Parcel Delivery Research Articles

Siuntų pristatymo sektoriaus įmonių veiklos efektyvumo vertinimas COVID-19 pandemijos kontekste

The paper examines the impact of COVID-19 pandemic on operational effectiveness of parcel delivery sector companies operating in Lithuania. As demand of parcel delivery sector‘s services has increased, it is important to find out whether the impact of COVID-19 pandemic on operational effectiveness could be considered as positive even though the sector faces difficulties related to parcel delivery. The aim of the research is to evaluate the efficiency of parcel delivery sector companies operating in Lithuania during the COVID-19 pandemic. After the literature analyses, it was decided to examine the financial statements of 27 parcel delivery companies during the COVID-19 pandemic (2020-2021) and before the pandemic (2017-2019). During the investigation the impact of pandemic was analysed using the methods of correlation, regression, and logistics analysis. The main purpose of correlation analysis was to find connections between variables and by regression analysis to find causality between dependent and independent variables. The results of pandemic ware compared with results of pre-pandemic. After this, the results of pandemic have been applied in logistics analysis in forecasting process. Overall, it became clear that during the pandemic operational profit was more effective and costs were less effective comparing with results of pre-pandemic. In addition, the results of logistics analysis showed that there is a possibility that in the future operation profit will be ineffective and costs will be effective.

Prioritizing Outlier Parcels for Public Transport-Based Crowdshipping in Urban Logistics

The continuous growth of e-commerce puts pressure on logistic service providers to fulfill more parcel deliveries. Concurrently, there are increasing calls from governments and society to carry this out sustainably. Crowdshipping is one possible innovative logistic service that addresses these challenges. Crowdshipping enlists members of the public to fulfill parcel deliveries, ideally en route along their pre-committed journeys. In this paper, we consider a setting where public transport passengers can serve as crowdshippers and propose a comprehensive framework to organize the scheme. Firstly, outlier parcels are identified as being suitable for crowdshipping. Then, these outlying parcels are matched with crowdshippers, who pick up parcels from a set of selected parcel lockers. We also investigate the viability of crowdshipping with real-world data. By comparing a carrier’s performance to a base case without crowdshipping, the results show that delivery vehicle kilometers traveled and associated carbon dioxide emissions can reduce by up to 20%. A total of 11% of parcels can be redirected to be delivered via crowdshipping. Crowdshipping using public transport has the potential to be a sustainable way to fulfill urban logistics in a dense city.

The sustainable hybrid truck-drone delivery model with stochastic customer existence

Drone delivery is a fast delivery mode that has gained tremendous attention from academia and various companies in recent years. However, due to limited battery and payload capacities which may reduce the system's efficiency, it is better to coordinate ground vehicles and drones to take advantage of both trucks' large capacity and the drone's high speed. As a restriction for realistic parcel delivery systems, customer presence is only sometimes deterministic. For instance, a customer makes an order from an e-retailer, but due to various probable reasons, he cannot be present at home to get service. In this paper, we have introduced a sustainable hybrid truck-drone delivery model with stochastic customer presence. We have modeled the system with the Markov chain and proposed a linear mathematical model. This work processes with a heuristic and a Branch-and-Bound algorithm. Also, we have carried out numerous computational experiments to evaluate the proposed solution methods' performance, where the results show the efficiency of the proposed algorithms. Finally, we performed a detailed sensitivity analysis on a case study and studied various aspects of the problem. The results highlight that truck and drone coordination reduces completion time, operational costs, truck emissions, and social penalties.

An adaptive large neighborhood search metaheuristic for a passenger and parcel share-a-ride problem with drones

With the increasing concerns about traffic congestion and climate change, much effort has been made to enhance sustainable urban mobility for passengers and goods. One emerging promising strategy is to transport passengers and goods in an integrated manner, as it could reduce the number of vehicles on the road compared with the separate transportation of passengers and goods. This study proposes the simultaneous transportation of passengers and goods using demand-responsive buses and drones. Compared with the prevalent strategies that rely only on ground vehicles to integrate passenger and parcel transportation, we propose the joint usage of ground vehicles and drones to transport passengers and deliver parcels. The ground vehicles for passenger and parcel delivery are on-demand buses, which combine the advantages of the flexibility of taxis and the large capacity of public transport modes. The drones automatically take off from and land on the on-demand buses’ rooftops and are only for parcel delivery. A new optimization problem that designs the routes for both demand-responsive buses and drones is proposed and denoted as the passenger and parcel share-a-ride problem with drones (SARP-D). A mixed-integer nonlinear programming model is devised; the nonlinearity exists because drone launch/recovery can occur simultaneously with request servicing by a bus at the same node. To solve the model for large-scale instances, we develop an adaptive large neighborhood search metaheuristic. Numerical experiments are conducted to validate the correctness of the model and evaluate the efficiency of the metaheuristic. Moreover, sensitivity analyses are performed to explore the influences of the maximum number of intermediate stops during one passenger request service, the drone flight endurance, and the unit delay penalty on the total cost, which comprises the transportation and delay costs.

