Parcel Delivery Research Articles

Supply Chain Management in Smart City Manufacturing Clusters: An Alternative Approach to Urban Freight Mobility with Electric Vehicles

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.

UAV p­­­arcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization

Unmanned Aerial Vehicles (UAVs), or drones, have recently become a favorable solution for fast parcel delivery due to their maneuverability and advances in navigation technologies. With the limitation of battery capacity and payload of drones, it is crucial to consider both efficiency and cost while conducting the tasks. Meanwhile, UAVs should not collide with each other while traveling to customers. In this article, we propose a UAV parcel delivery system involving deep reinforcement learning (DRL) approach for collision avoidance and a genetic algorithm for route optimization. Specifically, a delivery center generates near-optimal routes, loading UAV with parcels according to demands. Each UAV takes charge of delivering packages in compliance with the assigned route while avoiding collision with each other. We utilize DRL to achieve collision avoidance without having prior knowledge about the trajectories of other UAVs. Additionally, we adopt a genetic algorithm to obtain the lowest energy cost path for each UAV. To find such an optimized path, we solve a capacitated vehicle routing problem (CVRP) with a modified cost function and extra constraints. Realistic simulations using a physics engine and software-in-the-loop (SITL) are conducted to evaluate the feasibility of the proposed methods.

Optimizing an express delivery mode based on high-speed railway and crowd-couriers

The rapid development of e-commerce has led to various challenges in the express delivery industry. To provide efficient and economical delivery services, this study introduces a joint transportation system for parcel delivery utilizing high-speed railway (HSR) and crowd-couriers. In this system, long-haul transportation between cities is conducted by HSR, which enables multiple transshipments in different operation lines. The pre-haul parcel pickup and end-haul delivery services within the city are performed by crowd-couriers, thus achieving door-to-door transportation. A mathematical model is developed to jointly optimize the freight allocation problem in the rail segment and the crowd-courier routing problem in the road segment. The freight allocation problem is formulated based on the concept of transportation plans. The transportation plans with and without transshipments are found by a depth-first search-based algorithm in the rail segment. A customized heuristic algorithm based on the adaptive large neighborhood search with multiple operators is designed to find a near-optimal solution. A set of numerical experiments demonstrates the speed and accuracy with which the proposed method solves different-sized versions of problem. A sensitivity analysis based on a real HSR network is also conducted to provide more reliable managerial insights for operators.

The use of sound phonons in the development of NVH treatments for light commercial electric vehicles

Light Commercial Electric vehicles (LCEV) are becoming increasingly popular due to their environmental benefits and lower operating costs, making them a sustainable choice for businesses looking to reduce their carbon footprint and save fuel expenses. However, unlike Saloon and SUV electric vehicles LCEV's have to be potentially configured for a wide variety of operating roles including parcel delivery, mini-buses, retail wagons, ambulances and mobile workshops to name just a few. Consequently, when considering their refinement aspects during their development it has become economically restrictive to base this on actual physical prototypes. Acoustic modelling provides a suitable alternative, but any software package must be highly flexible so that multiple configurations can be examined without requiring time consuming re-modelling. This paper describes the use of sound phonons in an interactive GUI for the successful development process of an LCEV from target setting through to final optimised NVH package creation. The project involved full vehicle model creation via donor CAD, of a current ICE LCV, which was morphed into an EV to include an under-floor battery and rear drive system. Sub-system models of areas such as the interior bulkhead, battery pack insulation and EV encapsulation were also integrated during the optimisation process.

Through-Window Home Aerial Delivery System with In-Flight Parcel Load and Handover: Design and Validation in Indoor Scenario

