Delivery Requests Research Articles

Capacity and delay performance analysis for large-scale UAV-enabled wireless networks

In this paper, we analyze the capacity and delay performance of a large-scale Unmanned Aerial Vehicle (UAV)-enabled wireless network, in which untethered and tethered UAVs deployed with content files move along with mobile Ground Users (GUs) to satisfy their coverage and content delivery requests. We consider the case where the untethered UAVs are of limited storage, while the tethered UAVs serve as the cloud when the GUs cannot obtain the required files from the untethered UAVs. We adopt the Ornstein-Uhlenbeck (OU) process to capture the mobility pattern of the UAVs moving along the GUs and derive the compact expressions of the coverage probability and capacity of our considered network. Using tools from martingale theory, we model the traffic at UAVs as a two-tier queueing system. Based on this modeling, we further derive the analytical expressions of the network backlog and delay bounds. Through numerical results, we verify the correctness of our analysis and demonstrate how the capacity and delay performance can be significantly improved by optimizing the percentage of the untethered UAVs with cached contents.

Heatmap Design for Probabilistic Driver Repositioning in Crowdsourced Delivery

This paper proposes the use of heatmaps as a control lever to manage the probabilistic repositioning of independent drivers in crowdsourced delivery platforms. The platform aims to maximize order fulfillment by dynamically matching drivers and orders and selecting heatmaps that trigger the probabilistic flow of unmatched drivers to balance driver supply and delivery requests across the service region. We develop a Markov decision process (MDP) model to sequentially select matching and heatmap decisions in which the repositioning behavior of drivers is captured by a multinomial logit discrete choice model. Because of the curse of dimensionality and the endogenous uncertainty of driver repositioning, the MDP model is solved using a rolling-horizon stochastic lookahead policy. This policy decomposes matching and heatmap decisions into two optimization problems: a two-stage stochastic programming upper bounding problem for matching decisions and a mixed-integer programming problem for heatmap decisions. We also propose a simple policy for efficiently solving large-scale problems. An extensive computational study on instances derived from the Chicago ride-hailing data set is conducted. Computational experiments demonstrate the value of heatmaps in improving order fulfillment beyond the level achieved by matching alone (up to 25%) and identify conditions that affect the benefit of using heatmaps to guide driver repositioning. Funding: The authors gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada through Discovery Grants [Grants RGPIN-2024-04881, RGPIN-2020-04498, and RGPIN-2019-06207] awarded to the first, second, and third authors, respectively. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0418 .

A Bilevel Approach for Compensation and Routing Decisions in Last-Mile Delivery

In last-mile delivery logistics, peer-to-peer logistic platforms play an important role in connecting senders, customers, and independent carriers to fulfill delivery requests. As the carriers are not under the platform’s control, the platform has to anticipate their reactions while deciding how to allocate the delivery operations. Indeed, carriers’ decisions largely affect the platform’s revenue. In this paper, we model this problem using bilevel programming. At the upper level, the platform decides how to assign the orders to the carriers; at the lower level, each carrier solves a profitable tour problem to determine which offered requests to accept, based on her own profit maximization. Possibly, the platform can influence carriers’ decisions by determining also the compensation paid for each accepted request. The two considered settings result in two different formulations: the bilevel profitable tour problem with fixed compensation margins and with margin decisions, respectively. For each of them, we propose single-level reformulations and alternative formulations where the lower-level routing variables are projected out. A branch-and-cut algorithm is proposed to solve the bilevel models, with a tailored warm-start heuristic used to speed up the solution process. Extensive computational tests are performed to compare the proposed formulations and analyze solution characteristics. Funding: The research of E. Fernandez was partially funded through the Spanish Ministerio de Ciencia y Tecnología and European Regional Development Funds (ERDF) [Grant MTM2019-105824GB-I00]. The research of C. Archetti, M. Cerulli, and I. Ljubić was partially funded by CY Initiative of Excellence, France [Grant “Investissements d’Avenir ANR-16-IDEX-0008”]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0129 .

