Delivery Tasks Research Articles

Multi-task dispatch of shared autonomous electric vehicles for Mobility-on-Demand services – combination of deep reinforcement learning and combinatorial optimization method

The Autonomous Mobility-on-Demand system is an emerging green and sustainable transportation system providing on-demand mobility services for urban residents. To achieve the best recharging, delivering, and repositioning task assignment decision-making process for shared autonomous electric vehicles, this paper formulates the fleet dynamic operating process into a multi-agent multi-task dynamic dispatching problem based on Markov Decision Process. Specifically, the decision-making process at each time step is divided into 3 sub-processes, among which recharging and delivery task assignment processes are transformed into a maximum weight matching problem of bipartite graph respectively, and the repositioning task assignment process is quantified as a maximum flow problem. Kuhn-Munkres Algorithm and Edmond-Karp Algorithm are adopted to solve the above two mathematical problems to achieve the optimal task allocation policy. To further improve the dispatching performance, a new instant reward function balancing order income with trip satisfaction is designed, and a state-value function estimated by Back Propagation-Deep Neural Network is defined as a matching degree between each shared autonomous electric vehicle and each delivery task. The numerical results show that: (i) a reward function focusing on income and satisfaction can increase total revenue by 33.2%, (ii) the introduction of task allocation repositioning increases total revenue by 50.0%, (iii) a re-estimated state value function leads to a 2.8% increase in total revenue, (iv) the combination of charging and task repositioning can reduce user waiting time and significantly improve user satisfaction with the trip.

A data-driven compensation scheme for last-mile delivery with crowdsourcing

A recent relevant innovation in last-mile delivery is to consider the possibility of goods being delivered by couriers appointed through crowdsourcing. In this paper we focus on the setting of in-store customers delivering goods, ordered by online customers, on their way home. We assume that not all the proposed delivery tasks will necessarily be accepted, and use logistic regression to model the crowd agents’ willingness to undertake a delivery. This model is then used to build a novel compensation scheme that determines reward values, based on the current plan for the professional fleet’s routes and on the couriers’ probabilities of acceptance, by employing a direct search algorithm that seeks to minimise the expected cost.

Hybrid Task Allocation of an AGV System for Task Groups of an Assembly Line

An AGV system can be used to transport different-size materials in an assembly line. The hybrid task allocation problem is involved in the assembly line, where both single-AGV tasks and multi-AGV tasks exist. However, there is little research on this problem. The goal of solving this problem is to obtain a task allocation scheme with minimum idle time and maximum system throughput. Since all necessary materials must be delivered to the assembly station before the operation can start, the delivery tasks are not independent of each other in a task group serving the operation. To solve the problem above, a hybrid task allocation method based on a task binding strategy and an improved particle swarm optimization (IPSO) is proposed. Firstly, a mathematical model considering the punctuality of material delivery and the cooperative relationship between tasks is established. Secondly, a task binding strategy and four heuristic rules are devised to improve the quality of randomly- and heuristic-generated individuals in the initial population for model optimization. Thirdly, an IPSO is developed to help the optimization algorithm jump out of local optimums. Finally, a simulation is performed to verify the effectiveness of the proposed methods. The simulation results show that a better scheme can be obtained by our hybrid task allocation method, compared to conventional Genetic Algorithms and PSO algorithms.

Resilient multi-UAS coordination using cooperative localization

Over the last decade, Multi-UAS system (MUS) have found a variety of applications such as surveillance, data acquisition, search and rescue, and delivery tasks in various environments. However, for reliable operation of MUS, it is critical to provide safety guarantee conditions assuring obstacle collision avoidance, as well as, inter-UAS collision avoidance. In this work, we apply the principles of continuum mechanics to develop a physics-inspired algorithm to ascertain safe and resilient operation of a MUS in the presence of disturbances and unforeseen UAS failure(s). In particular, the proposed approach consists of two modes: (i) Homogeneous Deformation Mode (HDM), and (ii) Failure Resilient Mode (FRM). We formally specify transitions between HDM and FRM using Cooperative Localization (CL) approach to quantify UAS tracking error and detect anomalous conditions due to UAS failure.

