Last-mile Delivery Problem Research Articles

Demand Steering in a Last-Mile Delivery Problem with Home and Pickup Point Delivery Options

To increase the efficiency of last-mile delivery, online retailers can adopt pickup points in their operations. The retailer may then incentivize customers to steer them from home to pickup point delivery to reduce costs. However, it is usually uncertain whether the customer accepts this incentive to switch to pickup delivery. This setup gives rise to a new last-mile delivery problem with integrated incentive and routing decisions under uncertainty. We model this problem as a two-stage stochastic program with decision-dependent uncertainty. In the first stage, a retailer decides which customers to incentivize. However, customers’ reaction to the incentive is stochastic: they may accept the offer and switch to pickup point delivery, or they may decline the offer and stick with home delivery. In the second stage, after customers’ final delivery choices are revealed, a vehicle route is planned to serve customers via the delivery option of their choice. We develop an exact branch-and-bound algorithm and propose several heuristics to improve the algorithm’s scalability. Our algorithm solves instances with up to 50 customers, realizing on average 4%–8% lower last-mile delivery costs compared with the commonly applied approaches in the industry that do not use incentives or offer incentives to all customers. We also develop a benchmark policy that gives very fast solutions with a 2% average optimality gap for small instances and up to 2% average cost increase compared with the heuristic solutions. Funding: This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement [Grant 765395]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0287 .

Parallel drone scheduling vehicle routing problems with collective drones

We study last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers. In particular, we focus on problem settings where either a single truck or a fleet with several homogeneous trucks work in parallel to drones, and drones have the capability of collaborating for delivering missions. This cooperative behavior of the drones, which are able to connect to each other and work together for some delivery tasks, enhance their potential, since connected drone has increased lifting capabilities and can fly at higher speed, overcoming the main limitations of the setting where the drones can only work independently.In this work, we contribute a Constraint Programming model and a valid inequality for the version of the problem with one truck, namely the Parallel Drone Scheduling Traveling Salesman Problem with Collective Drones and we introduce for the first time the variant with multiple trucks, called the Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones. For the latter version of the problem, we propose two Constraint Programming models and a Mixed Integer Linear Programming model.An extensive experimental campaign leads to state-of-the-art results for the problem with one truck and some understanding of the presented models’ behavior on the version with multiple trucks. Some insights about future research are finally discussed.

Rewards, Penalties and Real Case References: Improvement of the Last Mile Delivery in USA

The growth of the platform economy has fueled the rapid growth of the cargo transportation industry. However, while it continues to scale up, many problems of the existing freight transportation industry are being exposed - environmental damage, harming consumers, and even depriving producers of profits. The last-mile problem is particularly significant. The United States and China, two of the world's largest countries, have representative platform economy enterprises, namely Amazon and Taobao. These two companies' different freight models represent the two existing mainstream freight models in the world - free home delivery and stagecoach containers. By comparing the two companies' existing last-mile delivery problems and analyzing the two countries' different cultural backgrounds, this paper tries to solve the existing last-mile problems by introducing reward and punishment mechanisms and applying the two models to each other. In this way, enterprises in each country can learn from each other's distribution models and find the optimal solution for the platform economy distribution model.

Stable Matching for Crowdsourcing Last-Mile Delivery

This study investigates a crowdsourcing last-mile delivery problem considering orders with different destinations and time windows and crowdsourced drivers with preplanned trips. In this problem, customers announce requests on a crowdsourced delivery platform to deliver orders from a depot to their destinations and crowdsourced drivers are willing to make a detour to deliver orders in exchange for rewards provided by the platform. The crowdsourced drivers have preference lists over groups of orders based on their profits and meanwhile each order has a preference list over crowdsourced drivers based on the drivers’ arrival times at the depot. According to their preference lists, to maximize profits, crowdsourced drivers need to consider routing and scheduling decisions for delivering orders. This crowdsourced driver-order matching problem is considered to be a non-cooperative game between crowdsourced drivers. A Nash equilibrium for such a non-cooperative game is a stable matching between crowdsourced drivers and orders. We propose two exact algorithms to find stable matchings and develop a heuristic algorithm to find feasible matchings for large scale problem. The results from computational experiments demonstrate that the proposed approaches are highly efficient and effective, and the matching rate of stable matching is larger than that of feasible matching. Finally, we investigate two extensions to demonstrate the applicability of our methods and also extend to a stochastic setting with random release times of orders.

