Same-Day Delivery with Fairness

• URL: http://arxiv.org/abs/2007.09541v2
• Date: Thu, 23 Dec 2021 00:16:12 GMT
• Title: Same-Day Delivery with Fairness
• Authors: Xinwei Chen, Tong Wang, Barrett W. Thomas, Marlin W. Ulmer
• Abstract summary: In 2016, certain minority neighborhoods were excluded from receiving Amazon's same-day delivery (SDD) service. In this paper, we study the problem of offering fair SDD-service to customers. We introduce a novel transformation of learning from rates to actual services, which creates a stable and efficient learning process.
• Score: 5.904739807133708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particularly boomed during the COVID-19 pandemic. The fast growth is not without its challenge. In 2016, due to low concentrations of memberships and far distance from the depot, certain minority neighborhoods were excluded from receiving Amazon's SDD service, raising concerns about fairness. In this paper, we study the problem of offering fair SDD-service to customers. The service area is partitioned into different regions. Over the course of a day, customers request for SDD service, and the timing of requests and delivery locations are not known in advance. The dispatcher dynamically assigns vehicles to make deliveries to accepted customers before their delivery deadline. In addition to the overall service rate (utility), we maximize the minimal regional service rate across all regions (fairness). We model the problem as a multi-objective Markov decision process and develop a deep Q-learning solution approach. We introduce a novel transformation of learning from rates to actual services, which creates a stable and efficient learning process. Computational results demonstrate the effectiveness of our approach in alleviating unfairness both spatially and temporally in different customer geographies. We also show this effectiveness is valid with different depot locations, providing businesses with an opportunity to achieve better fairness from any location. Further, we consider the impact of ignoring fairness in service, and results show that our policies eventually outperform the utility-driven baseline when customers have a high expectation on service level.

Related papers

• FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation [26.52731463877256]
  In this paper, we introduce the concept of adversarial multi-armed bandit to optimize the proposed objective with explicit constraints on performance disparities. Practically, we propose a novel multi-armed bandit-based allocation FL algorithm (FedMABA) to mitigate performance unfairness among diverse clients with different data distributions.
  arXiv  Detail & Related papers  (2024-10-26T10:41:45Z)
• Fairness in Serving Large Language Models [45.81800239353461]
  This paper introduces the definition of serving fairness based on a cost function that accounts for the number of input and output tokens processed. We propose a novel scheduling algorithm, the Virtual Counter Token (VTC), a fair difference between two backlogged clients. We prove a 2x tight upper bound on the service scheduler, adhering to the requirement of work-conserving.
  arXiv  Detail & Related papers  (2023-12-31T21:15:54Z)
• Price-Discrimination Game for Distributed Resource Management in Federated Learning [3.724337025141794]
  In vanilla federated learning (FL) such as FedAvg, the parameter server (PS) and multiple distributed clients can form a typical buyer's market. This paper proposes to differentiate the pricing for services provided by different clients rather than simply providing the same service pricing for different clients.
  arXiv  Detail & Related papers  (2023-08-26T10:09:46Z)
• Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems [27.34946988130242]
  We propose a simple incentive-based fairness scheme that can be implemented online as a part of this ILP formulation. We show how these fairness incentives can be formulated for two distinct use cases for passenger groups and driver fairness.
  arXiv  Detail & Related papers  (2023-03-25T02:24:27Z)
• FL Games: A Federated Learning Framework for Distribution Shifts [71.98708418753786]
  Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. We propose FL GAMES, a game-theoretic framework for federated learning that learns causal features that are invariant across clients.
  arXiv  Detail & Related papers  (2022-10-31T22:59:03Z)
• FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning [91.74206675452888]
  We propose a novel method FedFM, which guides each client's features to match shared category-wise anchors. To achieve higher efficiency and flexibility, we propose a FedFM variant, called FedFM-Lite, where clients communicate with server with fewer synchronization times and communication bandwidth costs.
  arXiv  Detail & Related papers  (2022-10-14T08:11:34Z)
• How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts [107.72786199113183]
  We propose a novel fairness learning method termed CUrvature MAtching (CUMA) CUMA achieves robust fairness generalizable to unseen domains with unknown distributional shifts. We evaluate our method on three popular fairness datasets.
  arXiv  Detail & Related papers  (2022-07-04T02:37:50Z)
• Low-Latency Federated Learning over Wireless Channels with Differential Privacy [142.5983499872664]
  In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement.
  arXiv  Detail & Related papers  (2021-06-20T13:51:18Z)
• A Deep Reinforcement Learning Approach for the Meal Delivery Problem [1.5391321019692434]
  We consider a meal delivery service fulfilling dynamic customer requests given a set of couriers over the course of a day. We model this service as a Markov decision process and use deep reinforcement learning as the solution approach. Our results present valuable insights on both the courier assignment process and the optimal number of couriers for different order frequencies on a given day.
  arXiv  Detail & Related papers  (2021-04-24T19:01:59Z)
• Double Momentum SGD for Federated Learning [94.58442574293021]
  We propose a new SGD variant named as DOMO to improve the model performance in federated learning. One momentum buffer tracks the server update direction, while the other tracks the local update direction. We introduce a novel server momentum fusion technique to coordinate the server and local momentum SGD.
  arXiv  Detail & Related papers  (2021-02-08T02:47:24Z)

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.