An intelligent open trading system for on-demand delivery facilitated by deep Q network based reinforcement learning

On-demand delivery in urban areas has been growing rapidly in recent years. Nevertheless, on-demand delivery networks lack an efficient, sustainable, and environmentally friendly operative strategy. An open trading system equipped with on-line auctions provides an opportunity for increasing the efficiency of on-demand delivery systems. Reinforcement learning techniques that automate decision-making can facilitate the implementation of such complex and dynamic systems. This paper presents an on-line auction-based request trading platform embedded within an open trading system as a new scheme for carriers and shippers to trade on-demand delivery requests. The system is developed based on a multi-agent model, composed of carriers, shippers, and the on-line platform as autonomous agents. Deep Q network enabled reinforcement learning is used in the decision-making processes for the agents to optimise their behaviour in a dynamic environment. Numerical experiments conducted on the Melbourne metropolitan network demonstrate the effectiveness of the open trading system, which can provide benefits for all stakeholders involved in the on-demand delivery market as well as the entire system. The reinforcement learning enabled platform can gain more profit when there are more learning carriers. The results indicate that the intelligent open trading system with on-line auctions is a promising city logistics solution.

An Iterative Sample Scenario Approach for the Dynamic Dispatch Waves Problem

A challenge in same-day delivery operations is that delivery requests are typically not known beforehand, but are instead revealed dynamically during the day. This uncertainty introduces a trade-off between dispatching vehicles to serve requests as soon as they are revealed to ensure timely delivery and delaying the dispatching decision to consolidate routing decisions with future, currently unknown requests. In this paper, we study the dynamic dispatch waves problem, a same-day delivery problem in which vehicles are dispatched at fixed decision moments. At each decision moment, the system operator must decide which of the known requests to dispatch and how to route these dispatched requests. The operator’s goal is to minimize the total routing cost while ensuring that all requests are served on time. We propose iterative conditional dispatch (ICD), an iterative solution construction procedure based on a sample scenario approach. ICD iteratively solves sample scenarios to classify requests to be dispatched, postponed, or undecided. The set of undecided requests shrinks in each iteration until a final dispatching decision is made in the last iteration. We develop two variants of ICD: one variant based on thresholds, and another variant based on similarity. A significant strength of ICD is that it is conceptually simple and easy to implement. This simplicity does not harm performance: through rigorous numerical experiments, we show that both variants efficiently navigate the large state and action spaces of the dynamic dispatch waves problem and quickly converge to a high-quality solution. Finally, we demonstrate that the threshold-based ICD variant achieves excellent results on instances from the EURO Meets NeurIPS 2022 Vehicle Routing Competition, nearly matching the performance of the winning machine learning–based strategy. History: This paper has been accepted for the Transportation Science Special Issue on DIMACS Implementation Challenge: Vehicle Routing Problems. Funding: This work was supported by TKI Dinalog, Topsector Logistics, and the Dutch Ministry of Economic Affairs and Climate Policy. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0111 .

A cooperative vehicle routing problem with delivery options for simultaneous pickup and delivery services in rural areas

The booming e-commerce is challenging express companies by requiring higher logistics service efficiency in rural China. This study investigates a cooperative vehicle routing problem for simultaneous pickup and delivery requests, in which the delivery orders can be fulfilled either by home delivery or customer pickup. The delivery option for each order is complex due to the optional cooperative relationships among express companies, the inter-dependence between self-pickup site selection and the overall online vehicle routing scheme, and the distance-dependent compensation to self-pickup customers. The problem is formulated as a mixed integer linear programming model and solved by a tailored adaptive large neighborhood search algorithm. A cost-saving allocation mechanism based on the core guaranteed Shapley value is applied to carrier collaboration. The numerical results show that the newly introduced cooperative distribution system can significantly reduce operational costs. Such improvements are robust even if the total demand changes or under different rural logistics network topologies.