Sustainable last mile parcel delivery and return service using drones

Improving last mile delivery, in terms of both efficiency and sustainability, has recently been enhanced using truck/drone tandems. However, nearly all these approaches focus only on deliveries, while returns are ignored. We propose a new model for integrating both delivery and returns in the combined operation of a truck and a drone, which we term the Flying Sidekick Traveling Salesman Problem integrating Deliveries and Returns with Multiple Payloads (FSTSP-DR-MP). This approach is more sustainable than truck-only deliveries as the drones operate through battery power with no emissions and can move more directly than vehicles along a road network. We formulate the problem as a mixed-integer linear program to minimize the total service time of the system, where the truck and the drone can perform both delivery and pickup of parcels during a single sortie. Because drones have a capacity of multiple parcels, each can visit more than one customer per dispatch, increasing drone utilization and responsiveness to customer expectations regarding return services, along with greater environmental improvements. Small-size cases are solved exactly using the MILP implemented in the CPLEX Python API. Since the MILP is not practical for realistically sized cases, we propose a meta-heuristic derived from Variable Neighborhood Search (VNS), which iteratively builds truck and drone routes. We show that this works well for up to 100 customers, a typical number in last-mile logistics. We assess the trade-offs between the ratio of returns to deliveries and drone capacity to provide managerial insights to the benefits of this approach for sustainable last mile logistics. Computational experiments show that integrating delivery and return truck-drone operations significantly reduces total service time and truck travel time compared to both traditional delivery schemes (single truck) and the well-known FSTSP drone schemes (one truck and one drone) up to 36.2% and 22.9%, respectively, by exploiting the nature of each customer (delivery or return) to increase the number of stops of each drone sortie. We also conduct a comparison with multiple drones have a single payload under similar scenarios. The results demonstrate that our model outperforms this alternative with an average improvement of 3.9% in total service time. Our approach addresses an important gap in the literature by accommodating returns, which are ubiquitous in last mile logistics, as well as providing for improved drone utilization, sustainability, and cost-effectiveness.

A two-stage data-driven metaheuristic to predict last-mile delivery route sequences

On delivery operations, vehicle routing problems regularly focus on optimizing route-solution quality. However, the real-life execution of routes often differs from the computed routing plan. Therefore, the routing algorithms should use information from historic routes and drivers’ decisions to make the planned route as similar as possible to the actual executed route. We study the problem of predicting route sequences that drivers follow in last-mile parcel delivery in the context of the 2021 Amazon Last-Mile Routing Research Challenge. We propose extracting information from historic routes using a probability estimation method that learns transition probabilities between clusters or zones, coupled with a simple yet effective two-stage Greedy Randomized Adaptive Search Procedure (GRASP) to incorporate the information from historical route data into route planning. Our GRASP includes components in the objective function that consider decisions observed on the historical route sequences supplied by Amazon. We evaluate the predicted route sequences using the score similarity metric provided in the Amazon challenge. The results show that our method obtains a similarity score of 0.0328, which is comparable to the scores achieved by the three winning teams of the Amazon challenge. The proposed method successfully uses historical routes to predict route sequences on a last-mile delivery setting.

Lack of Collaboration on the CEP Market and the Underlying Reasons—A Systematic Literature Review

The scientific community has been addressing the topic of last-mile delivery for years. To improve parcel delivery efficiency, a variety of different technologies have been created. Over the past 15 years, the focus has shifted from the operational efficiency of the individual organization to restoring sustainability and making cities more livable. As a result of the increased environmental burden, governments are enacting a growing number of restrictive measures, which will intensify economic challenges. To remain competitive, more cost-effective solutions are required. The goal of this article is to examine the significance of collaboration between CEP partners based on scientific interest, with the help of a systematic literature review. This examination is important since, despite the fact that working together with other service providers and competitors could be a favorable option for last mile suppliers looking to improve their efficiency, results show little interest in this approach. Although this strategy appears straightforward due to the potential financial and environmental benefits, there are only a few examples of collaboration in the field of last mile parcel delivery according to the results of the review. Since cooperation seems to be an inevitable operating model of the CEP market in the future, it is of utmost importance for scientific research to investigate the factors hindering the development of cooperation.

