A hybrid approach based on qualitative and quantitative techniques for analyzing last-mile parcel delivery

Unmanned Aerial Vehicles (UAVs) are being increasingly implemented in parcel delivery applications. The scientific progress in this field is progressing exponentially. However, there is a notable gap in synthesizing recent research progress in UAV applications for last-mile delivery. This review study addresses this gap and conducts an in-depth review of UAV research for last-mile delivery across seven domains: environmental performance, economic impacts, social impacts, policy and regulations, routing and scheduling, charging infrastructure, and energy consumption. The review indicates that UAVs promise to reduce last-mile delivery emissions by 71% and costs by 96.5% compared to truck delivery. Saturated knowledge analysis is conducted across the seven domains to identify potential research gaps. Additionally, this review identifies key knowledge gaps, including variability in environmental and cost data, limitations associated with 2D modelling, and a lack of experimental validation. Future research interventions aimed at advancing UAV adoption in last-mile delivery applications are discussed.

<div>Developing safe and reliable autonomous vehicles is crucial for addressing contemporary mobility challenges. While the goal of autonomous vehicle development is full autonomy, up to SAE Level 4 and beyond, human intervention remains necessary in critical or unfamiliar driving scenarios. This article introduces a method for gracefully degrading system functionality and seamlessly transferring decision-making and control between the autonomous system and a remote safety operator when needed. This transfer is enabled by an onboard dependability cage, which continuously monitors the vehicle’s performance during its operation. The cage communicates with a remote command control center, allowing for remote supervision and intervention by a safety driver. We assess this methodology in both lab and test field settings in a case study of last-mile parcel delivery logistics and discuss the insights and results obtained from these evaluations.</div>

Mobile home delivery parcel lockers

The e-commerce market has shown a remarkable expansion rate in recent years also leading to a comparable increase of the number of mail parcels to be delivered. In this article we propose the use of autonomous, fully connected shuttles, to deliver parcels to pick-up/drop-off (PUDO) points located close to bus stops. In doing so, we aim to share passenger transport and last-mile delivery, taking advantage of empty places on shuttles to transport mail baskets optimally filled with parcels. We propose a fast greedy algorithm and a more complex genetic algorithm to solve this problem considering not only the parcels’ size and destination, but also the different shuttles’ stops and onboard space availability. Our results show that the use of this proposal reduces the number of parcels to be delivered by using the traditional postal service, which translates in less road traffic congestion and a more efficient service for customers.

The integration of drones for last-mile parcel deliveries tends to be a key factor in improving logistics services. Therefore, this work aims to define a Mixed Integer Linear Programming (MILP) model in order to study the Vehicle Routing Problem with Drones (VRP-D). The mathematical formulation is based on the Simultaneous Traveling Repairman Problem with Drone (STRPD), in which a vehicle with one or more drones departs from a depot to deliver parcels, aiming to reduce customer waiting times. Whereas in the STRPD the drone can only serve one customer per trip, in the proposed study the drone can serve multiple customers per trip. However, its endurance is limited by battery energy consumption, which depends on the weight carried by the drone. This problem is called the Simultaneous Traveling Repairman Problem with Drone with Multiple Visits (STRPDMV), and this work validated the mathematical model through the analysis of nineteen instances. Due to the inability to obtain results for some instances with twenty or more customers, a heuristic was developed to find feasible solutions within reduced processing times. Then, the results provided by the new model and the heuristic were compared and the analyses were presented. Additionally, another analysis performed was the impact of varying drone speed compared to vehicle speed, and vice-versa.