Vehicle and UAV Collaborative Delivery Path Optimization Model

In the context of frequent public emergencies, emergency logistics distribution is particularly critical, and because of the unique advantages of unmanned aerial vehicles (UAVs), the model of coordinated delivery of vehicles and UAVs is gradually becoming an essential form of emergency logistics distribution. However, the omission of start-up costs prevents the cost of UAV battery replacement and the sorting, assembly and verification of packages from being factored into the total cost. Furthermore, most existing models focus on route optimization and delivery cost, which cannot fully reflect the customer’s desire for service satisfaction under emergency conditions. It is necessary to convert the unsatisfactory degree of time window into a penalty cost rather than a model constraint. Additionally, there is a lack of analysis on the mutual waiting cost between vehicles and UAVs when one of them is performing delivery tasks. Considering the effects of the time window, customer demand, maximum load capacity, and duration of distribution benefits, we propose a collaborative delivery path optimization model for vehicles and UAVs to minimize the total distribution cost. A genetic algorithm is used to obtain the model solution under the constraints of distribution subloops, distribution order, and take-off and landing nodes. To assess the efficacy of the vehicle and UAV collaborative delivery path optimization model, this paper employs a county-level district in Xi’an city as a pilot area for an emergency delivery. Compared with the vehicle-alone delivery model, the UAV-alone delivery model and vehicle-UAV collaborative delivery model, this model can significantly reduce the utilization of distribution vehicles while also significantly lowering the start-up cost, waiting cost and penalty cost. Thus, the model can effectively improve delivery timeliness and customer satisfaction. The total cost of this model is 39.2% less than that of the vehicle-alone delivery model and 16.5% less than that of the UAV-alone delivery model. Although its delivery cost is slightly higher than the vehicle-UAV collaborative delivery model, the reduction in the start-up cost and penalty cost decrease the overall cost of distribution by 11.8%. This suggests that to cut costs of all sizes and conserve half of the resources used by vehicles, employing the vehicle-UAV collaborative delivery model for emergency distribution is preferable. Moreover, the model integrating the start-up cost, penalty cost, waiting cost, etc., can more effectively express the requirements of timeliness for UAV delivery under emergency conditions.

Magnetic Navigation of Collective Cell Microrobots in Blood Under Ultrasound Doppler Imaging

We propose a strategy for magnetic navigation of collective stem cell microrobots in blood environments under ultrasound Doppler imaging. The cell microrobots are fabricated through a coculture process of stem cells and iron microparticles, they have spheroidal structures and are actuated under external magnetic fields. The collective cell microrobots can be reversibly gathered and spread by adjusting the magnetic interaction, and these microrobots are able to exhibit collective motion in whole blood under rotating magnetic fields. Simulation results indicate that the induced blood flow around the collective pattern affects the motion of red blood cells, and experimental results show that Doppler signals are observed when emitting ultrasound waves to the microrobots. Due to the induced three-dimensional blood flow, Doppler signals can also be observed when the imaging plane is above the collective microrobots, which enables indirect localization when performing navigation on uneven surfaces. Moreover, collective patterns of different numbers of microrobots are formed in flowing conditions, and these collective biohybrid agents are actively navigated against flowing blood with a flow rate up to 4.5 mL/min (mean velocity: 5.97 mm/s). Our study investigates a strategy for pattern formation and navigation of collective microrobots under ultrasound Doppler imaging, demonstrating the integration of collective control approach and medical imaging holds great potential for real-time active delivery tasks.

