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Timely delivery and optimal routing remain fundamental challenges in the modern logistics industry. Building on prior work that considers single-package delivery across networks using multiple types of collaborative agents with restricted movement areas (e.g., drones or trucks), we examine the complexity of the problem under structural and operational constraints. Our focus is on minimizing total delivery time by coordinating agents that differ in speed and movement range across a graph. This problem formulation aligns with the recently proposed Drone Delivery Problem with respect to delivery time (DDT), introduced by Erlebach et al. [ISAAC 2022]. We first resolve an open question posed by Erlebach et al. [ISAAC 2022] by showing that even when the delivery network is a path graph, DDT admits no polynomial-time approximation within any polynomially encodable factor $a(n)$, unless P=NP. Additionally, we identify the intersection graph of the agents, where nodes represent agents and edges indicate an overlap of the movement areas of two agents, as an important structural concept. For path graphs, we show that DDT becomes tractable when parameterized by the treewidth $w$ of the interse

We introduce FLUID (Fountain LiqUId Delivery), a protocol that uses fountain coding and receiver feedback for low-latency delivery of data blocks over lossy networks. Idealized Automatic Repeat reQuest (ARQ) protocols are bandwidth-optimal, but must deliver every packet in a block and therefore can require additional rounds under packet loss. FLUID uses a controlled amount of slack to relax this all-packets requirement, allowing delivery to finish once enough encoded packets have been received. This yields substantially tighter delivery latency while remaining deterministically close to the ARQ bandwidth optimum. FLUID is controlled by a slack parameter $ε$. Under the Loss-Product Rule, delivery finishes once the product of packet loss fractions across transmission rounds falls below $ε$. Thus, FLUID can finish delivery in a small number of rounds even when every round experiences packet loss, while $ε$ controls the gap between FLUID and bandwidth-optimal ARQ.

Urban last-mile parcel delivery increasingly relies on heterogeneous fleets whose performance depends on timely coordination, reliable communication, and scalable control. Truck-drone collaboration has emerged as a networked cyber-physical delivery paradigm that combines the payload capacity and range efficiency of trucks with the agility of drones in congested or access-limited urban environments. This paper proposes a layered planning and coordination framework that structures truck-drone collaborative delivery (TDCD) from a systems and control perspective. The framework consists of five interrelated layers: spatial-demand alignment, collaborative delivery configuration, resource and workflow orchestration, performance evaluation, and scalability analysis, providing a unified view of coordination, control, and system-level performance in networked delivery operations. The proposed framework is evaluated using a realistic urban last-mile delivery scenario derived from the 2021 Amazon Last Mile Routing Research Challenge dataset. The case study demonstrates how coordinated truck-drone operation, enabled by structured task orchestration and inter-agent synchronization, improves end-

In recent years, the illicit use of unmanned aerial vehicles (UAVs) for deliveries in restricted area such as prisons became a significant security challenge. While numerous studies have focused on UAV detection or localization, little attention has been given to delivery events identification. This study presents the first acoustic package delivery detection algorithm using a ground-based microphone array. The proposed method estimates both the drone's propeller speed and the delivery event using solely acoustic features. A deep neural network detects the presence of a drone and estimates the propeller's rotation speed or blade passing frequency (BPF) from a mel spectrogram. The algorithm analyzes the BPFs to identify probable delivery moments based on sudden changes before and after a specific time. Results demonstrate a mean absolute error of the blade passing frequency estimator of 16 Hz when the drone is less than 150 meters away from the microphone array. The drone presence detection estimator has a accuracy of 97%. The delivery detection algorithm correctly identifies 96% of events with a false positive rate of 8%. This study shows that deliveries can be identified using aco

Digital ads on social-media platforms play an important role in shaping access to economic opportunities. Our work proposes and implements a new third-party auditing method that can evaluate racial bias in the delivery of ads for education opportunities. Third-party auditing is important because it allows external parties to demonstrate presence or absence of bias in social-media algorithms. Education is a domain with legal protections against discrimination and concerns of racial-targeting, but bias induced by ad delivery algorithms has not been previously explored in this domain. Prior audits demonstrated discrimination in platforms' delivery of ads to users for housing and employment ads. These audit findings supported legal action that prompted Meta to change their ad-delivery algorithms to reduce bias, but only in the domains of housing, employment, and credit. In this work, we propose a new methodology that allows us to measure racial discrimination in a platform's ad delivery algorithms for education ads. We apply our method to Meta using ads for real schools and observe the results of delivery. We find evidence of racial discrimination in Meta's algorithmic delivery of ads