The development of green production types such as personalized production and shared manufacturing, which use additive technologies in city multifloor manufacturing clusters (CMFMCs), has led to an increase in last-mile parcel delivery (LMPD) activity. This study investigates the integration of electric vehicles and crowdshipping systems into smart CMMCs to improve urban logistics operations related to the distribution of products to consumers. The aim of this study is to improve the LMPD performance of these integrated systems and to provide alternative solutions for sustainable city logistics using the potential of crowdshipping and vehicle sharing fleets (VSFs) in the city logistics nodes (CLNs) of CMFMCs. The issues presented by the loading–unloading operations and sustainable crowdshipping scenarios for LMPD in CMFMCs are considered. This paper presents a new performance evaluation model for crowdshipping LMPD in CMFMCs using VSFs. The case study shows that the proposed model enables the analysis of LMPD performance in CMFMCs, taking into account their finite production capacity, and that it facilitates the planning of cargo turnover and the structure of VSFs consisting of e-bicycles, e-cars, and e-light commercial vehicles (e-LCVs). The model is verified based on a case study for sustainable LMPD scenarios using VSFs. The proposed model enables the planning of both short- and long-term logistics operations with the specified performance indicator of VSF usage in CMFMCs. The validity of using the integrated potential of crowdshipping and vehicle sharing services for LMPD under demand uncertainty in CMFMCs is discussed. This study should prove useful for decision-making and planning processes related to LMPD in CMFMCs and large cities.

There are several difficulties in the logistics chain's last-mile delivery phase, especially when it comes to guaranteeing package safety. The present study aims to provide a thorough analysis of the complex problems that affect package security in the final leg of delivery to different kinds of customers, recipients, and other users. Using a quantitative methodologies research methodology, the study seeks to identify the challenges associated with meeting the distinct needs of various demographic groups. The objective of this study is to investigate challenges in ensuring parcel safety during last-mile delivery. The research design in this study is quantitative. A quantitative research design involves the collection and analysis of numerical data to understand and quantify consumer experiences with last-mile parcel delivery. A total sample about 245 questionnaires was collected and the analysis using SPSS software. The results of the investigation will provide insight into the variables affecting package security and the general calibre of last-mile delivery services. Through the identification and comprehension of these challenges, we aim to provide significant insights into the postal and courier industry. This will facilitate the creation of focused strategies and solutions that aim to improve parcel safety in a variety of demographic contexts, ultimately maximizing the effectiveness and dependability of last-mile delivery services.

The customer satisfaction is closely linked to the security and integrity of postal items at the last mile delivery stage in the ever-changing postal and courier sector. This research explores the complex world of consumers' experiences and perceptions of their packages' safety on the last leg of delivery. The research design in this study is quantitative. A quantitative research design involves the collection and analysis of numerical data to understand and quantify consumer experiences with last-mile parcel delivery. Total sample about 245 questionnaires were collected and the analysis using SPSS software. The goal of the inquiry is to comprehend customer behaviours, expectations, and concerns regarding the safety of postal articles during last-mile delivery. Important components, including handling procedures, delivery schedules, communication efficacy, and package quality, will be closely examined to see how they affect customer satisfaction and confidence in the postal and courier services. The objective of this study is to examine the factors influence consumer perceptions on the practice of parcel delivery at the doorstep when not at home for service improvement to ensure parcel safety. This study aims to provide useful information to industry stakeholders by highlighting the postal article safety customer experience. The results are anticipated to guide tactics and procedures that improve the security measures used by courier and postal services, eventually encouraging a customer-focused strategy throughout the last-mile delivery stage. The results of this study seek to optimise postal item safety, assuring a good and secure customer experience in the constantly changing world of postal and courier logistics, as the sector grapples with elevated consumer expectations and changing market dynamics

