Delivery Tasks Research Articles

Optimizing task assignment and routing operations with a heterogeneous fleet of unmanned aerial vehicles for emergency healthcare services

This paper studies the optimization of task assignment and pickup and delivery operations using a heterogeneous fleet of unmanned aerial vehicles (UAVs). We specifically address the distribution of emergency medical supplies, including medications, vaccines, and essential medical aid, as well as the collection of biological blood samples for testing and analysis. Unique challenges, such as supply shortages, time windows, and geographical considerations, are explicitly taken into account. The problem is first formulated as a mixed-integer linear programming model aimed at maximizing the total profit derived from the execution of a set of emergency healthcare pickup and delivery tasks. An enhanced Q-learning-based adaptive large neighborhood search (QALNS) is proposed for large-scale benchmark instances. QALNS exhibits a superior performance on benchmark instances. It also improves the quality of the solutions on average by 5.49% and 6.86% compared to the Gurobi solver and a state-of-the-art adaptive large neighborhood search algorithm, respectively. Sensitivity analyses are performed on critical factors contributing to the performance of the QALNS algorithm, such as the learning rate and the discount indicator. Finally, we provide managerial insights on the use of the fleet of UAVs and the design of the network.

Design and Implementation of a Community Waimai Delivery Assistant System Based on Path Optimization Algorithm

With the rapid development of the food delivery industry, how to efficiently complete delivery tasks in complex community environments has become an important challenge for food delivery platforms and delivery personnel. This project focuses on the problem of planning delivery routes for b0oth inside and outside residential areas, and designs and implements a delivery assistant system based on the Internet of Things and path optimization algorithms. The system utilizes the DFRobot IoT platform to achieve real-time communication between Python programs and mobile apps. Through various path optimization algorithms such as genetic algorithm and simulated annealing algorithm, the system automatically generates the optimal delivery path, significantly improving delivery efficiency and reducing delivery costs. The experimental results show that the system has good operability and reliability in practical applications, providing an effective solution for improving the intelligence level of food delivery

Urban Air Logistics with Unmanned Aerial Vehicles (UAVs): Double-Chromosome Genetic Task Scheduling with Safe Route Planning

In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology of an evolutionary-based optimization framework designed to tackle a specific formulation of a Drone Delivery Problem (DDP) with charging hubs. The proposed evolutionary-based optimization framework is based on a double-chromosome task encoding logic. The goal of the algorithm is to find optimal (and feasible) UAV task assignments such that (i) the tasks’ due dates are met, (ii) an energy consumption model is minimized, (iii) re-charge tasks are allocated to ensure service persistency, (iv) risk-aware flyable paths are included in the paradigm. Hard and soft constraints are defined such that the optimizer can also tackle very demanding instances of the DDP, such as tens of package delivery tasks with random temporal deadlines. Simulation results show how the algorithm’s development methodology influences the capability of the UAVs to be assigned to different tasks with different temporal constraints. Monte Carlo simulations corroborate the results for two different realistic scenarios in the city of Turin, Italy.

Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles

To enhance urban freight efficiency and green development, China has implemented the Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic control policies and increasing the number of new energy vehicles (NEV). However, range anxiety is a significant challenge for freight drivers performing delivery tasks with electric vehicles (a major component of NEV). We constructed a prediction model for the state of charge (SOC), or battery remaining energy percentage when UGFD vehicles reach the next trip point, aiming to alleviate this issue. The model consists of three modules: (1) a vehicle SOC context prediction module, (2) a vehicle energy consumption prediction module, and (3) a multi-perspective SOC prediction value fusion module. Specifically, in the SOC context prediction module, historical SOC sequences, vehicle status (loading/unloading, charging), and time intervals between SOC points are used to accurately describe context change trends, and directly predict the vehicle SOC at the next trip point. The energy consumption prediction module combines community-level and grid-level geographical location information for the vehicle stops using weather, vehicle parameters, etc., to model the spatial dynamic correlation of energy consumption. The vehicle SOC at the next trip point is the difference between the current vehicle SOC and the predicted energy consumption. The multi-perspective SOC prediction value fusion module is a combination of the predicted values from the context and energy consumption perspectives, resulting in the final vehicle SOC prediction value. Taking Suzhou, China as an example, the results show that the mean absolute error, root mean square error, and symmetric mean absolute percentage error for the constructed model are 23.67%, 10.39%, and 20.03% less, respectively, than for the baseline models focusing on SOC short-term time series prediction. The research results can provide scientific evidence for formulating refined energy management, charging station layout, and freight delivery optimization approaches.