Abstract This paper presents the design, development, and validation in indoor scenario of an aerial delivery system intended to conduct the delivery of light parcels directly to the user through the window of his/her home, motivated by the convenience of facilitating the access to medicines to people with reduced mobility.The system consists of a fully-actuated multi-rotor (FAMR) equipped with a front basket where the parcel to be delivered is loaded by a lightweight and compliant anthropomorphic dual arm system (LiCAS) located at the supply point, using one of the arms to drop the parcel in the basket while the other arm holds its base to support the sudden moment exerted at the FAMR. The paper analyses four types of physical interactions raised during the operation on flight: (1) sudden changes in the mass distribution of the FAMR during the load/unload phase, affecting the multi-rotor position-attitude controllers, (2) impact and impulsive forces exerted by the human on the FAMR to demonstrate the reliability and robustness of conventional cascade controllers, (3) passive accommodation of the LiCAS while holding the FAMR during the parcel load, relying on the mechanical joint compliance, and (4) compliant human–FAMR interaction, interpreting the multi-rotor pose control error as a Cartesian/angular deflection to implement an admittance controller that allows the user guiding the platform. Experimental results allow the identification and characterization of these effects for different payload masses. The execution of the complete operation, involving the parcel load with the LiCAS and handover by the user through a window, is validated in a representative indoor scenario. Graphical Abstract

Designing a sustainable delivery network with parcel locker systems as collection and transfer points

Collection and delivery points, both attended and fully automated, have emerged as a popular solution in last-mile delivery. They offer not only operational advantages for the delivery company but also present a pathway to mitigating the negative environmental impact of parcel delivery operations. In this study, we explore a new approach, where parcel locker systems have a dual role: as collection points for customers and as transfer points for the delivery company. We introduce a variant of the Location-Routing Problem, optimizing the strategic location and capacity size of parcel locker systems as well as the delivery routes from those points. We develop an efficient branch-and-price algorithm and apply it to a real-life case study that focuses on the design of a delivery network with parcel lockers in a residential neighborhood in the city of Groningen, the Netherlands. Our case study findings underscore that the dual role of parcel locker systems considerably reduces the travel distance of delivery vehicles, is well-suited to deal with potential future increase in demand for parcel collection, and reduces the likelihood of customers traveling to parcel locker by car.

Optimising Drone-Assisted Logistics for Urban Last-Mile Delivery: An Overview of Applications, Methodologies, and Emerging Trends

The rapid evolution of drone-assisted logistics for urban last-mile (ULM) delivery has garnered significant interest from both academia and industry. This article presents a comprehensive review of the state-of-the-art research and practical implementations of ULM systems, focusing on the use of unmanned aerial vehicles (UAVs) for the final stage of goods and parcel delivery in urban environments. The applicability of UAV-based logistics across various contexts, including urban and rural areas, is examined, with real-world case studies highlighted to demonstrate practical uses. Key methodologies and models employed in optimising UAV routing and operations are discussed, particularly those that enhance the efficiency and reliability of ULM. The critical advantages and limitations of drone-assisted last-mile logistics are analysed, providing insights into the operational, regulatory, and technological challenges. The discussion is further expanded by addressing emerging trends in UAV technology, as well as innovations in drone deployment strategies and the evolving regulatory landscape. In conclusion, potential theoretical advancements and future applications of ULM systems are outlined, with an emphasis on integrating drones into broader logistics networks and smart city frameworks. The insights offered aim to guide future research and practical developments in this rapidly advancing field.

Prize-collecting Electric Vehicle routing model for parcel delivery problem

Electric Vehicle (EV)-based parcel delivery is getting much more popular these days due to EVs being eco-friendly and sustainable. One well-known problem in the literature to study last-mile delivery via EVs is the Electric Vehicle Routing Problem (EVRP). One limitation of EVRP is that it assumes all customers must be visited directly by EVs. This forces companies to purchase or utilize additional EVs, increasing operational costs for EV utilization and routing. To overcome this limitation, the present work introduces the Prize-collecting EVRP with Time Windows (PC-EVRP-TW), utilizing multiple EVs for the home-delivery service of high-priority customers (customers with high loyalty or premium memberships), leaving the rest for the next day or outsourcing to a third-party shipper. It also ensures a minimum number of served demands to maintain service quality. PC-EVRP-TW is modeled by mixed-integer linear programming and solved by a problem-specific hybrid metaheuristic aiming to minimize EVs’ route and usage cost and maximize collected profits (prizes) while satisfying EVs’ load and battery capacity, task completion, and time window constraints. Analyses were performed on the optimal solution of PC-EVRP-TW. The results show that the company could achieve significant route cost savings, up to 22% compared to EVRP-TW. Additionally, remarkable reductions in EV usage costs, 6%, 16%, and 18%, were observed for various-size instances of PC-EVRP-TW. These findings highlight the potential for practitioners and managers to realize substantial cost savings and optimize EV usage by selectively serving a prioritized subset of customers daily, particularly when facing high utilization costs and the inability to serve all customers directly.