ENERGY EFFICIENCY OPTIMIZATION OF LAST MILE SUPPLY SYSTEM WITH REVERSE LOGISTICS CONSIDERATION

Last mile supply system takes great importance in the designed supply chain management, especially in the big urban areas, where various goods delivery locations should be tackled. Transportation routes and vehicles play a critical share in the optimization of the energy spent in this system because it is considered a complicated case due to its high solutions possibilities. Also, part of these transport processes is considered reverse logistics, where the goods take the way back, starting from the customer. Using a metaheuristic optimization is usually a good way to increase operations efficiency and save time and energy, next to raising sustainability. Within this paper, the last mile supply system within urban areas focusing on the goods' delivery and collection tasks is presented. The model design is described, mathematical optimization modelling is detailed, and a case study to investigate the impact of using diesel and electric trucks on energy efficiency is solved. After an introduction and theoretical background that includes a brief literature review, the designed system and used methodology are described. The designed system incorporates cloud computing, real routes of vehicles, analysis of collected data, energy consumption optimization, and time windows. Also, a mathematical model is developed with the aim of optimizing the total energy consumption. Real thirty locations in Budapest in the VII district are described and used as a case study for finding the solutions of the optimized taken routes and energy consumption by the genetic algorithm for both diesel and electric trucks. In the end, the results are analyzed and compared against a random solution to clarify the presented optimization's effectiveness.

An ROS Architecture for Autonomous Mobile Robots with UCAR Platforms in Smart Restaurants

To lessen the spread of COVID-19 and other dangerous bacteria and viruses, contactless distribution of different items has gained widespread popularity. In order to complete delivery tasks at a catering facility, this paper explores the development of an autonomous mobile robot. The robot, in particular, plans its path and maintains smooth and flexible mobility using a Time Elastic Band (TEB) motion control method and an upgraded Dijkstra algorithm. On the open-source AI platform of iFLYTEK, a voice recognition module was trained to recognize voice signals of different tones and loudness, and an image recognition capability was attained using YOLOv4 and SIFT. The UCAR intelligent vehicle platform, made available by iFLYTEK, served as the foundation for the development of the mobile robot system. The robot took part in China’s 16th National University Student Intelligent Car Race, an experimental demonstration test of the developed mobile robotics. The results of the experiments and task tests demonstrated that the proposed robot architecture was workable. In addition, we designed and put together a mobile robot utilizing components from the Taobao website. Compared to UCAR, this robot is less expensive and has the flexibility to be used in a variety of real-world settings.

Handle with care! Service contract termination as a service delivery task

Purpose Profitability considerations lead service providers to terminate service contracts with low-value customers. However, customers targeted by service contract terminations often take revenge through negative word-of-mouth (NWOM). Presently, it is unclear how service contract termination initiatives prevent this harmful side effect. The purpose of this study is to compare the effectiveness of common service contract termination initiatives for reducing NWOM of customers whose service contracts are being cancelled. The study results provide guidance for minimizing the downside of service contract termination. Design/methodology/approach This study distinguishes between service contract termination initiatives common in practice (preannouncement, explanation, financial compensation, apology and support in finding an alternative provider). Drawing on a multi-industry survey of 245 customers who have experienced service contract terminations in real life, the authors estimate regression models to link perceived service contract termination initiatives to NWOM. Findings All else equal, only preannouncement and support in finding an alternative are effective to reduce NWOM. This study also shows that the right choice of service contract termination initiatives depends on the context of the termination. Making a preannouncement, offering an explanation and providing support in finding an alternative are more effective in reducing NWOM when these actions are aligned with the contextual factors of relationship duration and competitive intensity. Research limitations/implications This study shows that service contract termination needs to address several aspects of the service termination experience. The key implication for future research is that it matters in terms of NWOM how service contract terminations are performed. Practical implications This research identifies the service contract termination initiatives that are most effective to reduce NWOM after service contract termination in general and under consideration of the moderating roles of relationship duration and competitive intensity. Originality/value While most related studies have considered customer responses to the cancellation of other customers’ contracts, this study contributes to the scarce literature on the undesirable customer responses (such as NWOM) to the termination of their own contract. To the best of the authors’ knowledge, it is the first study in this emerging stream of research that accounts for the effects of process- and outcome-oriented contract termination initiatives on NWOM. To the best of the authors’ knowledge, it is also the first study to account for moderators of the effect of contract termination initiatives on NWOM, namely, relationship duration and competitive intensity.

Wound Care Self-Efficacy Assessment of Italian Registered Nurses and Wound Care Education in Italian Nursing Education System: A Cross-Sectional Study.

