Last-mile Delivery Research Articles

Optimizing last-mile delivery of essential medicines in Nigeria: Insights from Niger State’s health-care supply chain

Last-mile delivery of health commodities is a critical component of efficient supply chains, and third-party logistics (3PL) providers are pivotal in ensuring cost-effective and reliable product distribution. This study explores the implementation of last-mile delivery outsourcing by government-operated warehouses and drug management agencies (DMAs). This research focuses on the relationship between 3PL utilization and key performance metrics, such as cost reduction, timely delivery, and end-user satisfaction. This study combines a literature review, surveys, and interviews conducted with employees of the DMAs in Minna, Niger State, to establish significant correlations. The findings show that using 3PLs significantly reduced operational costs, improved the frequency of timely deliveries, and enhanced end-user satisfaction. These findings underscore the need for continued engagement with 3PL providers to optimize supply chain performance and improve service delivery outcomes. Managers are recommended to maintain and expand such collaborations to sustain these benefits.

Elevating logistics performance: harnessing the power of artificial intelligence in e-commerce

PurposeArtificial intelligence (AI) usage improves e-commerce logistics efficiency. However, many actors can play significant roles, such as supply chain consistency (SCC), last-mile logistics (LML) performance and collaboration and coordination among logistics firms. This study aims to assess how SCC and LML performance mediate and collaboration and coordination moderate the relationship between AI usage and logistics efficiency.Design/methodology/approachA structured questionnaire was used to collect the data. A total of 245 valid responses were received from Indian e-commerce businesses. The data were then analysed using AMOS v25 and structural equational modelling using SPSS for regression, PROCESS macro for mediation and moderated mediation analysis.FindingsThe findings show that AI usage independently impacts logistics efficiency, with SCC and last-mile delivery performance as mediating variables. Collaboration and coordination among logistic firms are also critical moderators in enhancing AI’s efficacy in logistic operations. The study findings suggest the integration of AI into logistic operations and provide implications to managers on the urgency of fostering a collaborative and synchronised environment to utilise the full potential of AI in e-commerce businesses.Originality/valueThis study not only contributes to the field of logistics theory by presenting empirical data on the various ramifications of AI but also offers practical guidance for logistics firms, particularly those operating in developing economies, on how to strategically employ AI to enhance operational efficiency and attain a competitive advantage in the era of e-commerce logistics in the digital age.

Re-Supplying Autonomous Mobile Parcel Lockers in Last-Mile Distribution

This paper investigates a practical last-mile delivery scenario where a fleet of trucks replenishes autonomous mobile parcel lockers (AMPLs) in an urban setting. The lockers move along specified paths within restricted zones to reach customers’ locations. Ensuring seamless coordination between trucks and AMPLs requires the identification of suitable locations to exchange empty or loaded modular lockers. We first introduce a mixed-integer linear programming (MILP) formulation for the investigated problem. The proposed formulation establishes the basis for optimizing meeting point selection and routing decisions. Additionally, the study introduces a cluster-based simulated annealing (CSA) algorithm tailored for addressing larger-scale instances of the studied problem. The CSA algorithm incorporates the K-means clustering method with specialized operators rooted in an extensive neighborhood search, aiming to improve the effectiveness of solution discovery. We generated a new set of benchmark instances to assess the MILP formulation’s efficiency and the proposed metaheuristic algorithm and conducted comprehensive numerical experiments.

Multi-Depot Electric Vehicle–Drone Collaborative-Delivery Routing Optimization with Time-Varying Vehicle Travel Time

This paper presents an electric vehicle-drone (EV–drone) collaborative-delivery routing optimization model that leverages the time-varying characteristics of electric vehicles and drones across multiple distribution centers (i.e., central depots) to address the logistics industry’s low-carbon transformation in the last-mile delivery. The model aims to minimize total delivery costs by formulating a mixed-integer programming (MIP) model that accounts for essential constraints such as nonlinear charging time, time-varying EV travel time, delivery time window, payload capacity, and maximum range. An improved adaptive large-neighborhood search (ALNS) algorithm is developed to solve the model. Experimental results validate the effectiveness of the proposed algorithm and highlight the impact of EV and drone technology parameters, along with the time-varying EV travel times, on the economic efficiency of delivery distribution and route planning.

