Real-time Dynamic Optimization for Omnichannel Retailers

该项目旨在使用数学模型和分析来识别全渠道零售中出现的动态优化问题的近最优算法。中心主题是帮助全渠道零售商了解，当订单履行（即向客户实际交付产品）成本高昂时，应如何做出各种决策（例如定价、分类和库存）。全渠道零售是指通过多渠道为顾客提供统一的购物体验的一种完全整合的方式。它正迅速成为零售业务的默认模式。为了通过多个接触点实现无缝购物体验，全渠道零售商在订单履行活动中承受了巨大的运营成本。此外，需要共同优化相关决策以实现利润最大化。这一领域日益增长的实际重要性和学术文献的稀缺性提供了令人兴奋的研究机会，这将在广泛的意义上产生直接影响。受最近全渠道零售创新的激励，我们将调查以下三个密切相关的研究问题，这些问题在文献中没有得到解决：1。延迟订单履行的价值是什么？2. 零售商如何共同优化交付选项和订单履行决策？3. 履行过程中的不灵活性如何影响零售商的定价和库存决策？我们的研究将提供理论与商业实践相结合的解决方案，并突出联合动态优化的效益和可行性。从理论的角度来看，我们的问题被建模为难以最优解决的随机控制问题。我们计划提出易于实时实现并具有接近最佳性能保证的算法。由于问题涉及以前未研究过的新动力学，该项目对随机控制和在线算法文献有广泛的贡献。五名hqp将获得运营和数据分析方面的高需求技能，这对于解决相关的现实问题以及在学术或行业职业生涯中取得成功至关重要。从实践的角度来看，该项目及时解决了全渠道零售商面临的共同挑战，这些零售商依靠复杂的物流网络来服务高服务要求的客户。我们的算法将使用真实数据进行测试，并有可能由零售领域的行业合作伙伴（例如亚马逊、甲骨文实验室和Instacart）实施。在Z世代的崛起和新冠疫情的影响下，消费者的购物习惯发生了巨大变化。研究结果将有利于更广泛的零售业接受这些变化。对加拿大零售业的潜在经济影响将是显著的，因为即使收入适度提高1%，也可能转化为超过50亿加元的额外销售额。

This project seeks to use mathematical models and analysis to identify near-optimal algorithms for dynamic optimization problems arising in omnichannel retail. The central theme is to help omnichannel retailers understand how various decisions (e.g. pricing, assortment and inventory) should be made when order fulfillment (i.e. physical delivery of products to customers) is costly. Omnichannel retail refers to a fully integrated approach where customers are provided with a unified shopping experience across multiple channels. It is fast emerging as the default mode for retail operations. To enable a seamless shopping experience with multiple touchpoints, omnichannel retailers endure significant operational costs from order fulfillment activities. Moreover, correlated decisions need to be optimized jointly to maximize profits. The increasing practical importance of this area and the sparsity of academic literature present exciting research opportunities that will be directly impactful in a broad sense. Motivated by recent innovations in omnichannel retail, we will investigate the following three closely related research questions that have not been addressed in the literature: 1. What is the value of delayed order fulfillment? 2. How can retailers jointly optimize delivery option offerings and order fulfillment decisions? 3. How do inflexibilities in the fulfillment process affect retailer's pricing and inventory decisions? Our research will provide solutions that align theory with business practices, and highlight the benefit and feasibility of joint dynamic optimization. From a theoretical perspective, our problems are modelled as stochastic control problems that are intractable to solve optimally. We plan to propose algorithms that are easily implementable in real-time and have near-optimal performance guarantees. As the problems involve novel dynamics that have not been studied before, this project broadly contributes to stochastic control and online algorithm literature. Five HQPs will gain a skillset that is highly in demand in Operations and Data Analytics that is essential to address relevant real-world problems and for success in an academic or industry career. From a practical perspective, this project timely addresses common challenges faced by omnichannel retailers, who rely on complicated logistic networks to serve customers with high service requirements. Our algorithms will be tested using real data and have the potential of being implemented by industry partners in the retail sector (e.g. Amazon, Oracle Labs, and Instacart). Driven by the rise of Generation Z and the impact of the COVID-19 pandemic, consumer shopping habits have changed drastically. The research outcomes will benefit the broader retail industry in embracing these changes. The potential economic impact on Canadian retail sectors will be significant, since even a moderate improvement of 1% in revenue could translate to over CAD 5B of additional sales.