Improving last-mile parcel delivery through shared consolidation and portering: A case study in London

Based on the insights derived from practical trials conducted with carriers operating in London, this article proposes a last-mile delivery model that involves both driving and walking. A particularly innovative aspect of the model is the shared use of a third-party portering service between the carriers as an enabler to collaborative provision, and shared micro-consolidation points for temporary storage of goods. The model gives rise to a complex routing problem, for which a tabu search algorithm is developed. The algorithm is able to suggest vehicle routes that consolidate parcels in shared locations and porter paths that deliver items from various companies, reducing the overall delivery cost. We applied this algorithm to a manifest dataset obtained from two carriers operating in London. The results from the model suggested that sharing a third party portering service for the last-mile delivery element between the rival carriers in the case study reduced overall transport costs by up to 14%.

Robotic System for Post Office Package Handling

Parcel sorting is becoming a significant challenge for delivery distribution centers and is mostly automated by using high-throughput sorting machinery, but manual work is still used to feed these machines by placing the parcels on the conveyor belt. In this paper, an AI-based robotic solution that automates the parcel placement task was developed. The architecture of the proposed system along with methods on how to implement it are described by using the currently available hardware and software components. The described choices lead to a well-functioning system and the gained insights will facilitate building similar systems for parcel delivery automation.

A variable neighborhood search for parcel delivery by vehicle with drone cycles

In spite of the growing literature on and relevance of vehicle-drone parcel delivery, the logistical impact of cyclic drone flights, in which a drone launches and lands at the same node (as opposed to distinct sites) while delivering to a customer in between, remains unclear. To assess the pertinence and logistical impact of drone cycles, we propose a variable neighborhood search (VNS) heuristic for the Traveling Salesman Problem with Drone (TSP-D), whereby a vehicle and its companion aerial drone are synchronously routed to deliver customer orders with the objective of minimizing the return time of both carriers to the depot. The key to the success of the proposed VNS is a two-phase intensification scheme. In the first phase, the VNS broadly explores the feasible space by temporarily limiting the scope of drone flights and rendezvous locations. In the second phase, two features are introduced to ensure a deeper exploration of the feasible space: (i) intervening visits to customers are allowed for the vehicle between the drone rendezvous (launch and re-collect) nodes and (ii) drone operations may include no cycles, single cycles, or multiple cycles. The VNS is powered by optimization models that may accommodate the diverse operational settings proposed in the TSP-D literature. Over a set of benchmark instances, the VNS improves upon the best-known results for 113/120 instances having up to 100 nodes with comparable computational effort to existing approaches. The VNS also reveals improvements of up to 1%–8% in delivery times when drone multi-cycles are permitted, over a test-bed of diverse customer topographies and instance sizes.

Development of Heuristic Approaches for Last-Mile Delivery TSP with a Truck and Multiple Drones

Unmanned Aerial Vehicles (UAVs) are gaining momentum in many civil and military sectors. An example is represented by the logistics sector, where UAVs have been proven to be able to improve the efficiency of the process itself, as their cooperation with trucks can decrease the delivery time and reduce fuel consumption. In this paper, we first state a mathematical formulation of the Travelling Salesman Problem (TSP) applied to logistic routing, where a truck cooperates synchronously with multiple UAVs for parcel delivery. Then, we propose, implement, and compare different sub-optimal routing approaches to the formulated mFSTSP (multiple Flying Sidekick Travelling Salesman Problem) since the inherent combinatorial computational complexity of the problem makes it unattractable for commercial Mixed-Integer Linear Programming (MILP) solvers. A local search algorithm, two hybrid genetic algorithms that permutate feasible and infeasible solutions, and an alternative ad-hoc greedy method are evaluated in terms of the total delivery time of the output schedule. For the sake of the evaluation, the savings in terms of delivery time over the well-documented truck-only TSP solution are investigated for each proposed routing solution, and this is repeated for two different scenarios. Monte Carlo simulations corroborate the results.

Urban Logistics through River: A Two-Echelon Distribution Model

Studies that use rivers in a last-mile delivery context are scarce. This research considers the first multimodal alternative based on a barge for parcel delivery activities. It proposes two sustainable network designs for a two-echelon distribution. The efficiency of scenarios is assessed through performance indicators. A three-stage decomposition heuristic is used. Allocation of the customers to the closest satellite at the first stage uses a non-supervised machine learning clustering method, 2D-k-means. The last two stages, comprising the two echelons routing, are solved using a heuristic based on the nearest neighbor procedure. The fixed costs decrease by 41% and energy consumption by 92% when applying a river transportation mode and e-cargo bikes in the distribution network’s first and second echelon, respectively. Future research avenues are to render the results more realistic with the consideration of other costs and a larger network.