Integrating drones into truck delivery systems can improve customer accessibility, reduce operational costs, and increase delivery efficiency. However, drone deployment incurs costs, including procurement, maintenance, and energy consumption, and its benefits depend on service demand. In low-demand areas, drone-assisted trucks may underutilize resources due to high upfront costs. Accurately predicting demand is challenging due to uncertainties from unforeseen events or infrastructure disruptions. To address this, a market entry and exit real option approach is used to optimize switching between truck-only and drone-assisted delivery under stochastic demand. Results show that deploying multiple drones per truck offers significant cost advantages in high-demand regions. Using the proposed dynamic switching model, deterministic and stochastic approaches reduce costs by 17.4% and 31.3%, respectively, compared to immediate cost-saving switching. Sensitivity analysis reveals asymmetric effects of stochastic parameters on entry and exit timings. A stochastic multiple-options model is further developed to dynamically switch between truck-only and drone-assisted delivery with varying drone

Accurate prediction of food delivery times significantly impacts customer satisfaction, operational efficiency, and profitability in food delivery services. However, existing studies primarily utilize static historical data and often overlook dynamic, real-time contextual factors crucial for precise prediction, particularly in densely populated Indian cities. This research addresses these gaps by integrating real-time contextual variables such as traffic density, weather conditions, local events, and geospatial data (restaurant and delivery location coordinates) into predictive models. We systematically compare various machine learning algorithms, including Linear Regression, Decision Trees, Bagging, Random Forest, XGBoost, and LightGBM, on a comprehensive food delivery dataset specific to Indian urban contexts. Rigorous data preprocessing and feature selection significantly enhanced model performance. Experimental results demonstrate that the LightGBM model achieves superior predictive accuracy, with an R2 score of 0.76 and Mean Squared Error (MSE) of 20.59, outperforming traditional baseline approaches. Our study thus provides actionable insights for improving logistics strategie

Companies like Amazon and UPS are heavily invested in last-mile delivery problems. Optimizing last-delivery operations not only creates tremendous cost savings for these companies but also generate broader societal and environmental benefits in terms of better delivery service and reduced air pollutants and greenhouse gas emissions. Last-mile delivery is readily formulated as the Travelling Salesman Problem (TSP), where a salesperson must visit several cities and return to the origin with the least cost. A solution to this problem is a Hamiltonian circuit in an undirected graph. Many methods exist for solving the TSP, but they often assume the travel costs are fixed. In practice, travel costs between delivery zones are random quantities, as they are subject to variation from traffic, weather, and other factors. Innovations such as truck-drone last-mile delivery creates even more uncertainties due to scarce data. A Bayesian D-optimal experimental design in conjunction with a regression model are proposed to estimate these unknown travel costs, and subsequently search for a highly efficient solution to the TSP. This framework can naturally be extended to incorporate the use of drones

A novel power delivery framework, comprising a package-embedded inductor topology and an inductance-island methodology, is introduced to maximize both inductance and current densities in vertical power delivery (VPD). The framework leverages multiple multi-phase converters, a common strategy in high-performance computing systems, to enhance efficiency and scalability. The proposed topology employs an array of tightly coupled spiral square inductors sharing a common magnetic rod, serving multiple converters operating in the same conversion phase. The array is optimized to maximize coupling and minimize conversion losses, achieving superior inductance and current densities of 250 nH/mm^2 and 10 A/mm^2, respectively. At the system level, the inductance-island methodology partitions the power delivery network into multiple islands, each dedicated to a converter phase and supplying a portion of the load current, thereby enabling scalable and efficient distribution. To validate the framework, the inductor array is designed and simulated in ANSYS Maxwell 3D and Mechanical, exhibiting an average quality factor of 23.6 and efficiency of 97.4% at 2 A load current, 6 V input, and 10 MHz switc