Real-Time Insertion Operator for Shared Mobility on Time-Dependent Road Networks

One of the most important challenges in shared mobility services ( e.g. , ride-sharing and parcel delivery) is planning routes for workers by considering real road conditions. To tackle this challenge, the "insertion operator", which computes the optimal route for the worker to serve ( i.e. , insert) the newly appeared delivery request, has been acted as the fundamental operation in existing solutions. However, existing works implicitly assume a static road network, hence are hard to fulfill the real-world scenario, where travel time between two locations is not constant at different times of a day. By contrast, we focus on the insertion operator over time-dependent road networks that capture the periodic pattern of road conditions. We also show that the time complexity of existing solutions would degrade into cubic time and hence such solutions can no longer satisfy the real-time requirement under this real-world setting. To satisfy the need for real-time computation, we propose a data summary to model the time-dependent travel time functions between pairs of vertices in the route. Based on the data summary, we design an efficient solution that can enumerate the best insertion position in linear time while satisfying complex spatiotemporal constraints. Finally, extensive experiments are conducted on real datasets from several applications of shared mobility. The results show that our solution is up to 44.5X faster than the state-of-the-art solution.

Integrated location and routing for cold chain logistics networks with heterogeneous customer demand

The critical interdependence between facility location and vehicle routing is a fundamental component of cold chain logistics management (CCLM). Furthermore, integrating information within CCLM has the potential to enhance operational efficiency, reduce costs, improve risk management, and elevate product quality, ultimately ensuring that temperature-sensitive goods are delivered in best condition. This paper introduces a novel non-linear multi-objective model designed to concurrently optimize warehouse facility location and vehicle routing, addressing the challenges inherent in cold chain logistics processes. The model seeks to minimize the aggregate costs related to transportation, facility location, and delivery tardiness. The study accounts for several pragmatic assumptions to address real-world scenarios: multiple delivery requests per customer, handling mixed commodities, and distributing mixed commodities using a single vehicle. This paper firstly studies simultaneous pickup and delivery with multiple requests and heterogeneous customer demands, each of which should be preserved in a different range of temperatures and needs different vehicle types. The epsilon-constraint method is employed to validate the proposed model, and a set of advanced, hybrid multi-objective evolutionary algorithms (MOEA) are presented to tackle the problem in a real-world context. A comprehensive set of performance metrics is utilized supported by rigorous statistical testing.

Coordinated logistics with trucks and drones for premium delivery

Last-mile delivery, the final stage of the delivery process before a package arrives at a customer's address, has emerged as an important business opportunity for inland transportation. As people's perception of last-mile delivery has changed, more customers are using premium delivery services to get the delivery at a specific time rather than just pursuing free shipping. In order to satisfy the new trend of logistics, we propose a delivery system in which several drones and trucks work together to provide service to customers within given time windows. In addition, pair constraints, which considered a truck and a drone as a pair, are relaxed to determine more flexible delivery plans. A three-stage savings-based heuristic procedure was also developed based on the concept of savings to determine the operations of trucks and drones in real-world settings. We found that the drone efficiently responds to urgent delivery requests from customers and that a delivery system that utilises both trucks and drones provides substantial benefits.

Multi-Agent Reinforcement Learning for Online Food Delivery with Location Privacy Preservation

Online food delivery services today are considered an essential service that gets significant attention worldwide. Many companies and individuals are involved in this field as it offers good income and numerous jobs to the community. In this research, we consider the problem of online food delivery services and how we can increase the number of received orders by couriers and thereby increase their income. Multi-agent reinforcement learning (MARL) is employed to guide the couriers to areas with high demand for food delivery requests. A map of the city is divided into small grids, and each grid represents a small area of the city that has different demand for online food delivery orders. The MARL agent trains and learns which grid has the highest demand and then selects it. Thus, couriers can get more food delivery orders and thereby increase long-term income. While increasing the number of received orders is important, protecting customer location is also essential. Therefore, the Protect User Location Method (PULM) is proposed in this research in order to protect customer location information. The PULM injects differential privacy (DP) Laplace noise based on two parameters: city area size and customer frequency of online food delivery orders. We use two datasets—Shenzhen, China, and Iowa, USA—to demonstrate the results of our experiments. The results show an increase in the number of received orders in the Shenzhen and Iowa City datasets. We also show the similarity and data utility of courier trajectories after we use our obfuscation (PULM) method.