Sustainability Aspects of Drone-Assisted Last-Mile Delivery Systems—A Discrete Event Simulation Approach

The goal of this article is to examine the advantages and disadvantages of the application of drones in last-mile delivery systems from a sustainability point of view regarding CO2 emissions and energy consumption. As commercial drones are developing rapidly, the application of such tools in the field of last-mile delivery and transportation can offer many opportunities to increase service flexibility, reduce delivery time and decrease CO2 emissions and energy consumption. In this article, a discrete event simulation is applied to examine how the assistance of drones in parcel delivery services can influence the sustainability aspects of such services regarding CO2 emissions and energy consumption in an urban environment. Based on factory parameters, a vehicle-based delivery scenario is compared to a drone-assisted scenario under ideal conditions. According to the results, within the simulation parameters with the assistance of drones, a decrease in CO2 emissions and energy consumption is possible in last-mile delivery services, but more environmental, technological and financial limitations should also be addressed and incorporated to determine whether such a development is worthwhile from a last-mile delivery company’s point of view.

Evaluation of a Team-Based Collection and Delivery Operation

The rise in the volume of e-commerce is adding increasing pressure on the logistics of parcel delivery. To improve the efficiency of their operations, the parcel industry in Japan is exploring team-based collection and delivery (TCD), whereby the sales driver (SD) hands out parcels to the field crews (FC), who subsequently deliver them to the door. However, the efficiency of TCD is still understudied. This study proposes a method for optimizing the delivery route for TCD and evaluates the efficiency of the ongoing operation. The TCD delivery problem focuses on minimizing the task completion time using parameters derived through surveys of the actual operations. Comparison between seven different methods show that the newly proposed method of fuzzy c-means clustering with a genetic algorithm outperforms the rest, rapidly computing sufficiently accurate results. Results suggest that the proposed optimal route reduces the total delivery time by up to 18.7%. However, the amount of time saved varies considerably by the area and the number of parcels delivered. Additional constraints for improving driver safety, the cost-benefit of increasing FCs, and the impact on the environmental cost are also considered. The proposed method also helps spread the workload and the travel time of the FCs more evenly, thus further reducing the total delivery time.

Divide and Conquer: A Location-Allocation Approach to Sectorization

Sectorization is concerned with dividing a large territory into smaller areas, also known as sectors. This process usually simplifies a complex problem, leading to easier solution approaches to solving the resulting subproblems. Sectors are built with several criteria in mind, such as equilibrium, compactness, contiguity, and desirability, which vary with the applications. Sectorization appears in different contexts: sales territory design, political districting, healthcare logistics, and vehicle routing problems (agrifood distribution, winter road maintenance, parcel delivery). Environmental problems can also be tackled with a sectorization approach; for example, in municipal waste collection, water distribution networks, and even in finding more sustainable transportation routes. This work focuses on sectorization concerning the location of the area’s centers and allocating basic units to each sector. Integer programming models address the location-allocation problems, and various formulations implementing different criteria are compared. Methods to deal with multiobjective optimization problems, such as the ϵ-constraint, the lexicographic, and the weighted sum methods, are applied and compared. Computational results obtained for a set of benchmarking instances of sectorization problems are also presented.

An investigation on consumers' preferences for parcel deliveries: applying consumer logistics in omni-channel shopping

PurposeOmni-channel shopping affords consumers a variety of delivery options to receive products based on their preferred times and locations. By considering consumers' contributions (physical, social and attentive efforts) in co-creating delivery services, this study investigates their preferences for parcel delivery.Design/methodology/approachA scenario-based questionnaire survey is conducted for data collection in Singapore (n = 483). Furthermore, a multinomial logistic regression is performed to assess consumers' choice mode of delivery among five alternatives, that is attended home delivery, unattended home delivery, automated self-collection locker, attended pickup point and click-and-collect.FindingsCompared to attended home delivery, consumers who choose the alternatives are found to be more willing to contribute physical effort but less interested in responding attentively to informational updates. Efforts required for social interactions discourage consumers from choosing attended deliveries, prompting unattended alternatives (e.g. home delivery and self-collection) as more attractive choices. Additionally, socio-demographic factors and product value also influence consumers' preferences.Originality/valueThis study contributes to the literature by integrating the theoretical concept of consumer logistics into omni-channel studies, providing a new approach to examining consumers' channel behaviour. With detailed profiling that links product value and consumers' socio-demographics to their choice mode of delivery, the authors create practical insight into the optimal design of omni-channel distribution systems that best harness consumers' voluntary contributions.