Optimizing Drone Delivery Paths from Shared Bases: A Location-Routing Problem with Realistic Energy Constraints

Recently, Amazon patented fulfillment centers for drones on a large scale in densely populated areas. A network of such shared centers can be used for landing and launching drones as an alternative to the traditional private bases in near future. This paper studies how a user of such a shared delivery infrastructure can optimally determine the required bases among the existing bases to perform her delivery tasks. To this end, a location-routing problem is studied where the problem determines the drone launching centers and their routes to deliver parcels. A realistic energy function is used that incorporates the effect of load weight for calculating energy consumption in all flight phases including take-off, level flight, hovering, and landing. Although the energy consumption is nonlinear, the problem is formulated as a mixed-integer linear programming model and strengthened by valid inequalities and a pre-processing algorithm that enables us to solve instances with up to 100 customers using off-the-shelf optimization solvers. Moreover, a heuristic method is presented to solve instances with up to 200 customers. Results indicate the importance of incorporating the load-dependent energy formula in contrast to using fixed flight duration constraints for drones. The value of allowing multiple visits on each trip versus only simple back-and-forth trips is also assessed. The advantage of using an integrated location-routing approach is shown over the sequential approach in which the locations of bases are decided first and the routes are constructed next. Moreover, to manage future demand uncertainty, the model is extended for the case that a number of demand scenarios are given.

Delivery Route Scheduling of Heterogeneous Robotic System with Customers Satisfaction by Using Multi-Objective Artificial Bee Colony Algorithm

This study addresses the route scheduling problem for the heterogeneous robotic delivery system (HRDS) that perform delivery tasks in an urban environment. The HRDS comprises two distinct types of vehicles: an unmanned ground vehicle (UGV), which is constrained by road networks, and an unmanned aerial vehicle (UAV), which is capable of traversing terrain but has limitations in terms of energy and payload. The problem is formulated as an optimal route scheduling problem in a road network, where the goal is to find the route with minimum delivery cost and maximum customer satisfaction (CS) enabling the UAV to deliver packages to customers. We propose a new method of route scheduling based on an improved artificial bee colony algorithm (ABC) and the non-dominated sorting genetic algorithm II (NSGA-II) that provides the optimal delivery route. The effectiveness and superiority of the method we proposed are demonstrated by comparison in simulations. Moreover, the physical experiments further validate the practicality of the model and method.

Bi-Objective Circular Multi-Rail-Guided Vehicle Scheduling Optimization Considering Multi-Type Entry and Delivery Tasks: A Combined Genetic Algorithm and Symmetry Algorithm

Bi-Objective Circular Multi-Rail-Guided Vehicle Scheduling Optimization Considering Multi-Type Entry and Delivery Tasks: A Combined Genetic Algorithm and Symmetry Algorithm

Comprehensive Task Optimization Architecture for Urban UAV-Based Intelligent Transportation System

