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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=660585
• 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.

随机容量和库存管理是运营管理研究和实践的基本领域之一。该领域的主要研究问题涉及在多种情况下确定最佳库存和容量决策。这些问题的答案对经济具有重要意义，对研究人员来说也具有理论意义。面临这些问题的组织涉及多个领域，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。这些问题有一些共同的基本基本条件和主题，使得它们的研究既困难又有趣。 它们包括某种不确定性（因此称为随机）（需求、供应、货币波动、手术时间、患者或客户到达系统等）、有限的能力（资本预算限制、高度非线性成本导致的有限制造能力） 、零售环境中的货架空间或医疗保健环境中有限的手术室容量），由于随时管理多种产品或服务，经常会产生多项决策，并且经常在一段时间内做出动态决策，其中一次做出的决策通常会影响后续时期的系统。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。 **尽管它们很普遍，但我们发现公司和决策者一再诉诸简单的经验法则，但在实践中表现往往平平。这立即意味着存在巨大的改进潜力，可以为社会带来重大利益。实践中行为欠佳的原因是多方面的。首先，这些都是极其困难的数学优化问题，从理论上很难想象出最佳或接近最佳的解决方案。因此，在实践中性能良好且易于实施的可用解决方案并不容易找到。这使得导出在实践中表现良好的解决方案以及具有有吸引力的理论特性（显示近似解决方案的鲁棒性）的潜力成为可能。在过去的几年里，我们利用创新方法结合动态规划在一些此类难题上取得了一些进展。这种分析从理论意义上推动了我们对这些问题的理解，并且还产生了一些易于实施且优于现有启发式的解决方案。其中许多已被实践者实施。 但还有很多工作要做。在当前的研究议程中，我建议致力于解决一系列广泛的问题，并在理论和应用方面取得重大进展。预期成果将是在我所在领域的顶级研究期刊上发表研究论文，以及行业合作伙伴将使用的解决方案程序，这将为整个经济带来积极的收益。