A new truck-drone routing problem for parcel delivery by considering energy consumption and altitude.

The high population density in metropolitan areas, high-rise buildings, and changes in people's lifestyles have completely changed the way postal packages are delivered. People no longer go to the ground floor to receive a postal package. In the meantime, the delivery of postal packages through the balconies and windows of the units on the upper floors of the buildings will gradually become inevitable. Hence, a new Vehicle Routing Problem with Drone mathematical model has been developed with the objective of minimizing total delivery time and with the ability to deliver postal packages in the path of drones at different heights. In addition, the drone's energy consumption is computed by taking into account wind speed, the weight of the postal parcel, the weight of the drone's body, and other factors in the drone's path. A two-phase algorithm based on the nearest neighborhood and local search is presented to solve the developed mathematical model in different instances. Several small-sized test problems are designed and solved, and the performance of the heuristic approach is evaluated compared to the outputs of the CPLEX solver. Finally, the proposed model is implemented on a real-world scale to demonstrate the efficacy and applicability of the proposed model as well as the heuristic approach. The results show that the model successfully finds the optimal planning of the delivery routes, especially when we deal with delivery points at different heights.

Modeling spatial dimensions of parcel delivery demand and its determinants

ABSTRACT This study aims to investigate the spatial dimensions of parcel delivery demand and its key determinants. Based on the parcel delivery volume data in South Korea, we regressed per capita parcel delivery volume on socioeconomic, land use, housing, and industry-related variables of city-level districts. When covering the entire country, Spatial Durbin Error Model worked best in modelling the spatial dependence in the disturbance and the spatial spillover effect of the explanatory variables in the neighbouring districts. When performing separate regression analyses for urban and non-urban areas, we identified a different relationship between parcel demand and its determinants in two subgroups. Given the rapid development of e-commerce, establishing logistics infrastructure for supporting home delivery services, particularly in urban areas, has become challenging. This study can contribute to improved prediction of parcel demand that varies over space, suggesting region-specific policies for establishing local parcel delivery infrastructure such as sub-terminals.

An Integer Programming Based Approach to Delivery Drone Routing under Load-Dependent Flight Speed

Delivery drones have been attracting attention as a means of solving recent logistics issues, and many companies are focusing on their practical applications. Many research studies on delivery drones have been active for several decades. Among them, extended routing problems for drones have been proposed based on the Traveling Salesman Problem (TSP), which is used, for example, in truck vehicle routing problems. In parcel delivery by drones, additional constraints such as battery capacity, payload, and weather conditions need to be considered. This study addresses the routing problem for delivery drones. Most existing studies assume that the drone’s flight speed is constant regardless of the load. On the other hand, some studies assume that the flight speed varies with the load. This routing problem is called the Flight Speed-Aware Traveling Salesman Problem (FSTSP). The complexity of the drone flight speed function in this problem makes it difficult to solve the routing problem using general-purpose mathematical optimization solvers. In this study, the routing problem is reduced to an integer programming problem by using linear and quadratic approximations of the flight speed function. This enables us to solve the problem using general-purpose mathematical optimization solvers. In experiments, we compared the existing and proposed methods in terms of solving time and total flight time. The experimental results show that the proposed method with multiple threads has a shorter solving time than the state-of-the-art method when the number of customers is 17 or more. In terms of total flight time, the proposed methods deteriorate by an average of 0.4% for integer quadratic programming and an average of 1.9% for integer cubic programming compared to state-of-the-art methods. These experimental results show that the quadratic and cubic approximations of the problem have almost no degradation of the solution.

Best-response planning for urban fleet coordination

The modeling of fleet vehicles as self-interested agents brings a realistic perspective to open fleet transportation research. This feature allows us to model the fleet operation from a non-cooperative point of view. In this work, we study parcel delivery in a city with limited resources (roads and charging stations). We designed and implemented a system composed of a multi-agent planner and a game-theoretic coordination algorithm: a Best-Response Fleet Planner. The system allows for the self-organization of the transportation system by coordinating a fleet of self-interested electric vehicles. The system’s operation is optimized together with resource usage while preserving the agents’ private interests, allowing each agent to plan its actions. The results show that our system has higher scalability than similar approaches, allowing it to function for a considerable number of agents in settings that feature congestion and conflicts. Additionally, overall solution quality is improved compared to other coordination systems, reducing congestion and avoiding unnecessary waiting times.