A Truthful Combinatorial Reverse Auction Mechanism for Crowdshipping

Crowdshipping is an emerging logistics paradigm that leverages excess capacities of occasional couriers to transport goods. This paper investigates the problem that a crowdshipping platform recruits occasional couriers to serve the parcel pickup and delivery requests. A combinatorial reverse auction framework is introduced to model the interactions between the platform and couriers. We design an auction mechanism that implements in polynomial time, which satisfies the computational requirements of crowdshipping. It can also achieve near-optimal social cost with the approximation ratio rigidly proved. Through both rigorous theoretical analyses and extensive simulations, we demonstrate that the proposed auction mechanism is truthful, individual rational and has high computation efficiency.

Operational planning of integrated urban freight logistics combining passenger and freight flows through mathematical programming

Recently, more environmentally friendly urban logistics (UL) services have emerged based on the integration of freight deliveries into passenger bus networks to perform UL activities within cities. The aim is to reduce the number of combustion powered vehicles operating within cities, thus improving the city quality of life in terms of pollution, noise, traffic congestion etc. This paper addresses the operational planning of an UL service where freight is dropped by clients at bus hubs located outside the city center, transported by buses to one of their stops located in the city center, and delivered to the destination address by a last mile operator (LMO). To support the operational planning of the service covering the entire logistics process (from the reception of freight delivery requests until the delivery of the requests on their destination), five operational objectives are considered and, for each objective, an Integer Linear Programming (ILP) model is proposed. The objectives cover the perspectives of the bus network operator and of the LMO and some objectives address the robustness of the operational planning solutions to failures. Additionally, five operational planning cases of practical interest where two of the previous objectives are lexicographically optimized are also addressed including a description of how they are solved with the proposed ILP models. We demonstrate the merits of the different operational planning methods with different generated instances whose characteristics allow the assessment of the impact of different parameters on the results obtained by the proposed models when solved with a standard solver.

User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality

In this paper, we present a novel content caching and delivery approach for mobile virtual reality (VR) video streaming. The proposed approach aims to maximize VR video streaming performance, i.e., minimizing video frame missing rate, by proactively caching popular VR video chunks and adaptively scheduling computing resources at an edge server based on user and network dynamics. First, we design a scalable content placement scheme for deciding which video chunks to cache at the edge server based on tradeoffs between computing and caching resource consumption. Second, we propose a machine learning-assisted VR video delivery scheme, which allocates computing resources at the edge server to satisfy video delivery requests from multiple VR headsets. A Whittle index-based method is adopted to reduce the video frame missing rate by identifying network and user dynamics with low signaling overhead. Simulation results demonstrate that the proposed approach can significantly improve VR video streaming performance over conventional caching and computing resource scheduling strategies.