Spatial participatory planning for urban logistics: A GIS-enhanced Real-Time Spatial Delphi approach

Effective urban logistics planning plays a crucial role in effectively managing infrastructure and transportation networks, contributing to the mitigation of negative externalities associated with last-mile deliveries and fostering city sustainability. Among the various facets of supply chain management, selecting appropriate locations in urban areas for logistics facilities, such as automated parcel lockers and smart loading bays, stands out. However, the complexity of decision-making in this context, involving diverse stakeholders with potential conflicting objectives, poses a significant challenge in reaching a common consensus. This paper proposes an innovative approach to address this issue by adopting a spatial participatory process that combines GIS models and Real-Time Spatial Delphi to identify suitable locations for two logistic facilities. The case study is the city of Dublin (Ireland), where an Urban Living Lab of the H2020 project SENATOR is based. A modified Delphi is used, first gathering expert opinions on preferences and objectives. These insights are then used to develop a spatial analysis using GIS, ranking areas according to the expert preferences. These outcomes form the basis for the Real-Time Spatial Delphi, where stakeholders and experts working on the project collaboratively engage to achieve a spatial consensus on the territory. The findings of this study provide valuable information for stakeholders, policymakers, local authorities, and governmental bodies to implement specific actions. Through the active involvement of key actors in the decision-making process, this approach ensures that the selected locations for logistics facilities align with the needs of all parties involved.

A predictive framework for last-mile delivery routes considering couriers’ behavior heterogeneity

Last-mile route prediction is a powerful tool for freight delivery companies that can be essential in the development of new features such as arrival time prediction or accurate workload allocation. Existing methodologies propose prediction models that, on the one hand, require external information, such as traffic data or a prior division of work areas, and, on the other hand, assume courier homogeneity in the search for travel patterns. However, this assumption may introduce noise in the predictions since different couriers may be subject to different routing habits. This study proposes a comprehensive predictive framework for delivery routes, which allows identifying the routing profiles that best suit each courier and work area, and which starts from basic information available for any carrier, ensuring the scalability of the tool. The analysis is supported by a case study, where the results obtained bring to light the heterogeneity in the routing decisions of the different drivers and show that the proposed approach produces consistent and accurate predictions for the vast majority of them.

Branch-and-Price for the Capacitated Autonomous Vehicle Assisted Delivery Problem

In recent years, the exponential growth of package volumes has posed significant challenges for logistics networks, particularly in the realm of last-mile delivery. To mitigate costs while upholding service and delivery commitments, companies are increasingly investigating autonomous assisted delivery as a viable solution. In this paper, we study the Capacitated Autonomous Vehicle Assisted Delivery Problem, where an autonomous vehicle works in conjunction with a delivery person. The autonomous vehicle drops off the delivery person at designated locations, and the delivery person completes the deliveries (with a capacity constraint) on foot to the final addresses. Once the deliveries are completed, the vehicle picks up the delivery person and travels to the next reloading point. The goal is to decide on a route to serve all customers while minimizing the route completion time. We introduce an integer programming formulation with exponentially many variables and develop a branch-and-price approach. For generating promising columns, we present a tailored pulse algorithm to solve the pricing problem. Furthermore, by leveraging the structural properties of optimal solutions, we carefully design algorithmic enhancements, valid inequalities, and preprocessing steps to improve computational tractability. By conducting computational experiments based on instances derived from real-world data, we demonstrate the positive impact of these components. More importantly, we provide optimal certificates for 426 out of the 450 instances documented in the literature. Among the 100 instances in which driving could be slower than walking, we report solutions for the 40 largest instances for the first time. History: Accepted by David Alderson, Area Editor for Network Optimization. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0177 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0177 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Uncrewed Aerial Vehicle Routing Problem for Integrated Crewed and Uncrewed Aircraft Operations

Last-mile delivery systems are one of the numerous industrial applications of uncrewed aircraft systems (UAS). This study analyzes UAS-based package delivery systems in the context of the integrated operations of crewed and uncrewed aircraft. It introduces a multi-trip, time-dependent vehicle routing problem aimed at optimizing UAS delivery routes within integrated operations. By conducting numerical simulations of helicopters (crewed aircraft) and UAS missions sharing the same airspace, the optimal UAS routes for resolving potential conflicts in the shared airspace can be effectively determined without any adverse impact on helicopter operations. Knowing the helicopter mission times in advance enables the efficient planning of UAS missions. The results of this study provide valuable insights regarding the implementation of integrated crewed and uncrewed aircraft operations.