A Novel Two-Phase Approach for Optimization of the Last-Mile Delivery Problem with Service Options

As the growth of e-commerce continues to accelerate, there is a need for new and innovative strategies in last-mile delivery to meet the changing demands of customers. The main objective of this study is to address this need by optimizing the last-mile delivery problem with service options (LMDPSOs) through a novel two-phase approach that considers various delivery options such as home delivery, self-pickup, and delivery at different prices. This original approach enables simultaneous optimization of the selection of the most appropriate pickup and delivery points and determination of the most efficient vehicle routing. The LMDPSOs reduces overall costs, minimizes environmental impact, and considers customer satisfaction levels by determining the most appropriate trips according to the available service options. This research employs a two-phase methodology for decision making. The first phase determines the value of the proposed locations through a novel multi-criteria decision-making (MCDM) approach that incorporates sustainability criteria. In the second phase, a tailored mathematical model is proposed for vehicle routing with service options. The model is coded in the CPLEXsoftware version 12.6 in various dimensions. We evaluate the potential and advantages of diverse delivery choices, illustrating that aggregating orders at pickup and delivery points can reduce delivery costs and minimize environmental impact. Additionally, this paper directs managers in selecting the most appropriate delivery method for last-mile delivery, considering environmental, social, and economic factors.

The cost of carrier consistency: Last-mile delivery by vehicle and drone for subscription-based orders

We investigate a deterministic last-mile delivery problem in which a firm delivering scheduled orders determines a subset of customers to be prioritized for drone delivery, relegating the rest to vehicle delivery. The vehicle and drone deliver in tandem over a planning horizon. The problem is amenable to a multi-day Traveling Salesman Problem with Drone, with consistency in the assignment of customers to each carrier across all days. We develop a mixed-integer program (MIP) that yields provably optimal solutions for instances having up to 40 customers over a 6-day horizon. We also devise a heuristic underpinned by the MIP to effectively and accurately prioritize customers for drone delivery. A computational study involving instances with up to 150 customers ordering over 6 days shows the matheuristic often matching the results of the proposed MIP, and also reveals a logistical burden of less than 0.5% on average over a solution lacking carrier consistency..

A Cyber-Physical System's Roadmap to Last-Mile Delivery Drones

According to recent studies, unmanned aerial vehicles can play a game-changing part in cost reduction and speed increase to address the last-mile delivery problem and attend to emergencies. Last-mile delivery services are getting more and more relevant, especially when in times when social distance is required. However, a systemic view of how to provide a feasible solution to enable this application as a vertical market in the urban context is still inexistent. Given this scenario, our paper contributes by proposing a cyber-physical system roadmap applicable to last-mile delivery drones. The proposed CPS guidelines are based on the system of systems to enable an enhanced operation towards smart cities' governance. This paper also discusses topics from air space control and reservation to communication infrastructure and decentralized control supported on a blockchain.

Joint optimization of parcel allocation and crowd routing for crowdsourced last-mile delivery

Urban last-mile delivery providers are facing more and more challenges with the explosive development of e-commerce. The advancement of smart mobile and communication technology in recent years has stimulated the development of a new business model of city logistics, referred to as crowdsourced delivery or crowd-shipping. In this paper, we investigate a form of crowdsourced last-mile delivery that utilizes the journeys of commuters/travelers (crowd-couriers) to deliver parcels from intermediate stations to customers. We consider a logistics service provider that jointly optimizes parcel allocation to intermediate stations and the delivery routing of the crowd-couriers. The joint optimization model gives rise to a new variant of the last-mile delivery problem. We propose a data-driven column generation algorithm to solve the problem based on a set-partitioning formulation. Additionally, a rolling-horizon approach is proposed to address large-scale instances. Extensive numerical experiments are conducted to verify the efficiency of our model and solution approach, as well as the significance of the joint optimization of parcel allocation and the delivery route of the crowdsourced last-mile delivery. The results show that our data-driven column generation algorithm can obtain (near-)optimal solutions for up to 200 parcels in significantly less time than the exact algorithm. For larger instances, the combination of the data-driven column generation algorithm and the rolling-horizon approach can obtain good-quality solutions for up to 1000 parcels in 15 min. Moreover, compared with crowd-courier route optimization only, the joint optimization of parcel allocation and crowd-routing reduces the total cost by 32%.