Wounds are a major public health challenge for nurses, and poor wound care has important implications for patients and health care systems. The aim of this study is to assess the Italian registered nurses’ (RNs) perception in the area of wound care, regarding their knowledge, tasks of care delivery, wound management, values, and attitudes, exploring also the previous specific education received during nursing education. An observational online web-based survey was used to assess learning goals and content for wound care education in undergraduate nursing education and the skills and level of self-efficacy in this area during clinical practice. The data were collected between April and May 2022. A total of 210 RNs were interviewed and divided into five national geographic areas. Northwestern RNs showed a better education about the wound care area during university courses: the rate of RNs that did not receive any training in the wound care area was lower than in other Italian geographical areas. Southern RNs presented a better knowledge about factors that expose the wound to becoming chronic, wound drains care, and the ability to assess diabetic foot. This study showed that, in Italy, education in wound care among nursing students is relatively poor, and many skills are achieved during an RN’s career in an empirical way.

Solution of World Robot Challenge 2020 Partner Robot Challenge (Real Space)

The World Robot Challenge is an international competition for the social implementation of robots. Among them, the Partner Robot Challenge (Real Space) is a category that focuses on domestic service robots, competing to achieve simple tasks such as tidying a room, avoiding small obstacles, grasping a specified object from the shelf, and delivering an object to a waving person. In this category, we focused on the theme of ‘Keep Moving’ and worked on researching and developing technologies for object recognition, grasping, and other tasks. For object recognition, we propose an automatic dataset generator using a physics simulator and generate about 490,000 images in two hours. We trained it with an instance segmentation model, achieving an accuracy of about 79.3% in all games. For object grasping, we installed a three-dimensional sensor on the robot hand, and achieved successful grasping with high accuracy of about 79.6% (88.9% excluding failures due to hardware problems) in all games. In addition, we propose a motion synthesis method that simultaneously performs movement and posture transition to achieve high-speed motion. When navigation is executed, the robot changes to the navigation posture while aiming for the goal. Before reaching the goal, it changes the posture for the following action. As a result, we realized a speedup of about 1.32 times in the pick-and-place task. In the tidy-up task, the robot could clear an average of 14 objects and a maximum of 16 objects in 15 minutes. In the obstacle avoidance task, the robot succeeded five times out of six games, and in the delivery task, it succeeded four times out of six games. Throughout all the tasks, we scored 800 points twice, the highest score in the world, and won the championship. This research is aimed at the practical application of domestic service robots. We believe that the results presented in this paper demonstrate the practicality of domestic service robots in a well-developed environment. The video of the finals is available at https://youtu.be/ElUb8bfSC34?t=5511 (right side). The our source code using the simulator is available at https://github.com/Hibikino-Musashi-Home/hma_wrs_sim_ws.

Finding Second Hierarchy Points for Cooperative Multi-Vehicles Delivery Route with P-median and Voronoi Diagram in Various Driving Area

With the recent development of autonomous driving technology, various unmanned vehicles such as serving robots, mobile delivery robots, and drones have been developed. However, each robot has a different driving area, so it is necessary to find cooperative route for optimal unmanned delivery. In this paper, a cooperative scenario of an autonomous courier vehicle and five mobile delivery vehicles perform unmanned delivery tasks. The courier vehicle moves from a warehouse to the second hierarchy point, and drops off the five mobile delivery vehicles in order to transport goods to the doorstep of the destinations. We define the second hierarchy point by using the P-Median method and the Voronoi diagram method, and compare the results of each method using real map data. The points using P-Median are calculated based on the actual road distance and have shorter average moving distance than the Voronoi diagram method. The Voronoi diagram uses the Euclidian distance based on location information, which defines the second hierarchy point faster than the P-Median method. By comparing the results, we consider the Voronoi diagram method have capability in the cooperative multi-vehicle delivery route problem. The result of this paper have potential to be used in rough and remote areas where road information and infrastructure are lacking.

Solving open vehicle problem with time window by hybrid column generation algorithm

This paper addresses the open vehicle routing problem with time window (OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task. The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process. We propose a hybrid column generation algorithm (HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time.