Since 2018, there has been a consistent decline in the distance traveled by U.S. manufacturing imports, reaching a level not observed since 2008. This trend is the result of the substitution away from imports from China and towards imports from closer countries. At the same time, U.S. manufacturing inventory-to-sales ratio has continued to rise. These trends are at odds with the literature, which finds that reductions in the distance of imports are associated with a decline in inventories. We argue that a rise in delivery time risk, driven by longer and more frequent delays and supply disruptions, can reconcile these trends. We do so in the context of a model of global sourcing with stochastic delivery times and inventories. Firms trade off the lower price of farther inputs with the increase in exposure to demand volatility and longer delays. In response, firms increase their inventories. Yet, as delivery delays rise, firms need to carry more inventories per unit of the input used. We calibrate the model for the period from 2018 to 2024 using data on the increase in tariffs for inputs from China, and the rise in inventories over sales. We find an increase in delivery delays for for

One of the recent innovations in urban distribution is crowdsourced delivery, where deliveries are made by occasional drivers who wish to utilize their surplus resources (unused transport capacity) by making deliveries in exchange for some compensation. The potential benefits of crowdsourced delivery include reduced delivery costs and increased flexibility (by scaling delivery capacity up and down as needed). The use of occasional drivers poses new challenges because (unlike traditional couriers) neither their availability nor their behavior in accepting delivery offers is certain. The relationship between the compensation offered to occasional drivers and the probability that they will accept a task has been largely neglected in the scientific literature. Therefore, we consider a setting in which compensation-dependent acceptance probabilities are explicitly considered in the process of assigning delivery tasks to occasional drivers. We propose a mixed-integer nonlinear model that minimizes the expected delivery costs while identifying optimal assignments of tasks to a mix of professional and occasional drivers and their compensation. We propose an exact two-stage solution algorit

This study focuses on order dispatch decisions within two-echelon supply chains, where order dispatch creates economic shipments to reduce delivery costs. Dispatching orders is often constrained by delivery windows, leading to penalty costs for untimely deliveries. Prolonged dispatch times can increase the lead time of orders and potentially violate these delivery windows. To balance the trade-offs between lead time and economic delivery, this study introduces a simulation-optimization approach for determining optimal ordering and dispatch rules. It emphasizes the intricacies of the order dispatch process and explores how these can be integrated into the simulation-optimization procedure to improve ordering and delivery decisions. The study evaluates various options for implementing dispatch rules, including the number of dispatch queues and prioritized dispatch. The results indicate that a single-queue, quantity-based, first-in-first-out dispatch approach achieves the greatest cost reduction while maintaining a desirable service level.

We report the discovery of channel fracture, a silent architectural failure in multi-agent systems where information routed across agent boundaries is silently blocked by invisible constraints. We present three instances in a production Hermes Agent deployment: (1) cron memory injection blocked by scheduler barriers; (2) cross-profile skill routing fractured by recursive directory traversal; (3) WebSocket delivery confirmation fallback fracture causing message duplication. We propose CADVP v1.1, a 13-dimension verification protocol with a veto-level confirmation check. Through 30,012 trials, zero failure rates under protocol versus 69 to 98 percent without. Real-world validation (10,008 trials) confirms quality elevation from 0.90 to 1.00. Three design principles: inverse verification, channel matching, and PIP protection.

Low Earth Orbit (LEO) satellite ISPs promise universal Internet connectivity, yet their interaction with content delivery remains poorly understood. We present the first comprehensive measurement study decomposing Starlink's web content delivery performance decomposed across Point of Presence (PoP), DNS, and CDN layers. Through two years of measurements combining 225K Cloudflare AIM tests, M-Lab data, and active probing from 99 RIPE Atlas and controlled Starlink probes, we collect 6.1M traceroutes and 10.8M DNS queries to quantify how satellite architecture disrupts terrestrial CDN assumptions. We identify three distinct performance regimes based on infrastructure density. Regions with local content-rich PoPs achieve near-terrestrial latencies with the satellite segment dominating 80-90% of RTT. Infrastructure-sparse regions suffer cascading penalties: remote PoPs force distant resolver selection, which triggers CDN mis-localization, pushing latencies beyond 200 ms. Dense-infrastructure regions show minimal sensitivity to PoP changes. Leveraging Starlink's infrastructure expansion in early 2025 as a natural experiment, we demonstrate that relocating PoPs closer to user location red