From operational to strategic modelling: A continuous multi-scale approach for last-mile analysis

Supply chain planning requires decision-making at all levels, especially in the new normal of intervened supply networks. The integration of strategic network design and operational routing decisions has been widely studied in the literature as the location-routing problem (LRP). However, the LRP does not consider the differences in the planning horizon of each sub-problem, nor have geospatial elements been included in the distribution of demand. This paper aims to redesign the conceptual modelling of the last mile to inform strategic network design problems. A continuous multi-scale approach (CMA) is proposed by taking elements from the districting problem (DP) and continuous approximation (CA). This approach includes stochastic demand in the analysis and the effects of time windows and failed deliveries. The validation of CMA in a case of rural parcel deliveries in Belgium shows an estimate of the distance travelled similar to traditional routing algorithms in scenarios with high demand density. Likewise, the effects of time windows on the spatial configuration of multi-scale districting are explored. This approach provides insights for decision-making in strategic and tactical planning, such as sonification, differentiated services to satisfy consumer preferences, and fleet management. The limitations of the CMA lie in its implementation in purely operational scenarios since it does not offer detailed routing information. Subsequent research aims to exploit the potential of strategic last-mile modelling with effective integration into network design problems.

Analysis of the causal effect of the Covid-19 pandemic on parcel delivery: The case of Belo Horizonte, Brazil

The impact of the COVID-19 pandemic on e-commerce is evident and well-reported. However, few studies have measured the effect of restrictive measures on e-commerce. Therefore, this article aims to explore the causal effect of the COVID-19 pandemic on parcel deliveries. For this purpose, different techniques (interrupted time series analysis, counterfactual analysis, and structural equation modelling) were used to verify to what extent the number of cases and deaths from COVID-19 and the rigour of the restriction measures (measured by the stringency index) increased parcel deliveries in Belo Horizonte, Brazil. The results confirm the causal effect of the stringency index that positively influences parcel deliveries. The marginal effect of the stringency index on parcel deliveries was 5.44 deliveries per day during the pandemic compared to before the pandemic. This result means that each unit of stringency index increases the number of parcel deliveries by 5.44. The effects vary depending on the technique used; however, all showed the contribution of stringency index on parcel deliveries.

Towards greener city logistics: an application of agile routing algorithms to optimize the distribution of micro-hubs in Barcelona

The COVID-19 pandemic accelerated the shift towards online shopping, reshaping consumer habits and intensifying the impact on urban freight distribution. This disruption exacerbated traffic congestion and parking shortages in cities, underscoring the need for sustainable distribution models. The European Union's common transport policy advocates for innovative UFD approaches that promote intermodal transportation, reduce traffic, and optimize cargo loads. Our study addresses these challenges by proposing an agile routing algorithm for an alternative UFD model in Barcelona. This model suggests strategically located micro-hubs selected from a set of railway facilities, markets, shopping centers, district buildings, pickup points, post offices, and parking lots (1057 points in total). It also promotes intermodality through cargo bikes and electric vans. The study has two main objectives: (i) to identify a network of intermodal micro-hubs for the efficient delivery of parcels in Barcelona and (ii) to develop an agile routing algorithm to optimize their location. The algorithm generates adaptive distribution plans considering micro-hub operating costs and vehicle routing costs, and using heuristic and machine learning methods enhanced by parallelization techniques. It swiftly produces high-quality routing plans based on transportation infrastructure, transportation modes, and delivery locations. The algorithm adapts dynamically and employs multi-objective techniques to establish the Pareto frontier for each plan. Real-world testing in Barcelona, using actual data has shown promising results, providing potential scenarios to reduce CO2 emissions and improve delivery times. As such, this research offers an innovative and sustainable approach to UFD, that will contribute significantly to a greener future for cities.

Acquisition of Data on Kinematic Responses to Unpredictable Gait Perturbations: Collection and Quality Assurance of Data for Use in Machine Learning Algorithms for (Near-)Fall Detection.

Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in the traffic, transport and logistics sector, STF accidents are the most frequently reported occupational accidents. Therefore, an accurate detection of near-falls is critical to improve worker safety. Efficient detection algorithms are essential for this, but their performance heavily depends on large, well-curated datasets. However, there are drawbacks to current datasets, including small sample sizes, an emphasis on older demographics, and a reliance on simulated rather than real data. In this paper we report the collection of a standardised kinematic STF dataset from real-world STF events affecting parcel delivery workers and steelworkers. We further discuss the use of the data to evaluate dynamic stability control during locomotion for machine learning and build a standardised database. We present the data collection, discuss the classification of the data, present the totality of the data statistically, and compare it with existing databases. A significant research gap is the limited number of participants and focus on older populations in previous studies, as well as the reliance on simulated rather than real-world data. Our study addresses these gaps by providing a larger dataset of real-world STF events from a working population with physically demanding jobs. The population studied included 110 participants, consisting of 55 parcel delivery drivers and 55 steelworkers, both male and female, aged between 19 and 63 years. This diverse participant base allows for a more comprehensive understanding of STF incidents in different working environments.

Sharing a Ride: A Dual-Service Model of People and Parcels Sharing Taxis with Loose Time Windows of Parcels

(1) Efficient resource utilization in urban transport necessitates the integration of passenger and freight transport systems. Current research focuses on dynamically responding to both passenger and parcel orders, typically by initially planning passenger routes and then dynamically inserting parcel requests. However, this approach overlooks the inherent flexibility in parcel delivery times compared to the stringent time constraints of passenger transport. (2) This study introduces a novel approach to enhance taxi resource utilization by proposing a shared model for people and parcel transport, designated as the SARP-LTW (Sharing a ride problem with loose time windows of parcels) model. Our model accommodates loose time windows for parcel deliveries and initially defines the parcel delivery routes for each taxi before each working day, which was prior to addressing passenger requests. Once the working day of each taxi commences, all taxis will prioritize serving the dynamic passenger travel requests, minimizing the delay for these requests, with the only requirement being to ensure that all pre-scheduled parcels can be delivered to their destinations. (3) This dual-service approach aims to optimize profits while balancing the time-sensitivity of passenger orders against the flexibility in parcel delivery. Furthermore, we improved the adaptive large neighborhood search algorithm by introducing an ant colony information update mechanism (AC-ALNS) to solve the SARP-LTW efficiently. (4) Numerical analysis of the well-known Solomon set of benchmark instances demonstrates that the SARP-LTW model outperforms the SARP model in profit rate, revenue, and revenue stability, with improvements of 48%, 46%, and 49%, respectively. Our proposed approach enables taxi companies to maximize vehicle utilization, reducing idle time and increasing revenue.

Considering Socially Scalable Human-Robot Interfaces

Collaborative robots are becoming increasingly present in everyday life, with applications ranging food and parcel delivery, security, and more. They can offer great value propositions for organizations and consumers. However, most people lack knowledge of how to interact with robots, and many robots themselves necessitate formal training which can be inaccessible to many and thus not societally scalable. Further, there is a lack of existing work investigating interfaces designs that can support non-dyadic interactions consisting of two or more individuals interacting with a robot sequentially and simultaneously; such interactions will be common in real-world usage. This research explores the efficacy of natural language interfaces for human-robot interaction through a human experiment and post-experiment survey. Results show that the natural language interface can afford teams enhanced capabilities to share robot control and avoid errors relative to other interfaces, while also increasing user perceptions towards overall interaction.

Urban Air Mobility for Last-Mile Transportation: A Review

Urban air mobility (UAM) is a revolutionary approach to transportation in densely populated cities. UAM involves using small, highly automated aircraft to transport passengers and goods at lower altitudes within urban and suburban areas, aiming to transform how people and parcels move within these environments. On average, UAM can reduce travel times by 30% to 40% for point-to-point journeys, with even greater reductions of 40% to 50% in major cities in the United States and China, compared to land transport. UAM includes advanced airborne transportation options like electric vertical takeoff and landing (eVTOL) aircraft and unmanned aerial vehicles (UAVs or drones). These technologies offer the potential to ease traffic congestion, decrease greenhouse gas emissions, and substantially cut travel times in urban areas. Studying the applications of eVTOLs and UAVs in parcel delivery and passenger transportation poses intricate challenges when examined through the lens of operations research (OR). By OR approaches, we mean mathematical programming, models, and solution methods addressing eVTOL- and UAV-aided parcel/people transportation problems. Despite the academic and practical importance, there is no review paper on eVTOL- and UAV-based optimization problems in the UAM sector. The present paper, applying a systematic literature review, develops a classification scheme for these problems, dividing them into routing and scheduling of eVTOLs and UAVs, infrastructure planning, safety and security, and the trade-off between efficiency and sustainability. The OR methodologies and the characteristics of the solution methods proposed for each problem are discussed. Finally, the study gaps and future research directions are presented alongside the concluding remarks.

