Home delivery failures, traffic congestion, and relatively large handling
times have a negative impact on the profitability of last-mile logistics. These
external factors contribute to up to $28\%$ of the overall costs and $25\%$ of
emissions for the home delivery supply chain. A potential solution, showing
annual growth rates up to $36\%$, is the delivery to parcel lockers or parcel
shops, denoted by out-of-home (OOH) delivery. In the academic literature,
models of customer behavior with respect to OOH delivery were so far limited to
deterministic settings, contrasting with the stochastic nature of actual
customer choices. We model the sequential decision-making problem of which OOH
location to offer against what incentive for each incoming customer, taking
into account future customer arrivals and choices. We propose Dynamic Selection
and Pricing of OOH (DSPO), an algorithmic pipeline that uses a novel
spatial-temporal state encoding as input to a convolutional neural network. We
demonstrate the performance of our method by benchmarking it against three
state-of-the-art approaches. Our extensive numerical study, guided by
real-world data, reveals that DSPO can save $20.8\%$ in costs compared to a
situation without OOH locations, $8.1\%$ compared to a static selection and
pricing policy, and $4.6\%$ compared to a state-of-the-art demand management
benchmark. We provide comprehensive insights into the complex interplay between
OOH delivery dynamics and customer behavior influenced by pricing strategies.
The implications of our findings suggest practitioners to adopt dynamic
selection and pricing policies as OOH delivery gains a larger market share.

对于最后一公里物流的利润能有着直接和间接的影响，而居家送货失败、交通拥堵以及较长的处理时间都是其负面因素之一。其中，向包裹柜或包裹店的投递，即所谓的户外投递，在学术文献中对于户外投递的顾客行为模型通常只考虑了确定性的情况，但实际顾客的选择具有随机性。本研究提出了一种动态选择和定价户外投递的算法，通过对顾客未来的到达和选择进行建模，以及使用一种新颖的时空状态编码和卷积神经网络进行输入。经过在真实数据的基础上进行的大规模数值研究表明，相比没有户外投递的情况，DSPO 算法能节约 20.8% 的成本，相比于静态选择和定价策略，能节约 8.1% 的成本，并且相比于当前最先进的需求管理基准，能节约 4.6% 的成本。我们的研究提供了对户外投递动态与顾客行为及定价策略之间复杂相互作用的全面洞察，强调了随着户外投递的市场份额增加，企业应采用动态选择和定价策略。

学习动态选择和定价的户外交付

Learning Dynamic Selection and Pricing of Out-of-Home Deliveries

Understanding customer behavior in retail stores plays a crucial role in
improving customer satisfaction by adding personalized value to services.
Behavior analysis reveals both general and detailed patterns in the interaction
of customers with a store items and other people, providing store managers with
insight into customer preferences. Several solutions aim to utilize this data
by recognizing specific behaviors through statistical visualization. However,
current approaches are limited to the analysis of small customer behavior sets,
utilizing conventional methods to detect behaviors. They do not use deep
learning techniques such as deep neural networks, which are powerful methods in
the field of computer vision. Furthermore, these methods provide limited
figures when visualizing the behavioral data acquired by the system. In this
study, we propose a framework that includes three primary parts: mathematical
modeling of customer behaviors, behavior analysis using an efficient deep
learning based system, and individual and group behavior visualization. Each
module and the entire system were validated using data from actual situations
in a retail store.

理解零售店中的顾客行为对于通过增加个性化价值来提升顾客满意度起着至关重要的作用。本研究提出了一个包括数学建模、基于深度学习的行为分析系统以及个体和群体行为可视化的框架，并对整个系统进行了验证。

零售店顾客行为分析系统：设计与实施

Retail store customer behavior analysis system: Design and  Implementation

We consider a planning problem where the dynamics and rewards of the
environment depend on a hidden static parameter referred to as the context. The
objective is to learn a strategy that maximizes the accumulated reward across
all contexts. The new model, called Contextual Markov Decision Process (CMDP),
can model a customer's behavior when interacting with a website (the learner).
The customer's behavior depends on gender, age, location, device, etc. Based on
that behavior, the website objective is to determine customer characteristics,
and to optimize the interaction between them. Our work focuses on one basic
scenario--finite horizon with a small known number of possible contexts. We
suggest a family of algorithms with provable guarantees that learn the
underlying models and the latent contexts, and optimize the CMDPs. Bounds are
obtained for specific naive implementations, and extensions of the framework
are discussed, laying the ground for future research.

论文讨论了一种名为 CMDP 的新模型，可模拟顾客在与网站交互时的行为，并基于此行为决定顾客特征，优化交互。作者提出了一系列算法，可以学习潜在的模型和上下文，并优化 CMDPs。