This paper tackles the problem of resource sharing and dynamic task assignment in a task scheduling architecture designed to enable a persistent, safe, and energy-efficient Intelligent Transportation System (ITS) based on multi-rotor Unmanned Aerial Vehicles (UAVs). The addressed task allocation problem consists of heterogenous pick-up and delivery tasks with time deadline constraints to be allocated to a heterogenous fleet of UAVs in an urban operational area. The proposed architecture is distributed among the UAVs and inspired by market-based allocation algorithms. By exploiting a multi-auctioneer behavior for allocating both delivery tasks and re-charge tasks, the fleet of UAVs is able to (i) self-balance the utilization of each drone, (ii) assign dynamic tasks with high priority within each round of the allocation process, (iii) minimize the estimated energy consumption related to the completion of the task set, and (iv) minimize the impact of re-charge tasks on the delivery process. A risk-aware path planner sampling a 2D risk map of the operational area is included in the allocation architecture to demonstrate the feasibility of deployment in urban environments. Thanks to the message exchange redundancy, the proposed multi-auctioneer architecture features improved robustness with respect to lossy communication scenarios. Simulation results based on Monte Carlo campaigns corroborate the validity of the approach.

A Two Phases Multiobjective Trajectory Optimization Scheme for Multi-UGVs in the Sight of the First Aid Scenario.

Timely delivery of first aid supplies is significant to saving lives when an accident happens. Among the promising solutions provided for such scenarios, the application of unmanned vehicles has attracted ever more attention. However, such scenarios are often very complex, while the existing studies have not fully addressed the trajectory optimization problem of multiple unmanned ground vehicles (multi-UGVs) against the scenario. This study focuses on multi-UGVs trajectory optimization in the sight of first aid supply delivery tasks in mass accidents. A two-stage completely decoupling fuzzy multiobjective optimization strategy is designed. On the first stage, with the proposed timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm, collision-free coarse tunnels are build within a tridimensional coordinate system, respectively, for the UGVs as the corresponding configuration space for a further multiobjective optimization. On the second stage, a fuzzy multiobjective transcription method is designed to solve the decoupled optimal control problem (OCP) within the configuration space with the consideration of priority constrains. Following the two-stage design, the computational time is significantly reduced when achieving an optimal solution of the multi-UGV trajectory planning, which is crucial in a first aid task. In addition, other objectives are optimized with the aspiration level reflected. Simulation studies and experiments have been curried out to testify the effectiveness and the improved computational performance of the proposed design.

Anthropomorphic last-mile robots and consumer intention: An empirical test under a theoretical framework

Despite booming online retail in cities, there is almost no research on the complex relationship between anthropomorphic delivery robot targets and consumer behavior. This study employs an in-depth investigation into the relationship between last-mile anthropomorphic delivery robots and consumer usage intentions by integrating current literature and applying the theories of service quality hierarchy and task-technology fit. The study gathered data from 663 Chinese customers and employed structural equation modeling to analyze the findings. The study results suggest that the anthropomorphic qualities of delivery robots, such as their intelligence, autonomy, ability to learn, and social behavior, positively influence consumers’ willingness to use these robots for delivery tasks. In addition, the quality of human-robot interaction and task-technology fit are critical to increasing consumer willingness to use these systems. This study enriches the discussion of consumer-robot dynamics in urban retail and provides strategic guidance for optimizing smart delivery solutions in the logistics and robotics industries.

A fluid–particle decomposition approach to matching market design for crowdsourced delivery systems

This paper considers a crowdsourced delivery (CSD) system that effectively utilizes the existing trips to fulfill parcel delivery as a matching problem between CSD drivers and delivery tasks. This matching problem has two major challenges. First, it is a large-scale combinatorial optimization problem that is hard to solve in a reasonable computational time. Second, the evaluation of the objective function for socially optimal matching contains the utility of drivers for performing the tasks, which is generally unobservable private information. To address these challenges, this paper proposes a hierarchical distribution mechanism of CSD tasks that decomposes the matching problem into the task partition (master problem) and individual task-driver matching within smaller groups of drivers (sub-problems). We incorporate an auction mechanism with truth-telling and efficiency into the sub-problems so that the drivers’ perceived utilities are revealed through their bids. Furthermore, we formulate the master problem as a fluid model based on continuously approximated decision variables. By exploiting the random utility framework, we analytically represent the objective function of the problem using continuous variables, without explicitly knowing the drivers’ utilities. The numerical experiment shows that the proposed approach solved large-scale matching problems at least 100 times faster than a naive LP solver and approximated the original objective value with errors of less than 1%. The code of this work is available at https://github.com/yuki-oyama/fluid-particle-csd.