Energy Efficient UAV-Based Last-Mile Delivery: A Tactical-Operational Model With Shared Depots and Non-Linear Energy Consumption

This paper studies the drone-aided last-mile delivery problem with shared depot resources. Our research motivation comes from E-commerce logistics, where big companies such as Amazon, are already filing up patents for the development of drone-friendly fulfillment centers towers that could serve as both charging hubs and convenient pit stops for delivery drones to pick up and drop off packages efficiently. We mainly focus on the tactical decisions about the selection of shared fulfillment centers used as the drone launch and retrieve stations and the fleet size plans. The operational drone route decisions are also incorporated into a unified framework to account for the mutual impact between tactical and operational plans. Moreover, we consider explicitly the non-linear and load-dependent nature of the energy consumption function for drone batteries. The problem is formulated as a mixed integer program with linear constraints, developed in the realm of layered networks, where the non-linear nature of energy consumption and its load dependency are incorporated and efficiently handled without the need of approximating non-linear terms. The proposed model is tested on an extensive set of instances with up to 75 customers, showing its computational efficiency. Insights about the route costs and spatial configuration of depots are also discussed.

Multi-Criteria Simulation-Optimization Analysis of Usage of Automated Parcel Lockers: A Practical Approach

The rapid growth of electronic commerce is having an impact on the way urban logistics are organized. In metropolitan settings, the last-mile delivery problem, i.e., the problem regarding the final stage of delivering a shipment to a consumer, is a major concern due to its inefficiency. The development of a convenient automated parcel lockers (APLs) network improves last-mile distribution by reducing the number of vehicles, the distances driven, and the number of delivery stops. Using automated parcel lockers, the last-mile issue could be overcome for the environment’s benefit. This study aimed to define and validate an APL network containing hundreds of APLs with the use of an example made up of real case study data from the city of Poznań in Poland. The goal of this research was to use mathematical programming for optimization and simulation to tackle the facility location problem for automated parcel lockers through a practical approach. Multi-criteria simulation-optimization analysis was used to assess the data. In fact, the simulation was carried out using Anylogic software and the optimization with the use of the Java programming language and CPLEX solver. Three years were simulated, allowing for comparable results for each year in terms of expenses, e-shoppers, APL users, and demand evolution, as well as achieving the city’s optimal locker usage. Finally, encouraging conclusions were obtained, such as the relationship between the demand and the number of lockers, along with the model’s limitations.

Application of Unmanned Aerial Vehicles in Logistics: A Literature Review

The booming development of e-commerce has brought many challenges to the logistics industry. To ensure the sustainability of the logistics industry, the impact of environmental and social sustainability factors on logistics development needs to be considered. Unmanned Aerial Vehicles (UAVs)/drones are used in the logistics field because of their flexibility, low cost, environmental protection and energy-saving advantages, which can achieve both economic benefits and social benefits. This paper reviews 36 studies on UAVs applications in logistics from the Web of Science database from the past two years (2021–2022). The selected literature is classified into theoretical models (the traveling salesman problem and other path planning problems), application scenarios (medical safety applications and last-mile delivery problems) and other problems (UAV implementation obstacles, costs, pricing, etc.). Finally, future directions of UAVs are proposed, such as different application scenarios that can be considered and different algorithms that can be combined to optimize paths for UAVs to specific flight environments.

2-echelon lastmile delivery with lockers and occasional couriers

We propose a new approach for the lastmile delivery problem where, besides serving as collecting points of orders for customers, parcel lockers are also used as transshipment nodes in a 2-echelon delivery system. Moreover, we consider that a customer (occasional courier) visiting a locker may accept a compensation to make a delivery to another customer on their regular traveling path.The proposed shared use of the locker facilities – by customers that prefer to self-pick up their orders, and also as a transfer deposit for customers that prefer home delivery – will contribute to better usage of an already available storage capacity. Furthermore, the use of occasional couriers (OCs) brings an extra layer of flexibility to the delivery process and may positively contribute to achieving some environmental goals: although non-consolidation of deliveries may, at first sight, seem negative, by only considering OCs that would go to the locker independently of making or not a delivery on their way home, and their selection being constrained by a maximum detour, the carbon footprint can be potentially reduced when compared to that of dedicated vehicles.We present a mixed-integer linear programming formulation for the problem that integrates three delivery options – depot to locker, depot to locker followed by final delivery by a professional fleet, and depot to locker followed by final delivery by an OC. Furthermore, to assess the impact of OCs’ no show on the delivery process, we extend the formulation to re-schedule the delivery of previous undelivered parcels, and analyze the impact of different no-show rates.Thorough computational experiments show that the use of OCs has a positive impact both on the delivery cost and on the total distance traveled by the dedicated fleets. Experiments also show that the negative impact of no-shows may be reduced by using lockers with higher capacities.