An improved variable neighborhood search for parallel drone scheduling traveling salesman problem

Parallel drone scheduling traveling salesman problem (PDSTSP) is a typical optimization problem of the truck-drone hybrid delivery system and hardly solved by using meta-heuristics. In this study, an improved variable neighborhood search (IVNS) is presented to minimize the completion time of all vehicles for serving all delivery tasks. Three methods based on the longest processing time (LPT) rule and two reduced variable neighborhood search (RVNS) algorithms are used to produce an initial solution. Then IVNS based on a shaking method and an adaptive RVNS is used to improve the initial solution. 20 neighborhood structures are proposed, 14 of which are used to assign customers between the truck and drones. A number of experiments are conducted on 90 instances. The results reveal that IVNS is very competitive for PDSTSP and obtains state-of-the-art solutions of 12 instances.

Functionalized poly(oligo(lactic acid) methacrylate)-block-poly(oligo(ethylene glycol) methacrylate) block copolymers: A synthetically tunable analogue to PLA-PEG for fabricating drug-loaded nanoparticles

PL(G)A-PEG block polymers have been widely used in a variety of drug delivery applications but are inherently limited in terms of their capacity for functionalization and thus customization to specific delivery tasks. Herein, we report synthetically tunable and functionalizable brush co-polymer analogues of conventional PL(G)A-PEG polymers fabricated via atom transfer radical polymerization (ATRP) of methacrylate-based co-monomers with oligo(lactic acid) side chains in the hydrophobic block and oligo(ethylene glycol) side chains in the hydrophilic block. Block co-polymers prepared by varying the molecular weights of each block, the functionality of either block (via functional monomer copolymerization), as well as the number of lactic acid repeat units in the hydrophobic block were subsequently used to fabricate nanoparticles with sizes of 100–500 nm and tunable degradation rates via flash nanoprecipitation. The resulting nanoparticles exhibited high colloidal stability (consistent with the dense brush-like POEGMA interface), favorable in vitro cytocompatibility, and the capacity for effective drug loading (>96% encapsulation efficiency for paclitaxel and 41% even for the hydrophilic therapeutic doxorubicin hydrochloride). Furthermore, functionalization of the hydrophobic POLAMA block with methacrylic acid residues enabled controlled dissolution of the nanoparticle over time, offering potential to tune drug release via an alternative mechanism not readily accessible with conventional PLA-PEG-based block copolymers. Combining these properties with the flexible chemical compositions and the generally recognized as safe degradation properties of the brush polymer analogues, POLAMA-b-POEGMA polymers represent a highly versatile platform for nanoparticle-based drug delivery applications.

Adaptive-Learning-Based Vehicle-to-Vehicle Opportunistic Resource-Sharing Framework

With an ever-increasing number of connected devices on roads, it becomes unsustainable to provide nearby specialized execution resources (compute and storage) for servicing innovative applications. Moreover, the vehicular environment being inherently ad hoc and opportunistic, not to mention highly mobile, makes it unsuitable to use traditional cloud computing due to delayed and interrupted services. Thus, there is a possibility to introduce potential collaboration among nearby connected vehicles. However, the underlying decision model for the selection of the most suitable vehicle for task offloading is challenging in such a dynamic environment. In this study, we propose a collaborative vehicular computing framework that adopts online learning for efficient task assignment between local and neighboring computing resources. The underlying workload adaptive task offloading intends to balance out the workload across neighboring vehicles. The framework is compared against three techniques including two adaptive learning techniques in terms of service delay, efficiency, task delivery rate, task failures, and learning regret. The results demonstrate the effectiveness of the proposed resource-sharing network, improving service quality and throughput for servicing innovative intelligent transportation applications.

Bio-Inspired Vision and Gesture-Based Robot-Robot Interaction for Human-Cooperative Package Delivery.