Assessing the potential impacts of public transport-based crowdshipping: A case study in a central district of Copenhagen

AbstractThe expansion of e-commerce and the sharing economy has paved the way for crowdshipping as an innovative approach to addressing last-mile delivery challenges. Previous studies and implementations have predominantly concentrated on private vehicle-based crowdshipping, which may lead to increased traffic congestion and emissions due to additional trips made specifically for deliveries. To circumvent these possible adverse effects, this paper explores a public transport (PT)-based crowdshipping concept as a complementary solution to the traditional parcel delivery systems. In this model, PT users leverage their routine journeys to perform delivery tasks. We propose a methodology that includes a parcel locker location model and a vehicle routing model to analyze the effect of PT-based crowdshipping. Notably, the parcel locker location model aids in planning a PT-based crowdshipping network and identifying obstacles to its development. A case study conducted in the central district of Copenhagen utilizing real-world data assesses the effects of PT-based crowdshipping. The findings suggest that PT-based crowdshipping can decrease the total kilometers traveled by vehicles, the overall working hours of drivers, and the number of vans required for last-mile deliveries, thereby alleviating urban traffic congestion and environmental pollution. Nevertheless, the growth of PT-based crowdshipping may be limited by the availability of crowdshippers, indicating that initiatives to increase the number of crowdshippers are essential.

Automated Destination Renewal Process for Location-Based Robot Errands

In this paper, we propose a new approach for service robots to perform delivery tasks in indoor environments, including map-building and the automatic renewal of destinations for navigation. The first step involves converting the available floor plan (i.e., CAD drawing) of a new space into a grid map that the robot can navigate. The system then segments the space in the map and generates movable initial nodes through a generalized Voronoi graph (GVG) thinning process. As the second step, we perform room segmentation from the grid map of the indoor environment and classify each space. Next, when the delivery object is recognized while searching the set space using the laser and RGB-D sensor, the system automatically updates itself to a position that makes it easier to grab objects, taking into consideration geometric relationships with surrounding obstacles. Also, the system supports the robot to autonomously explore the space where the user’s errand can be performed by hierarchically linking recognized objects and spatial information. Experiments related to map generation, estimating space from the recognized objects, and destination node updates were conducted from CAD drawings of buildings with actual multiple floors and rooms, and the performance of each stage of the process was evaluated. From the quantitative evaluation of each stage, the proposed system confirmed the potential of partial automation in performing location-based robot services.

Flexibility and freedom suit me better: food delivery couriers' preferred employment status.

This research examines food delivery couriers' preferred employment status and factors explaining their opinions. Previous studies have used qualitative research methods and are unable to explain couriers' general views on employment status. In this research, a survey of 1,539 Wolt couriers was carried out in Finland with logistic regression, cross-tabulation, and content analysis as analysis methods. The results show that 56% of the couriers wanted to work as self-employed and 25% as employed. The opinion was most strongly explained by valuing work-related freedom and flexibility, which were associated with the right to refuse delivery tasks offered and to choose the amount of work, working hours and delivery vehicle. The preference for self-employment was also increased by the duration of courier work, one's own choice to work as a courier, and age. Freedom and flexibility are dependent on the sufficient availability of delivery tasks, posing challenges when the demand is low.

Container drayage problem integrated with truck appointment system and separation mode