A stochastic-fuzzy multi-objective model for the last-mile delivery problem using drones and ground vehicles, a case study

Drone delivery as a novel approach for parcel delivery has been under the focus of many scholars and practitioners. In this regard, this paper introduces a stochastic-fuzzy multi-objective optimization model for designing a last-mile delivery system with drones and ground vehicles. The first two objective functions aim to minimize the detrimental effects of the delivery system on the environment and the total costs. The last objective function maximized the system's reliability by considering the breakdown probability of both drones and ground vehicles. Then, AUGMECON2 is utilized as an exact method to solve the proposed model. besides determining the number of required drones and ground vehicles, the model indicates locations and capacities of facilities where vehicles start their one-to-one trips to meet the customer demands. The proposed model is then validated by applying it to a real case study of an e-commerce company in Karaj, Iran. The findings suggest that the system's total cost rises when the reliability increases and the environmental impacts decrease. Furthermore, when both drones and ground vehicles are considered for meeting the customer demands, the delivery system functions better in terms of costs, environmental impacts, and reliability than when only one mode of delivery is considered.

Robot-Based Last-Mile Deliveries With Pedestrian Zones

Since delivery robots share sidewalks with pedestrians, it may be beneficial to choose paths for them that avoid zones with high pedestrian density. In this paper, we investigate a robot-based last-mile delivery problem considering path flexibility given the presence of zones with varying pedestrian level of service (LOS). Pedestrian LOS is a measure of pedestrian flow density. We model this new problem with stochastic travel times and soft customer time windows. The model includes an objective that reflects customer service quality based on early and late arrivals. The heuristic solution approach uses the minimum travel time paths from different LOS zones (path flexibility). We demonstrate that the presence of pedestrian zones leads to alternative path choices in 30% of all cases. In addition, we find that extended time windows may help increase service quality in zones with high pedestrian density by up to 40%.

Hybrid Truck-Drone Delivery Systems: A Systematic Literature Review

Owing to the continued development of e-commerce, logisticians now have an outstanding obligation to tackle last-mile delivery challenges. A number of logistics providers have suggested the incorporation of drones with trucks to provide a more flexible delivery system. This paper analyzes the content of 95 publications related to hybrid truck-drone delivery systems (HTDDS) in the context of last-mile delivery. First, a brief overview of the potential implementation of drone delivery systems is presented, including their integration with other vehicles. The overview aims to demonstrate the operational characteristics of such systems and their implications. Then, the surveyed literature is classified based on vehicles roles, system configuration, problem formulation, and solution methods. In relation to this research, several key findings and potential research directions are discussed. Despite the high level of interest in HTDDS research, it is still in its early phases and requires improvements in various areas. The payload capacity, speed, range, and energy consumption are all factors that must be considered in the modeling of drone characteristics. Almost all studies identify customer requests before the delivery operation begins. However, customer demands for immediate delivery present an opportunity for real-time optimization to provide solutions for e-commerce activities. Environmental issues are developing, as the last-mile delivery problem is regarded as the most polluting portion of the supply chain. Thus, more consideration should be given to the environmental impact of HTDDS. Finally, research on drone routing related to air traffic management has received relatively little attention.

Drone-Assisted Last-Mile Delivery Problem by Covering Salesman Problem with Nodes and Segments

Drone-Assisted Last-Mile Delivery Problem by Covering Salesman Problem with Nodes and Segments

Vehicle Routing Problem When Using UAVs

Introduction. In recent years, the use of unmanned aerial vehicles (UAVs) is growing rapidly. Initially introduced for military purposes, drones and related technologies have been successfully switched to a number of new civilian applications in the last few years, such as delivery, logistics, surveillance, entertainment, and more. They also opened up new opportunities, such as working in difficult or dangerous areas. The UAV has the potential to solve the problem of air mobility, allowing to change transport and logistics in the future. Combining UAVs with traditional land vehicles can solve the last-mile delivery problem by achieving significant improvements in distribution costs and speed of vehicle delivery. One of the biggest challenges is to plan UAV routes with a number of constraints, including time, distance or energy costs, cargo weight, environmental and environmental conditions (such as wind direction or obstacles), UAV battery life, and demand requirements. users you want to visit. Thus, it revealed the need to classify different types of research and study the general characteristics of the study area. This article aims to help identify the main topics and new areas of research, as well as provides a published overview of the current state and contribution to the problem of UAV routing, as well as a general categorization of the problem of vehicle routing (VRP). The purpose of the paper is to analyze the scientific contributions to the problem of UAV routing to determine the main characteristics of these problems, as well as trends in research and recent improvements. Results. Sources are classified according to the areas of application of UAVs; methods that include exact, heuristic, metaheuristic, and mixed algorithms are mentioned. Conclusions. An overview of the work on routing problems using UAVs and the tasks they generate, trends in research and recent developments. Keywords: Unmanned aerial vehicle, routing, vehicle, optimization.