This study explores the complex field of last-mile delivery with the goal of offering an in-depth analysis of the various difficulties related to guaranteeing parcel safety. In a time of swift advancements in technology and elevated customer demands, the last-mile portion of the delivery process becomes paramount importance. The research design in this study is quantitative. A quantitative research design involves the collection and analysis of numerical data to understand and quantify consumer experiences with last-mile parcel delivery. Total sample about 245 questionnaires were collected and the analysis using SPSS software. This study used a range of quantitative techniques, such as frequency analysis, mean, and standard deviation computations to investigate issues in ensuring parcel safety during last-mile delivery. The study employs statistical tests to investigate possible variances across demographic factors in addition to identifying critical difficulties. The results provide comprehensive insights into the most and least difficult components of the last-mile delivery environment. The study's conclusions highlight how important it is to deal with these issues in order to improve consumer satisfaction, operational effectiveness, and service quality. The research offers helpful advice for courier industry stakeholders on everything from handling competence difficulties and regulatory obstacles to simplifying equipment requirements and navigating a fiercely competitive market. Moreover, a thorough examination of the discrepancies between consumer experiences and expectations provides information about areas that need strategic interventions.

The dynamics of e-commerce, combined with the growing number of users and the increasing volume of last-mile consignments, are driving current developments in logistics. As online commerce continues to grow, so does the need for active service providers to deliver courier, express, and parcel (CEP) shipments on time. Autonomous last-mile delivery is said to have the potential to transform the way we receive goods. As these autonomous delivery technologies continue to develop and become more widespread, it is possible to foresee a future where autonomous delivery vehicles seamlessly navigate our roads and sidewalks, bringing our online orders to our doorsteps faster and more efficiently than ever. After a general classification of sidewalk autonomous delivery robots (SADRs), this paper examines how far the development and implementation of these vehicles have progressed and which developments offer the greatest opportunities for ecological and economic use in the future.

South Korea has one of the largest e-commerce markets in the world. The last-mile delivery segment of e-commerce often causes critical damage to products in protective packages. Despite the rapid growth of the e-commerce market in Korea, the last-mile distribution environment has not yet been thoroughly investigated. The main aim of this study was to provide an understanding of the vibration levels that were measured from various parcel delivery routes within Seoul, Korea, using common types of parcel delivery trucks. Vibration levels of ten delivery trucks were measured and analyzed in terms of power spectral densities (PSDs) and presented as PSD spectra. The last-mile delivery vehicle vibration levels in Korea were found to be consistently lower (in the 1 to 200 Hz frequency range) than those recommended by international standards and lower than the vibration levels of parcel delivery vehicles in the U.S. and Hungary. The results also revealed that the highest intensity peak of the PSD spectrum for Korea was located in the lower frequency range (1.5 to 2 Hz) compared to the ISTA 3A pickup and delivery test profile (3 to 4 Hz) and the test profile recommended for Hungary (13 to 16 Hz). A smoothed composite spectrum was also provided to support Korean packaging engineers in optimizing their packages by simulating proper last-mile truck delivery vibration levels in lab conditions.

In the last few years, online shopping has been increasing rapidly. Optimising the sorting processes in E-fulfilment centres, combined with direct parcel delivery from the E-fulfilment centres to the close-by customers has not been widely studied in a quantitative manner. The main objective is to show whether E-fulfilment centres can generate a reduction in costs for parcel delivery.Two models are used to calculate the cost savings by using of an E-fulfilment centre. The volume of parcels has a crucial role in the calculation (and the competitiveness) of delivery costs. The time-driven performance reaches a break-even point for the volumes that can compete with the logistics providers. The impacts of directly delivering parcels from an e-fulfilment centre to nearby customers result in lower greenhouse gas emissions.

Agent-based model of last-mile parcel deliveries and travel demand incorporating online shopping behavior

The impact of civil airspace policies on the viability of adopting autonomous unmanned aerial vehicles in last-mile applications