Rolling optimal scheduling for urban parcel crowdsourced delivery with new order insertion

The rapid development of mobile information technology has introduced numerous new solutions for delivery companies to enhance profits. One such solution employed by some companies is crowdsourced delivery. In this paper, we focus on rolling optimal scheduling for urban parcel crowdsourced delivery by utilizing private cars that will be in passing with the incorporation of new order insertion. The bonus incentive strategy is introduced to enhance the delivery probability of private car drivers. A static model and a rolling optimization model to maximize profits and the number of parcels delivered by private cars are established. To address the NP-hard problem, a hybrid genetic algorithm and insertion algorithm are designed. Numerical experiments are carried out to verify the proposed method in different scenarios, including the scattered network, clustered network, Dalian network, and Foursquare network. The computational results demonstrate that the method enhances the matching ratio and increases profits. Utilizing private cars that will be in passing for urban parcel delivery reduces the need for dedicated vehicles, mitigating traffic growth and alleviating traffic congestion. Increasing the private car-parcel ratio improves profits and the matching ratio while reducing traffic growth. Raising the incentive coefficient for bonuses increases the matching ratio and detour distance, but profits first increase and then decrease, and delivery distance by dedicated vehicles decreases. Our research findings offer a more rational basis for green urban parcel delivery decision-making by companies.

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.

Protecting Digital Society: Policies for Criminalizing Illegal Smartphone Applications Through Cyber Law Frameworks

This research aims to contribute to the development of criminal law policies addressing the widespread distribution of smartphone applications, both APK and iOS, that are used as tools for unauthorized online loan activities, online fraud, threats, and intimidation. These applications often disguise themselves as legitimate online loan platforms, wedding invitation apps, and parcel delivery services. Upon download, they illicitly debit mobile banking accounts or present themselves as government auction platforms for criminal goods at discounted prices, posing significant threats to consumers, debtors, and the general public. Theoretical framework: The foundation of this research lies in cyber law theory, which endeavors to create legal strategies to address cybercrimes and safeguard the digital community. This study utilizes a multidisciplinary approach, integrating legal analysis, policy assessment, and collaboration with stakeholders. Methods: This study utilized the normative juridical method to examine digital policies by criminalizing illegal smartphone applications through cyber law policies. This approach involves a systematic legal analysis of current regulations, assessing their effectiveness, and suggesting formal legal measures to address new threats posed by unauthorized applications, with an emphasis on protecting digital society from harm. Results and Conclusions: This indicates a pressing need for comprehensive criminal law policies specifically designed to effectively combat these unauthorized applications. Such policies are crucial in preventing and mitigating the harms caused by these applications before they impact society. The author's contribution lies in proposing formal offenses against the creators and disseminators of these applications. malicious applications, thereby bridging the current gap in legal measures to counteract digital criminal activities. Originality/Value: This research underscores the urgency of formulating and implementing cyber law policies to safeguard the public from the detrimental effects of illicit smartphone applications. Its originality lies in proposing innovative legal strategies to proactively address emerging digital threats.

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.

Does express delivery run ahead of stock price?

Abstract We study the role of logistics information in the stock price discovery process. Using a proprietary dataset on parcel-level express delivery service records from one of the largest express service providers in China, we find that express delivery contains firm-specific information for stock pricing. A long/short portfolio of buying (selling) stocks with high (low) quarterly growth in the number of parcels sent by firms generates an 8.23 percent risk-adjusted annual return. The return predictability is more significantly driven by document, light, and business-to-business parcels, which reflects the intensity of a firm’s overall operations (rather than just sales growth), and by parcels sent to new addresses, which represents new business expansion. Echoing this return predictability, parcel growth also predicts firm growth, profitability, and earnings surprises. Finally, we provide evidence on return predictability associated with topology of logistic network identified by parcel delivery data.