamic decisions made over time with the overall objective of maximizing/minimizing certain returns on investment (profits, costs, throughput for certain service classes etc.). Dealing with these problems naturally involve at least two mathematical tasks, i.e., forecasting of the uncertainty and optimizing a suitable objective. These two tasks are distinct, but are related to each other. Despite the prevalence of these problems, we find that repeatedly firms and decision makers resort to simple sub-optimal rules of thumb to perform the above two tasks which often result in mediocre outputs. This is costly to the economy and our society. That is, often there is ample room to improve the solutions used by practitioners. The reasons for this are manifold. First of all, often, these are extremely hard mathematical optimization problems for which optimal or near optimal solutions that are easy to implement in practice are not easily found. The forecasting problem (i.e., resolving the uncertainty) is often not an easy task. Despite the availability of large amounts of data, complex systems which are intrinsically highly non linear often yield forecasts with large errors. Often there is little theoretical or practical guidance on how much to forecast and what heuristics to use in the optimization. We find this as a common phenomenon with our work with several partner firms spanning industries such as various hospitals, agriculture, retail, and other services. This leads to the potential of deriving solutions and techniques that perform well in practice as well as have attractive theoretical properties. In the last several years, we have made some progress in this front.  Our understanding of these problems has moved forward from a theoretical sense and has also yielded solution procedures that are somewhat easy to implement and outperform existing heuristics. There is still significant potential to combine, extend and develop new techniques and theory to combine heuristics to stochastic optimization with forecasting.  Expected outcomes will be research publications in top tier research journals in my field as well as solution procedures that will be implemented in practice.

库存，能力和收入管理是运营管理研究和实践的一些基本领域。该领域感兴趣的主要研究问题是确定最佳库存和与容量相关的决策以及政策，以增加对加拿大经济具有重要意义的广泛实例中最大化实体的收入。例子包括医疗保健，制造，零售和金融服务组织，例如银行和其他投资公司。上述问题的设置具有某些共同的主题。这些包括某种形式的不确定性，有限的能力，随着时间的推移而发展的信息以及对随着时间的推移做出的动态决策的需求，其总体目标是最大程度地提高/最小化某些投资回报（利润，成本，某些服务类别的吞吐量等）。解决这些问题自然涉及至少两个数学任务，即预测不确定性并优化合适的目标。这两个任务是不同的，但彼此相关。尽管存在这些问题的普遍性，但我们发现，反复的公司和决策者诉诸于简单的亚最佳经验规则，以执行上述两个任务，这通常会导致Mediacle输出。这对经济和我们的社会来说是昂贵的。也就是说，通常有足够的空间来改善从业者使用的解决方案。原因是多种多样。首先，通常，这些是极其困难的数学优化问题，对于在实践中易于实现的最佳或接近最佳解决方案而言，这些问题不容易找到。预测问题（即解决不确定性）通常不是一件容易的事。尽管有大量数据的可用性，但本质上非线性的复杂系统通常会产生较大错误的森林。通常，关于预测多少以及在优化中使用的启发式方法几乎没有理论或实践指导。我们认为这是我们与多家合作伙伴公司的工作，例如各种医院，农业，零售和其他服务。这导致了在实践中表现良好并具有有吸引力的理论特性的解决方案和技术的潜力。在过去的几年中，我们在这方面取得了一些进展。我们对这些问题的理解已经转移了预期的结果，这将是我领域顶级研究期刊的研究出版物以及将在实践中实施的解决方案程序。