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Essays on Inventory Management and Conjoint Analysis.

机译：关于库存管理和联合分析的论文。

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摘要

With recent theoretic and algorithmic advancements, modern optimization methodologies have seen a substantial expansion of modeling power, being applied to solve challenging problems in impressively diverse areas. This dissertation aims to extend the modeling frontier of optimization methodologies in two exciting fields-inventory management and conjoint analysis. Although the three essays concern distinct applications using different optimization methodologies, they share a unifying theme, which is to develop intuitive models using advanced optimization techniques to solve problems of practical relevance.;The first essay (Chapter 2) applies robust optimization to solve a single installation inventory model with non-stationary uncertain demand. A classical problem in operations research, the inventory management model could become very challenging to analyze when lost-sales dynamics, non-zero fixed ordering cost, and positive lead time are introduced. In this essay, we propose a robust cycle-based control policy based on an innovative decomposition idea to solve a family of variants of this model. The policy is simple, flexible, easily implementable and numerical experiments suggest that the policy has very promising empirical performance. The policy can be used both when the excess demand is backlogged as well as when it is lost; with non-zero fixed ordering cost, and also when lead time is non-zero. The policy decisions are computed by solving a collection of linear programs even when there is a positive fixed ordering cost. The policy also extends in a very simple manner to the joint pricing and inventory control problem.;The second essay (Chapter 3) applies sparse machine learning to model multimodal continuous heterogeneity in conjoint analysis. Consumers' heterogeneous preferences can often be represented using a multimodal continuous heterogeneity (MCH) distribution. One interpretation of MCH is that the consumer population consists of a few distinct segments, each of which contains a heterogeneous sub-population. Modeling of MCH raises considerable challenges as both across- and within-segment heterogeneity need to be accounted for. In this essay, we propose an innovative sparse learning approach for modeling MCH and apply it to conjoint analysis where adequate modeling of consumer heterogeneity is critical. The sparse learning approach models MCH via a two-stage divide-and-conquer framework, in which we first decompose the consumer population by recovering a set of candidate segmentations using structured sparsity modeling, and then use each candidate segmentation to develop a set of individual-level representations of MCH. We select the optimal individual-level representation of MCH and the corresponding optimal candidate segmentation using cross-validation. Two notable features of our approach are that it accommodates both across- and within-segment heterogeneity and endogenously imposes an adequate amount of shrinkage to recover the individual-level partworths. We empirically validate the performance of the sparse learning approach using extensive simulation experiments and two empirical conjoint data sets.;The third essay (Chapter 4) applies dynamic discrete choice models to investigate the impact of return policies on consumers' product purchase and return behavior. Return policies have been ubiquitous in the marketplace, allowing consumers to use and evaluate a product before fully committing to purchase. Despite the clear practical relevance of return policies, however, few studies have provided empirical assessments of how consumers' purchase and return decisions respond to the return policies facing them. In this essay, we propose to model consumers' purchase and return decisions using a dynamic discrete choice model with forward-looking and Bayesian learning. More specifically, we postulate that consumers' purchase and return decisions are optimal solutions for some underlying dynamic expected-utility maximization problem in which consumers learn their true evaluations of products via usage in a Bayesian manner and make purchase and return decisions to maximize their expected present value of utility, and return policies impact consumers' purchase and return decisions by entering the dynamic expected-utility maximization problem as constraints. Our proposed model provides a behaviorally plausible approach to examine the impact of return policies on consumers' purchase and return behavior.
机译：随着理论和算法的最新发展，现代优化方法已大大扩展了建模能力，可用于解决各种领域的难题。本文旨在将优化方法的建模领域扩展到两个令人兴奋的领域：库存管理和联合分析。尽管这三篇论文涉及使用不同优化方法的不同应用程序，但是它们具有一个统一的主题，即使用先进的优化技术开发直观模型来解决实际相关的问题。第一篇论文（第2章）应用鲁棒优化来解决一个问题。非平稳不确定需求的安装清单模型。在引入运销动态，非零固定订购成本和正提前期后，库存管理模型在运筹学中是一个经典问题，很难进行分析。在本文中，我们提出了一种基于创新分解思想的鲁棒的基于周期的控制策略，以解决该模型的一系列变体。该策略简单，灵活，易于实施，数值实验表明该策略具有很好的经验表现。既可以在积压过多需求时也可以在失去需求时使用该策略。固定订购成本为非零，以及交货时间为非零时。即使有固定的正定购成本，也可以通过求解线性程序集合来计算策略决策。该策略还以非常简单的方式扩展到联合定价和库存控制问题。;第二篇文章（第3章）将稀疏机器学习应用于联合分析中的多模式连续异质性模型。消费者的异质偏好通常可以使用多模式连续异质性（MCH）分布来表示。对妇幼保健的一种解释是，消费者群体由几个不同的部分组成，每个部分都包含一个异质的亚群。由于需要考虑跨部门和部门内部的异质性，MCH的建模提出了巨大的挑战。在本文中，我们提出了一种创新的稀疏学习方法来对妇幼保健进行建模，并将其应用于对消费者异质性进行充分建模至关重要的联合分析。稀疏学习方法通​​过两步分治框架对MCH进行建模，在该框架中，我们首先通过使用结构化稀疏模型恢复一组候选细分来分解消费者群体，然后使用每个候选细分来开发一组个体级别的妇幼保健代表。我们使用交叉验证选择MCH的最佳个体水平表示法和相应的最佳候选分割。我们方法的两个显着特征是，它既可以适应跨部门的差异，也可以适应部门内部的异质性，并且内生性地施加了足够的收缩量以恢复个人级别的价值。我们使用大量的模拟实验和两个经验联合数据来经验地验证稀疏学习方法的性能。第三篇文章（第4章）应用动态离散选择模型来研究退货政策对消费者产品购买和退货行为的影响。退货政策在市场上无处不在，允许消费者在完全承诺购买之前使用和评估产品。尽管退货政策具有明显的现实意义，但是，很少有研究提供经验评估来评估消费者的购买和退货决定如何应对他们面临的退货政策。在本文中，我们建议使用具有前瞻性和贝叶斯学习的动态离散选择模型对消费者的购买和退货决策建模。更具体地说，我们假设消费者的购买和退货决定是一些潜在的动态预期效用最大化问题的最佳解决方案，在这种情况下，消费者通过贝叶斯方式通过使用来学习对产品的真实评估，并做出购买和退货决定，以最大化他们的预期礼物效用价值和退货政策通过将动态的期望效用最大化问题作为约束条件来影响消费者的购买和退货决定。我们提出的模型提供了一种行为上可行的方法，以检验退货政策对消费者购买和退货行为的影响。

著录项

  • 作者

    Chen, Yupeng.;

  • 作者单位

    Columbia University.;

  • 授予单位 Columbia University.;
  • 学科 Operations Research.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 161 p.
  • 总页数 161
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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