Advanced supply chain optimization for emerging market healthcare systems

The complexities of pharmaceutical supply chains in emerging markets present significant challenges that directly impact healthcare outcomes, particularly in underserved regions. This research focuses on the optimization of supply chain systems for the healthcare sector, with an emphasis on enhancing pharmaceutical delivery. By leveraging data analytics and innovative procurement strategies, the study seeks to improve efficiency, reduce operational costs, and ensure the timely distribution of essential medications. Drawing on practical experiences at eMedic Technologies Africa, the research highlights the need for advanced models that integrate real-time data to optimize inventory management, forecast demand, and address bottlenecks in last-mile delivery. The study proposes a multi-faceted approach to addressing common challenges in emerging market supply chains, including unreliable infrastructure, fluctuating demand, and limited access to technology. By utilizing predictive analytics, the proposed models aim to improve accuracy in demand planning, reducing stockouts and overstock situations. Additionally, innovative procurement strategies, such as centralized procurement and strategic partnerships with local suppliers, are explored as key methods for enhancing cost-effectiveness and streamlining supply chain operations. A critical component of the research is the focus on last-mile delivery, which remains a significant hurdle in rural and underserved areas. The study advocates for the use of digital platforms and mobile technologies to enhance delivery tracking and coordination, ensuring that essential medications reach their destination efficiently. Furthermore, it emphasizes the importance of fostering collaboration between governments, non-governmental organizations, and private sector stakeholders to build resilient supply chain systems capable of adapting to local conditions. By providing actionable insights and scalable models for supply chain optimization, this research aims to contribute to improved healthcare delivery in emerging markets. The proposed strategies not only reduce costs and improve efficiency but also ensure a reliable and consistent supply of medications, ultimately leading to better health outcomes in regions most in need. Keywords: Supply Chain Optimization, Data-Driven Procurement, Last-Mile Delivery, Healthcare Efficiency, Emerging Markets

Spatial Analysis of Middle-Mile Transport for Advanced Air Mobility: A Case Study of Rural North Dakota

Integrating advanced air mobility (AAM) into the logistics of high-value electronic commodities can enhance efficiency and promote sustainability. The objective of this study is to optimize the logistics network for high-value electronics by integrating AAM solutions, specifically using heavy-lift cargo drones for middle-mile transport and using the mostly rural and small urban U.S. state of North Dakota as a case study. The analysis utilized geographic information system (GIS) and spatial optimization models to strategically assign underutilized airports as multimodal freight hubs to facilitate the shift from long-haul trucks to middle-mile air transport. Key findings demonstrate that electronics, because of their high value-to-weight ratio, are ideally suited for air transport. Comparative analysis shows that transport by drones can reduce the average cost per ton by up to 60% compared to traditional trucking. Optimization results indicate that a small number of strategically placed logistical hubs can reduce average travel distances by more than 13% for last-mile deliveries. Cost analyses demonstrate the viability of drones for middle-mile transport, especially on lower-volume rural routes, highlighting their efficiency and flexibility. The study emphasizes the importance of utilizing existing infrastructure to optimize the logistics network. By replacing truck traffic with drones, AAM can mitigate road congestion, reduce emissions, and extend infrastructure lifespan. These insights have critical implications for supply chain managers, shippers, urban planners, and policymakers, providing a decision support system and a roadmap for integrating AAM into logistics strategies.

Drone Integration in Last-Mile Delivery Operations

Purpose: This research examines the integration of drone technology into last-mile delivery operations, focusing on its impact on supply chain efficiency, cost reduction, and sustainability. The study explores operational challenges, such as air traffic management, payload limitations, regulatory compliance, and public perception, providing a comprehensive understanding of how drones can reshape last-mile logistics. Methodology: The study employs a multi-faceted approach, combining historical analysis, in-depth case studies, and scenario planning to assess the impact of drone technology on last-mile delivery efficiency, cost optimization, and accessibility. Data is gathered from industry reports, interviews with logistics professionals, and regulatory documents. Thematic analysis is conducted to identify critical patterns, while scenario-based modeling offers insight into the potential outcomes of various integration strategies. A comparative evaluation of existing last-mile delivery methods against drone-based delivery models is also provided. Findings: The research reveals that drone technology offers significant potential for enhancing last-mile delivery efficiency, particularly in urban areas and remote locations, aligning with findings from studies like those by Murray & Chu (2015). However, the study identifies considerable obstacles, including regulatory challenges, limited payload capacities, and public acceptance issues. Solutions such as airspace regulation frameworks, collaborative logistics networks, and advanced battery technologies are highlighted as critical enablers for successful drone integration. Contribution to Theory, Policy and Practice: This study contributes to the growing body of knowledge on drone technology's role in logistics by providing a comprehensive framework for businesses and policymakers to navigate the complexities of drone integration. It recommends actionable steps for integrating drones into last-mile delivery, bridging the gap between theoretical innovation and practical application. It provides policy recommendations for regulatory bodies to develop airspace management systems that support drone operations. For practitioners, the research offers a strategic guide to overcoming operational challenges, facilitating the adoption of drones as a sustainable solution for last-mile delivery.

