Stochastic Multiproduct Capacity and Inventory Problems: Exact Algorithms and Heuristics

随机多产品产能和库存问题：精确算法和启发式

• 批准号: RGPIN-2014-03901
• 负责人: Nagarajan, Mahesh
• 金额: $ 2.04万
• 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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587482
• 关键词: Stochastic; Multiproduct; Capacity; Inventory; Problems

Stochastic Capacity and Inventory Management is one of the fundamental areas of research and practice in operations management. The main research questions in this field deal with determining optimal inventory and capacity decisions in a wide set of instances. Answers to these are of great significance to the economy as well of theoretical interest to researchers. Organizations that face these problems span multiple areas from manufacturing, retail and health care to financial service organizations such as banks and other investment firms. There are some basic underlying conditions and themes that are common to these problems that make them both difficult and interesting to study. They include uncertainty (hence the name stochastic) of some kind (demand, supply, currency fluctuations, surgical times, patient or customer arrival to systems etc.), limited capacity (budget constraints on capital, limited manufacturing capacity due to highly non linear costs, shelf space in retail settings or limited operating room capacity in health care settings), multiple decisions often arising due to the fact multiple products or services are being managed at any time and dynamic decisions often over time periods where decisions made at one time usually affect the system in subsequent periods. The typical overall objective involves making decisions that either maximize certain returns on investment (profits, throughput for certain service classes etc.) or minimize costs and/or unpleasant incidences. Despite their prevalence, we find that repeatedly firms and decision makers resort to simple rules of thumb whose performance in practice is often mediocre. This immediately means that there is a huge potential for improvement that can have a significant benefit for society. The reasons for sub-optimal behavior in practice are manifold. First of all, these are extremely hard mathematical optimization problems for which optimal or near optimal solutions are hard to hard to envision theoretically. Therefore, usable solutions that perform well and are simple to implement in practice are not easily found. This leads to the potential of deriving solutions that perform well in practice as well as have attractive theoretical properties that show robustness of the approximate solutions. In the last several years, we have made some progress on some such difficult problems using innovative methods combined with dynamic programming. This analysis has moved our understanding of these problems forward from a theoretical sense and has also yielded solution procedures that are somewhat easy to implement and outperform existing heuristics. Many of these have been implemented by practitioners. But there is a lot left to be done. In the current research agenda, I propose to work on a broad set of these problems and make significant progress on both the theoretical and applied front. Expected outcomes will be research publications in top tier research journals in my field as well as solution procedures that will be used by industry partners which will yield positive gains to the economy at large.

随机能力和库存管理是 运营管理研究和实践。该领域的主要研究问题涉及确定广泛实例中的最佳库存和能力决策。这些答案对研究人员的理论利益以及理论上的兴趣具有重要意义。面临这些问题的组织跨越了从制造，零售和医疗保健到银行和其他投资公司等金融服务组织的多个领域。这些问题有一些基本的基本条件和主题，这些问题使它们既困难又有趣。 它们包括某种形式（需求，供应，货币波动，手术时间，患者或客户到达系统等）的不确定性（因此是随机性），容量有限（资本预算限制，有限的制造能力，高度非线性成本，由于零售业中的零售空间高度的空间，零售业中的零售空间，零售设置有限或在医疗保健中的各种决定通常是在多个决定的情况下进行的，因此在多个决策中，要在多个决定的情况下进行多个服务，因此，在多个决定的情况下，有多个服务的服务），在多个服务中，有多个服务的服务），在多个服务中，有多个服务的服务），在多个决策中，有多个服务的服务），在多个决策中，有限的服务），在多个决策中），造成多种选择，一次决定通常会在随后的时期内影响系统。典型的总体目标涉及做出最大化某些投资回报（利润，某些服务类别的吞吐量等）或最小化成本和/或不愉快事件的决策。 尽管他们的盛行，但我们发现，反复的公司和决策者诉诸于简单的经验法则，这些经验通常是平庸的。这立即意味着有很大的改进潜力可以为社会带来重大利益。在实践中，次优行为的原因是多种多样的。首先，这些是极其困难的数学优化问题，而理论上很难难以设想最佳或接近最佳的解决方案。因此，在实践中表现良好且易于实现的可用解决方案不容易找到。这导致了在实践中表现出色的解决方案以及具有有吸引力的理论特性的潜力，这些溶液表现出近似解决方案的稳健性。在过去的几年中，我们使用创新方法与动态编程相结合，在某些困难的问题上取得了一些进展。这种分析使我们对这些问题的理解从理论意义上转发了，并且还产生了解决方案程序，这些程序易于实施，并且胜过现有的启发式方法。其中许多是由从业者实施的。 但是还有很多事情要做。在当前的研究议程中，我建议研究这些问题，并在理论和应用方面取得重大进展。预期的结果将是我领域顶级研究期刊的研究出版物以及行业合作伙伴将使用的解决方案程序，这些程序将为整个经济带来积极的收益。