Attended home delivery in Indian public distribution system: an iterated local search approach

PurposeIn the modern era of e-grocery, attended home delivery (AHD) has been identified as a crucial part of the last-mile delivery problem. This paper aims to deal with a real-life last-mile-delivery problem in the context of the Indian public distribution system (PDS). The authors identified two different environments for the said AHD problem and proposed two different approaches to address the issue under these problem settings.Design/methodology/approachIn this study, the authors first consider the problem in a static environment and propose an iterated local search (ILS) integrated with an adaptive large neighborhood search (ALNS) meta-heuristic algorithm to obtain a routing solution. Thereafter, they extend the study in a dynamic environment where new delivery requests occur dynamically and propose a heuristic algorithm to solve the problem. For the dynamic case, multiple scenarios for the occurrence of delivery requests are considered to determine decisions regarding the opportunity to include a new request into the current solution.FindingsBy computational experiments, the authors show that the proposed approach performs significantly well for large size problem instances. They demonstrate the differences and advantages of the dynamic problem setting through a set of different scenarios. Also, they present a comparative analysis to show the benefits of adopting the algorithm in dynamic routing scenarios.Research limitations/implicationsFuture research may extend the scope of this study by incorporating stochastic delivery failure probabilities and customer behavior affecting the delivery response. Also, the present study does not take inventory policies at the depot into consideration. It will be of interest to see how the system performs under the uncertainty of supply from the depot. Despite the limitations, the authors believe that this study provides food for thought and encouragements for practitioners.Practical implicationsThis study shows the benefits of adopting an AHD problem in a dynamic setting in terms of customer service as compared to a traditional static environment. The authors clearly demonstrate the differences and advantages of the dynamic problem setting through a set of different scenario analysis.Social implicationsThis paper investigates a real-life AHD problem faced by the Department of Food, Supply and Consumer Affairs, India. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.Originality/valueThe study is unique and highly relevant for real-world applications and can help build a more robust AHD system. Also, the proposed solution approaches to aid the problem in both static and dynamic routing scenarios will be of particular interest to practitioners.

A variable neighborhood search for the last-mile delivery problem during major infectious disease outbreak.

During major infectious disease outbreak, such as COVID-19, the goods and parcels supply and distribution for the isolated personnel has become a key issue worthy of attention. In this study, we propose a delivery problem that arises in the last-mile delivery during major infectious disease outbreak. The problem is to construct a Hamiltonian tour over a subset of candidate parking nodes, and each customer is assigned to the nearest parking node on the tour to pick up goods or parcels. The aim is to minimize the total cost, including the routing, allocation, and parking costs. We propose three models to formulate the problem, which are node-based, flow-based and bilevel programing formulations. Moreover, we develop a variable neighborhood search algorithm based on the ideas from the bilevel programing formulations to solve the problem. Finally, the proposed algorithm is tested on a set of randomly generated instances, and the results indicate the effectiveness of the proposed approach.

Application of Deep Reinforcement Learning Algorithm in Uncertain Logistics Transportation Scheduling.

Nowadays, finding the optimal route for vehicles through online vehicle path planning is one of the main problems that the logistics industry needs to solve. Due to the uncertainty of the transportation system, especially the last-mile delivery problem of small packages in uncertain logistics transportation, the calculation of logistics vehicle routing planning becomes more complex than before. Most of the existing solutions are less applied to new technologies such as machine learning, and most of them use a heuristic algorithm. This kind of solution not only needs to set a lot of constraints but also requires much calculation time in the logistics network with high demand density. To design the uncertain logistics transportation path with minimum time, this paper proposes a new optimization strategy based on deep reinforcement learning that converts the uncertain online logistics routing problems into vehicle path planning problems and designs an embedded pointer network for obtaining the optimal solution. Considering the long time to solve the neural network, it is unrealistic to train parameters through supervised data. This article uses an unsupervised method to train the parameters. Because the process of parameter training is offline, this strategy can avoid the high delay. Through the simulation part, it is not difficult to see that the strategy proposed in this paper will effectively solve the uncertain logistics scheduling problem under the limited computing time, and it is significantly better than other strategies. Compared with traditional mathematical procedures, the algorithm proposed in this paper can reduce the driving distance by 60.71%. In addition, this paper also studies the impact of some key parameters on the effect of the program.