This research presents a novel bio-inspired framework for two robots interacting together for a cooperative package delivery task with a human-in the-loop. It contributes to eliminating the need for network-based robot-robot interaction in constrained environments. An individual robot is instructed to move in specific shapes with a particular orientation at a certain speed for the other robot to infer using object detection (custom YOLOv4) and depth perception. The shape is identified by calculating the area occupied by the detected polygonal route. A metric for the area’s extent is calculated and empirically used to assign regions for specific shapes and gives an overall accuracy of 93.3% in simulations and 90% in a physical setup. Additionally, gestures are analyzed for their accuracy of intended direction, distance, and the target coordinates in the map. The system gives an average positional RMSE of 0.349 in simulation and 0.461 in a physical experiment. A video demonstration of the problem statement along with the simulations and experiments for real world applications has been given here and in Supplementary Material.

Crowdsourced last-mile delivery with parcel lockers

Crowdshipping is increasingly known as a sustainable solution to address the challenges of last mile delivery (LMD) in urban areas. While employing the crowd to perform LMD appears to be an operationally and financially appealing model, it comes with several challenges in practice, including low willingness to participate in delivery work due to low financial incentive and additional travel effort. Inspired by the Physical Internet concept, in this paper we propose a novel crowdsourced LMD problem and solution approach, which allows a delivery task to be performed by one or multiple crowdshippers using parcel lockers as exchange points. The utilisation of parcel lockers in a crowdshipping network allows for shorter trip detour and better geographical coverage. To achieve this objective, we develop a novel model for locating parcel lockers and allocating delivery tasks. A two-phase algorithm is then developed to rank and choose parcel lockers from the potential locations, which first classifies jobs into single and joint delivery sets and then scores each prospective locker by its utilisation in cooperative delivery. A second algorithm is then designed with three selection strategies of random, roulette, and inverse roulette to assign jobs to crowdshippers for single or joint delivery. To evaluate the performance of the algorithms, experiments were conducted in small and large instances based on a real-world case study. While the exact solution was only capable to deal with small-sized problems, the proposed algorithms were able to produce (sub-)optimal results with significantly low computational expenses. Numerical analyses conducted on large instances showed that enabling joint delivery can improve the success delivery rate by up to 5%, which can be achieved by having a small number of parcel lockers hired at ‘critical’ locations.

Hybrid Convolutional-Transformer framework for drone-based few-shot weakly supervised object detection

Drone delivery is becoming a new trend in the logistics system, but few researches are developed in this field. Locating the target buildings in the drone camera is a crucial technique. However, it is difficult to collect extensive drone-view images and their bounding box annotations for supervised training. Therefore, we address this problem by formulating it as a weakly supervised task and using small amount of category labels as supervision. To extract representative features of cross-view and cross-device images, we propose a Hybrid Convolutional-Transformer (HCT) framework for detection given the very few image-level annotated images. To better evaluate the proposed method in the realistic drone delivery task, we build a drone-view object detection dataset based on the University-1652 benchmark by annotating bounding boxes of target buildings. Extensive experimental results demonstrate the effectiveness of the proposed method.

Supply chain resilience: how autonomous rovers empirically provide relief to constrained flight line maintenance activities

PurposeThe purpose of this research is to explore the utility of autonomous transport across two independent airframe maintenance operations at a single location.Design/methodology/approachThis study leveraged discrete event simulation that encompassed real-world conditions on a United States Air Force flight line. Though the Theory of Constraints (TOC) lens, a high-demand, human-controlled delivery asset is analyzed and the impact of introducing an autonomous rover delivery vehicle is assessed. The authors’ simulations explored varying numbers and networks of rovers as alternative sources of delivery and evaluated these resources’ impact against current flight line operations.FindingsThis research indicates that the addition of five autonomous rovers can significantly reduce daily expediter delivery tasks, which results in additional expertise necessary to manage and execute flight line operations. The authors assert that this relief would translate into enhancements in aircraft mission capable rates, which could increase overall transport capacity and cascade into faster cargo delivery times, systemwide. By extension, the authors suggest overall inventory management could be improved through reduction in transportation shipping time variance, which enhances the Department of Defense’s overall supply chain resilience posture.Originality/valueWhen compared against existing practices, this novel research provides insight into actual flight line movement and the potential benefits of an alternative autonomous delivery system. Additionally, the research measures the potential savings in the workforce and vehicle use that exceeds the cost of the rovers and their employment.