Growing demand for seaborne trade has exacerbated the challenges faced by drayage operators in inland container transportation. In some cases, drayage operators must make truck scheduling decisions and book periods for trucks to visit terminals daily. This paper considers the appointment system and separation mode in container drayage problem, which consists of delivery and pickup transportation tasks for empty and full containers. A mixed-integer linear programming model is developed to minimize the total truck operating time while solving the truck scheduling problem, the empty container allocation problem, and the appointment problem. Another key feature of the model is considering the temporal constraints between tasks from a more realistic perspective. Due to the complexity of the problem, it is not practical to obtain exact solutions for large instances. Therefore, we complete the implementation of a new genetic algorithm (GA) that introduces a topological sort tailored to this problem. Several experiments and analyses are used to demonstrate the correctness of the proposed model as well as the efficiency of the algorithm. The findings indicate that, when compared to traditional mode, separation mode can reduce total truck operating time by an average of 29.4%. The total truck operating time difference between the two modes shows an overall positive trend if the task size increases. In addition, a rational setting of the appointment system will have a beneficial effect on truck scheduling. The decision on the selection of periods and the increment of each quota is crucial in optimizing the setting of the appointment system to reduce the total truck operating time for the mutual benefit of both terminals and drayage operators.

Comprehensive multistage approach for measuring the efficiency of logistics processes in the presence of a mismatch between sales and logistics

Frequently, various sectors within the same company often find themselves at odds with one another due to conflicting objectives. For example, the sales and logistics departments frequently fail to synchronize, with sales viewing logistics merely as a cost center. Conversely, sales place demanding delivery tasks on logistics, particularly evident in scenarios involving out-of-stock or non-assortment items. To address this pervasive issue, the primary objective of this paper is to propose a model for assessing the efficiency of logistical processes, distribution centers, and drivers. Hence, it can be asserted that this study, through its proposed model, aims to accomplish several aims: (i) determine the effectiveness of distribution centers (DCs), (ii) analyze how the efficiency of DCs evolves over time, (iii) evaluate the performance of distribution drivers, and (iv) rank proficient drivers for bonus allocation based on their performance. To achieve these objectives, the suggested model relies on the application of the CI-DEA-Malmquist Index-DEA-MEREC-ADAM methodology. Specifically, the model is delineated into three distinct phases. The initial phase entails employing the CI-DEA and Malmquist index methodologies to condense the multitude of parameters considered in the study by utilizing the CI-DEA approach. Additionally, Malmquist analysis is applied to observe the multi factor productivity change in the efficiency of DCs and to identify the most efficient DC across all time periods. Following the identification of the most efficient DC, the subsequent phase focuses on selecting drivers from that center, followed by applying the DEA method to isolate only the most efficient drivers, who are then further evaluated and ranked accordingly. Through the application of these methodologies, it was determined that out of a total of 10 drivers, only four were deemed efficient. In the final phase of the model, these drivers were assessed based on seven criteria for ranking purposes. During this phase, the MEREC method was utilized to ascertain the criteria weights, while the ADAM method was employed for driver ranking. The findings revealed that the second driver ranked the highest and thus should be granted the highest bonus.

Using electric vehicles to enhance power outage resilience – An agent-based modeling approach

Power outages can cause severe disruption to critical infrastructure. With the predicted increase in the electrification of the transport sector, society will become even more vulnerable to the effects of power outages. While increased electric vehicle (EV) adoption will contribute to the electrification process, EVs can also offer capabilities to provide services during an outage. This paper studied the use of a fleet of EVs during the aftermath of a disaster to provide disaster relief by donating power to a shelter, delivering critical supplies and people in need, and providing transport for personnel or performing inspections. While the bulk of the past work has focused on using EVs to increase the resilience of the distribution grid, or individual buildings, to a power outage, this paper was novel in its use of an agent-based model to study EVs that are performing functions to increase the resilience of a community to an outage. The fleet of EVs were provided access to a microgrid with a solar array, one or two EV fast chargers, and three possible sizes of a storage. Useful outputs were produced and studied for such features as daily energy donated to a shelter, daily energy used at the microgrid, and the length of outages that can be supported before the energy is depleted at the microgrid. Results showed that increasing storage size at the microgrid led to substantial increases in the outage length that could be support. Additionally, it was found that focusing a fleet on delivery and transport tasks, as opposed to energy donation, could also increase the length of outages that could be supported.