The coordination among drones and ground vehicles for last-mile delivery has gained significant interest in recent years. In this paper, we study \textit{multiple drone delivery scheduling problem(MDSP) \cite{Betti_ICDCN22} for last-mile delivery, where we have a set of drones with an identical battery budget and a set of delivery locations, along with reward or profit for delivery, cost and delivery time intervals. The objective of the MDSP is to find a collection of conflict-free schedules for each drone such that the total profit for delivery is maximum subject to the battery constraint of the drones. Here we propose a fully polynomial time approximation scheme (FPTAS) for the single drone delivery scheduling problem (SDSP) and a $\frac{1}{4}$-approximation algorithm for MDSP with a constraint on the number of drones.

Many e-commerce marketplaces offer their users fast delivery options for free to meet the increasing needs of users, imposing an excessive burden on city logistics. Therefore, understanding e-commerce users' preference for delivery options is a key to designing logistics policies. To this end, this study designs a stated choice survey in which respondents are faced with choice tasks among different delivery options and time slots, which was completed by 4,062 users from the three major metropolitan areas in Japan. To analyze the data, mixed logit models capturing taste heterogeneity as well as flexible substitution patterns have been estimated. The model estimation results indicate that delivery attributes including fee, time, and time slot size are significant determinants of the delivery option choices. Associations between users' preferences and socio-demographic characteristics, such as age, gender, teleworking frequency and the presence of a delivery box, were also suggested. Moreover, we analyzed two willingness-to-pay measures for delivery, namely, the value of delivery time savings (VODT) and the value of time slot shortening (VOTS), and applied a non-semiparametric approac

We introduce and study a new cooperative delivery problem inspired by drone-assisted package delivery. We consider a scenario where a drone, en route to deliver a package to a destination (a point on the plane), unexpectedly loses communication with its central command station. The command station cannot know whether the drone's system has wholly malfunctioned or merely experienced a communications failure. Consequently, a second, helper drone must be deployed to retrieve the package to ensure successful delivery. The central question of this study is to find the optimal trajectory for this second drone. We demonstrate that the optimal solution relies heavily on the relative spatial positioning of the command station, the destination point, and the last known location of the disconnected drone.

Peer-to-peer (p2p) content delivery is promising to provide benefits like cost-saving and scalable peak-demand handling in comparison with conventional content delivery networks (CDNs) and complement the decentralized storage networks such as Filecoin. However, reliable p2p delivery requires proper enforcement of delivery fairness, i.e., the deliverers should be rewarded according to their in-time delivery. Unfortunately, most existing studies on delivery fairness are based on non-cooperative game-theoretic assumptions that are arguably unrealistic in the ad-hoc p2p setting. We for the first time put forth the expressive yet still minimalist securities for p2p content delivery, and give two efficient solutions FairDownload and FairStream via the blockchain for p2p downloading and p2p streaming scenarios, respectively. Our designs not only guarantee delivery fairness to ensure deliverers be paid (nearly) proportional to his in-time delivery, but also ensure the content consumers and content providers to be fairly treated. The fairness of each party can be guaranteed when the other two parties collude to arbitrarily misbehave. Moreover, the systems are efficient in the sense of attai

As autonomous driving technology is getting more and more mature today, autonomous delivery companies like Starship, Marble, and Nuro has been making progress in the tests of their autonomous delivery robots. While simulations and simulators are very important for the final product landing of the autonomous delivery robots since the autonomous delivery robots need to navigate on the sidewalk, campus, and other urban scenarios, where the simulations can avoid real damage to pedestrians and properties in the real world caused by any algorithm failures and programming errors and thus accelerate the whole developing procedure and cut down the cost. In this case, this study proposes an open-source simulator based on our autonomous delivery robot ZebraT to accelerate the research on autonomous delivery. The simulator developing procedure is illustrated step by step. What is more, the applications on the simulator that we are working on are also introduced, which includes autonomous navigation in the simulated urban environment, cooperation between an autonomous vehicle and an autonomous delivery robot, and reinforcement learning practice on the task training in the simulator. We have pub