The advantages of rotary-wing drone (RWD) delivery modes have already been delineated. However, single-unit RWDs do not completely solve real problems in last-mile parcel deliveries because of limited payload capacity and flight endurance. Currently, drone swarm technology is being rapidly developed. An RWD swarm can deliver heavy or multiple packages to customers; therefore, the RWD swarm strategy can address the payload capacity limitation of RWDs. Herein, an RWD delivery mode that involves dynamic swarms of RWDs in addition to single-unit RWDs is explored. The “dynamic” characteristic permits the RWD members in swarms to vary by coupling/decoupling operations at nodes. From the routing plan perspective, using RWD swarms for last-mile parcel deliveries is challenging; accordingly, we introduce a swarm synchronization mode that involves interactions among RWD routes. We formally define the drone routing problem with swarm synchronization (DRP-SS) and develop a mixed-integer linear programming model, which considers the decision on RWD swarms and multi trips. An adaptive large neighborhood search heuristic with specific operators is proposed. In the computational experiments, both small- and large-scale instances are used to validate the effectiveness of the mathematical formulation and the heuristic. Several managerial insights are obtained regarding the influence of detours, the utilization of RWD swarms, and the benefits of multi trips. The DRP-SS model and solution method can be used to estimate the performance of the selection of RWD swarms in practical situations.

This paper considers using drones to conduct the last-mile parcel delivery. To enable the beyond-battery-lifetime flight, drone stations are considered to replace or recharge the battery for drones. We focus on the flight planning problem with the goal of minimizing the total travel time from the depot to a customer, a key indicator of the quality of service. We investigate four typical ways for the drone to get extra energy at drone stations: 1) replacing the battery with a fresh one, 2) recharging the battery to the full capacity, 3) recharging the battery to the optimal level, and 4) recharging the battery to the optimal level accounting for the availability of drone stations (i.e., whether a drone station is occupied by other drones). While the first two scenarios can be formulated following the framework of integer linear programming, the last two scenarios turn into mixed-integer nonlinear programming problems. To address the later problems, we present a framework in which discretized state graphs are constructed first and then the optimal paths are found by graph searching algorithms. We propose a dynamic version of Dijkstra’s algorithm to deal with the unavailability issue of drone stations. The algorithm can quickly find the optimal flight path for a drone, and extensive computer-based experimental results have been presented to demonstrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Multi-rotary unmanned aerial vehicles (UAVs), also known as drones, have been regarded as a promising means to reshape future logistics. To save human labour and reduce cost, many giant logistics companies have been dedicated to developing various drones to deliver light and small parcels during the past decade. However, due to the limitation of payload, the battery capacity is constrained, which prevents drones from long-distance flights. Practitioners have tried the drone-vehicle collaboration method, but this still requires human labour to participate. In this paper, we present a framework where drones autonomously conduct long-distance delivery with the assistance of drone stations. It is worth pointing out that such a framework is not to replace the ground delivery method but to serve as an alternative to the ground counterpart for small and light parcels. A particular focus is on the flight planning from the depot to a destination, which includes not only a sequence of drone stations to stop at but also the corresponding rest time to recharge the battery. Several typical scenarios about battery recharging are discussed, and a dynamic version of Dijkstra’s algorithm is presented to deal with the challenging case where drone station resources are limited. The presented approach is able to find out the optimal flight plan quickly.

Acceptance of e-vehicles for last-mile parcel delivery from the perspective of drivers: a study in Turkiye

The drone latency location routing problem under uncertainty

This paper analyzes the carbon emission impact of pickup points in last-mile parcel delivery. Pickup points provide customers and delivery companies with an alternative to attended home delivery. The delivery company can drop a parcel off at the pickup point, such as a service desk in a grocery store or a parcel locker, from where the customer collects the parcel. Because of the potential efficiency gains for the delivery vehicle, pickup points are often presented as a sustainable alternative to home delivery. The efficiency gains for the delivery vehicle need to be weighed against customers traveling to the pickup point by car, however. The mathematical analysis presented in this paper integrates continuous approximation techniques to assess the potential for improved delivery route efficiency with multinomial logistic regression for estimating the travel distance and mode choice of customers collecting their parcels. The results challenge the suggestion that pickup points are a universally sustainable alternative to home delivery. The potential for a net positive carbon emission impact is greatest when pickup points are established in urban settings, while in rural settings, the carbon emission benefits derived from improved delivery route efficiency are quickly offset by the carbon footprint associated with customer travel.