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

• URL: http://arxiv.org/abs/2311.13983v3
• Date: Wed, 30 Oct 2024 13:56:21 GMT
• Title: Learning Dynamic Selection and Pricing of Out-of-Home Deliveries
• Authors: Fabian Akkerman, Peter Dieter, Martijn Mes,
• Abstract summary: 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. Our extensive numerical study, guided by real-world data, reveals that DSPO can save 19.9%pt in costs compared to a situation without OOH locations. We provide comprehensive insights into the complex interplay between OOH delivery dynamics and customer behavior influenced by pricing strategies.
• Score: 1.2289361708127877
• License:
• Abstract: Home delivery failures, traffic congestion, and relatively large handling times have a negative impact on the profitability of last-mile logistics. A potential solution is the delivery to parcel lockers or parcel shops, denoted by out-of-home (OOH) delivery. In the academic literature, models for OOH delivery were so far limited to static settings, contrasting with the sequential nature of the problem. 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 two state-of-the-art approaches. Our extensive numerical study, guided by real-world data, reveals that DSPO can save 19.9%pt in costs compared to a situation without OOH locations, 7%pt compared to a static selection and pricing policy, and 3.8%pt 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.

Related papers

• MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services [94.61039892220037]
  We present a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models for augmented reality (AR) services in the vehicular metaverse. Considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets.
  arXiv  Detail & Related papers  (2024-10-25T16:20:46Z)
• Dynamic Demand Management for Parcel Lockers [0.0]
  We develop a solution framework that orchestrates algorithmic techniques rooted in Sequential Decision Analytics and Reinforcement Learning. Our innovative approach to combine these techniques enables us to address the strong interrelations between the two decision types. Our computational study shows that our method outperforms a myopic benchmark by 13.7% and an industry-inspired policy by 12.6%.
  arXiv  Detail & Related papers  (2024-09-08T11:38:48Z)
• Dynamic Pricing for Electric Vehicle Charging [6.1003048508889535]
  We develop a novel formulation for the dynamic pricing problem by addressing multiple conflicting objectives efficiently. A dynamic pricing model quantifies the relationship between demand and price while simultaneously solving multiple conflicting objectives. Two California charging sites' real-world data validates our approach.
  arXiv  Detail & Related papers  (2024-08-26T10:32:21Z)
• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning [70.22819290458581]
  Reinforcement learning with human feedback (RLHF) is a widely adopted approach in current large language model pipelines. Our approach introduces two key innovations: (1) on-policy query to avoid OOD and imbalance issues in seed data, and (2) active learning to select the most informative data for preference queries.
  arXiv  Detail & Related papers  (2024-07-02T10:09:19Z)
• Switchable Decision: Dynamic Neural Generation Networks [98.61113699324429]
  We propose a switchable decision to accelerate inference by dynamically assigning resources for each data instance. Our method benefits from less cost during inference while keeping the same accuracy.
  arXiv  Detail & Related papers  (2024-05-07T17:44:54Z)
• LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication [11.337245234301857]
  We argue that a pay-as-you-go service mode will be suitable in this context, referred to as Large Model as a Service (LM) We propose an Iterative Model Pricing (IMP) algorithm that optimize the prices of large models iteratively by reasoning customers' future rental decisions. In the second step, we optimize customers' selection decisions by designing a robust selecting and renting algorithm.
  arXiv  Detail & Related papers  (2024-01-05T07:19:19Z)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework [68.8725783112254]
  We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
  arXiv  Detail & Related papers  (2023-02-22T15:52:37Z)
• A Deep Reinforcement Learning Approach for Constrained Online Logistics Route Assignment [4.367543599338385]
  It is crucial for the logistics industry on how to assign a candidate logistics route for each shipping parcel properly. This online route-assignment problem can be viewed as a constrained online decision-making problem. We develop a model-free DRL approach named PPO-RA, in which Proximal Policy Optimization (PPO) is improved with dedicated techniques to address the challenges for route assignment (RA)
  arXiv  Detail & Related papers  (2021-09-08T07:27:39Z)
• Online Regularization towards Always-Valid High-Dimensional Dynamic Pricing [19.11333865618553]
  We propose a novel approach for designing dynamic pricing policy based regularized online statistical learning with theoretical guarantees. Our proposed online regularization scheme equips the proposed optimistic online regularized maximum likelihood pricing (OORMLP) pricing policy with three major advantages. In theory, the proposed OORMLP algorithm exploits the sparsity structure of high-dimensional models and secures a logarithmic regret in a decision horizon.
  arXiv  Detail & Related papers  (2020-07-05T23:52:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.