Constrained Parameterized Differential Dynamic Programming for Waypoint-Trajectory Optimization

Unmanned aerial vehicles (UAVs) are required to pass through multiple important waypoints as quickly as possible in courier delivery, enemy reconnaissance and other tasks to eventually reach the target position. There are two important problems to be solved in such tasks: constraining the trajectory to pass through intermediate waypoints and optimizing the flight time between these waypoints. A constrained parameterized differential dynamic programming (C-PDDP) algorithm is proposed for meeting multiple waypoint constraints and free-time constraints between waypoints to deal with these two issues. By considering the intermediate waypoint constraints as a kind of path state constraint, the penalty function method is adopted to constrain the trajectory to pass through the waypoints. For the free-time constraints, the flight times between waypoints are converted into time-invariant parameters and updated at the trajectory instants corresponding to the waypoints. The effectiveness of the proposed C-PDDP algorithm under waypoint constraints and free-time constraints is verified through numerical simulations of the UAV multi-point reconnaissance problem with five different waypoints. After comparing the proposed algorithm with fixed-time constrained DDP (C-DDP), it is found that C-PDDP can optimize the flight time of the trajectory with three segments to 7.35 s, 9.50 s and 6.71 s, respectively. In addition, the maximum error of the optimized trajectory waypoints of the C-PDDP algorithm is 1.06 m, which is much smaller than that (7 m) of the C-DDP algorithm used for comparison. A total of 500 Monte Carlo tests were simulated to demonstrate how the proposed algorithm remains robust to random initial guesses.

Identifying the influence of land logistic driver cognitive energy impact on supply chain performance through agent-based simulation

Logistic transports link demand generators, distributors, and producers in a supply chain network (SCN). The existence of logistic transports is critical to ensure whole nodes’ economic sustainability. This study explores the impact of human factors on SCN performance through cognitive energy expenditure (CEE) tracking. Agent-based model (ABM) simulation was used to analyze the impact of CEE from truck driver’s electroencephalography (EEG) data to obtain the postsynaptic potential values, which were then transformed to calorific energy. The fleet agents, retailers and distributor models were built based on the East Java, Indonesia, logistic transport route around Karanglo, Gempol, Bungurasih, and Gubeng. The frequency and the peak value of the EEG data, postsynaptic potential, and energy data indicate the same information. All data indicate that more challenging routes have higher frequency and higher peak values. The ABM simulation of the fleet agents shows balanced CEE throughout entire routes due to the precise rest period and eat scheduling. The average delivery success rate was 8 out of 30 or 26.7 % in each simulation time step. Hence, most goods delivery tasks can be completed by fleet agents in a balanced system. As a consequence, the SCN performance is also balanced due to the fluid inventory shift without overstock and stockouts. The rest and eat periods of a fleet agent were scheduled after the CEE has been peaked. The time lag between rest periods and transport operations has to be maintained to overcome fleet agents task buildup. Task buildup has a potential to decay both transport safety and inventory shift rates. Therefore, the upgrade in SCN performance is possible through proper fleet agents scheduling

A framework for visual-based adaptive object-robot interaction of a mobile service robot

The COVID-19 pandemic (hereinafter “the pandemic”) necessitated social distancing measures and limited physical contact, prompting the exploration of alternative methods for tasks like object delivery. Mobile service robots emerged as a potential solution, offering a bridge between humans and various tasks. While existing techniques have been introduced to enable robots to deliver objects in an end-to-end manner, they come with limitations. Grippers, for instance, can deliver only one object per round, cabinet robots require manual speed tuning to keep the object in place, and object holders lack generalizability. Inspired by the idea of human nature to use a tray to deliver the object, we developed the Visual-Based Adaptive Interaction System (hereinafter “VAIS”), a novel learning system, to improve service delivery using visual information and a fast neural learning mechanism. Within this system, the robot learns the optimal angular rotational and linear translational moving speeds to effectively transport objects placed on a tray without an extra holder. The robot validates these learnt movements by successfully completing multiple-object delivery tasks along designated routes. The results exhibit that the robot can utilize online learning after a few attempts to determine its proper moving speed and deliver different objects to a given location.