Crowd-shipping as a Service: Game-based operating strategy design and analysis

Crowd-shipping as a Service (CSaaS), a novel concept proposed in this study for the Online-to-Offline (O2O) market, integrates different kinds of shipping services provided by individuals and public transport (PT) operators and enables booking and payment through a single CSaaS platform. It largely increases the shipping capacities and provides a flexible shipping mode for consignees with parcel delivery requests. Focusing on the role of the CSaaS platform, we design the commission-based and integrator-based operating strategies in the presence of CSaaS, where the CSaaS platform acts as an intermediary and a reseller, respectively. To explicitly depict the interactive relations among O2O players, i.e., the CSaaS platform, the individual-based crowd-shipping (I-CS) platform, the PT operator, the conventional logistics (CL) platform, and consignees, we adopt a game-theoretic approach to formulate the business models with and without CSaaS and derive the optimal behaviors of these players, i.e., the price decisions of platforms/operators and the choice decisions of consignees regarding which shipping mode (i.e., the I-CS, CSaaS, and CL modes) to choose. Analytical results show that the commission-based operating strategy outperforms the integrator-based strategy with a larger CSaaS platform's profit. In addition, the impact of CSaaS on O2O players is also identified by comparing the results of two business models, which is found to be closely related to the urban structure regarding PT accessibility. Managerial implications concerning launching the CSaaS service are proposed at last.

A multi-visit flexible-docking vehicle routing problem with drones for simultaneous pickup and delivery services

This study investigates a multi-visit flexible-docking vehicle routing problem that uses a truck and drone fleet to fulfill pickup and delivery requests in rural areas. In this collaborative truck–drone system, each drone may serve multiple customers per trip (multi-visit services), dock to the same or different truck from where it launched (flexible docking), and perform simultaneous pickup and delivery. These characteristics complicate the temporal, spatial, and loading synchronization for trucks and drones, making the decisions of order allocation and vehicle routing highly interdependent and intractable. This problem is formulated as a mixed-integer linear programming model and solved by a tailored adaptive large neighborhood search metaheuristic. Numerical experiments are conducted on sparse rural networks to demonstrate the efficiency of the proposed method. We observe that the proposed truck–drone system shows an average cost saving of 34% compared to the truck-only case. Moreover, deep insights into the impacts of multi-visit services, flexible docking, and simultaneous pickup and delivery on the performance of the truck–drone system are discussed.

How much is too much? Medical students’ perceptions of evaluation and research requests, and suggestions for practice

A combination of institutional requirements and research activity means health profession students receive many requests to complete evaluations or participate in research. This study aimed to understand pre-clinical medical students’ perceptions and attitudes towards these requests. A prospective audit of evaluation and research requests to students during 2022 was undertaken to identify the volume and frequency of requests, and inform survey development. The online survey included questions about request frequency, volume, and timing. Two student cohorts received 42 and 34 evaluation plus 8 and 10 research requests, respectively. Responses (n = 167, aggregated response rate 28%) showed 70% felt they received too many evaluation requests, 76% indicated evaluation request volume should be limited, and 30% indicated receiving evaluation requests was ‘a little stressful’. Students indicated reasonable evaluation and research request frequency was 1–2 per month, with yearly maximums of 10–19 evaluation and 10 research requests. Students preferred receiving requests at the start of semesters; examination and holiday times were least preferred. Findings indicate students feel over-evaluated with the current evaluation schedule; students suggested the Medical School should regulate requests. The current research schedule was viewed as reasonable. Outcomes may guide institutional practice around request delivery.

Creating a “no cost” automated database tool for managing ILL requests

Winner of the 2022 HLA/MedicalDirector Digital Health Innovation Award, ANZCA’s ILLs MANAGER database was developed as a way of processing an increasing number of document delivery requests whilst at the same time automating many of the associated manual processes.

Modelling shared logistics resources in loosely coupled software architectures

This research is part of the City Line project, a collaborative initiative within Belgium aiming to promote collaboration between various logistics operators, from cargo bikes to trucks, while ensuring low emissions for last-mile deliveries in cities. The objective is to integrate all relevant information regarding shared assets into a digital collaborative logistics platform managed by a third-party logistics provider acting as a central decision-maker coordinating transportation activities. First, we describe how the digital platform receives the services offered by the logistics operators and the delivery requests. Then, we propose an incremental method to assign the transport demand to the best route according to environmental and economic factors.

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content's early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.

Impacts of the COVID-19 pandemic on interlibrary loan and document delivery requests

Impacts of the COVID-19 pandemic on interlibrary loan and document delivery requests