Evaluation of integrated sustainable last-mile delivery methods in an intuitionistic fuzzy environment

Evaluation of integrated sustainable last-mile delivery methods in an intuitionistic fuzzy environment

Optimizing a Dynamic Vehicle Routing Problem with Deep Reinforcement Learning: Analyzing State-Space Components

Background: The dynamic vehicle routing problem (DVRP) is a complex optimization problem that is crucial for applications such as last-mile delivery. Our goal is to develop an application that can make real-time decisions to maximize total performance while adapting to the dynamic nature of incoming orders. We formulate the DVRP as a vehicle routing problem where new customer requests arrive dynamically, requiring immediate acceptance or rejection decisions. Methods: This study leverages reinforcement learning (RL), a machine learning paradigm that operates via feedback-driven decisions, to tackle the DVRP. We present a detailed RL formulation and systematically investigate the impacts of various state-space components on algorithm performance. Our approach involves incrementally modifying the state space, including analyzing the impacts of individual components, applying data transformation methods, and incorporating derived features. Results: Our findings demonstrate that a carefully designed state space in the formulation of the DVRP significantly improves RL performance. Notably, incorporating derived features and selectively applying feature transformation enhanced the model’s decision-making capabilities. The combination of all enhancements led to a statistically significant improvement in the results compared with the basic state formulation. Conclusions: This research provides insights into RL modeling for DVRPs, highlighting the importance of state-space design. The proposed approach offers a flexible framework that is applicable to various variants of the DVRP, with potential for validation using real-world data.

Conceptual Aspect of Measuring the Efficiency of Cargo Unmanned Aerial System

This article examines the conceptual framework for measuring the efficiency of unmanned systems, focusing on a task-oriented and problem-solving approach to evaluating the performance of cargo unmanned aerial vehicles. The efficiency of cargo unmanned aerial vehicles pertains to their ability to complete missions on time and cost-effectively, while maximizing utilization, minimizing resource loss, and maintaining an acceptable level of flight safety. It is important to highlight that the efficiency of cargo unmanned aerial vehicles is multifaceted, encompassing technical, operational, economic, environmental, and regulatory dimensions. Progress in each of these areas contributes to the successful deployment and optimization of cargo drones across various applications, from last-mile deliveries to supply chain operations in remote regions. The article presents two methodologies for assessing the efficiency of cargo drones.

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.

Express shipments with autonomous robots and public transportation

Growing urbanization, exploding e-commerce, heightened customer expectations, and the need to reduce the environmental impact of transportation ask for innovative last-mile delivery solutions. This paper explores a new express shipment model that combines public transportation with Autonomous Robots (ARs) and studies its real-time management. Under dynamic demand arrivals with short delivery time promises, we propose a rolling horizon framework and devise a machine learning-enhanced Column Generation (CG) methodology to solve the real-time AR dispatching problem. The results of our numerical experiments with real-world delivery demand data show the significant potential of the proposed system to reduce travel time, vehicle traffic, emissions, and noise. Our results also reveal the efficacy of the learning-based CG methodology, which provides almost the same quality solutions as the classical CG approach with much less computational effort.

Last‐Mile Delivery: A Process View, Framework, and Research Agenda

ABSTRACTThe expansion of e‐commerce has increased scholarly interest in Business‐to‐Customer (B2C) last‐mile delivery (LMD). LMD occurs in a unique logistical context and presents many new, evolving challenges worthy of scholarly investigation. While most LMD studies emerged after 2018, existing literature reviews predate the “boom.” Current reviews have focused at the macro‐level, considering LMD secondarily within broad topics like omni‐channel or micro perspectives that address granular issues like transportation within LMD. The majority of studies have focused on analytical models that investigate different concerns compared to conceptual and empirical studies. Our study examines an up‐to‐date sample of 104 articles in LMD literature from a process, meso‐level perspective. We develop a framework, classifying studies around pre‐delivery, delivery, and post‐delivery activities that produce valuable insights linking these activities. We synthesize the prior literature and offer a research agenda that incorporates different stakeholder perspectives and identifies methodological opportunities. The analysis reveals several areas of future research that were validated and enhanced by practitioner interviews.

Sustainability and New Technologies: Last-Mile Delivery in the Context of Smart Cities

Sustainability and New Technologies: Last-Mile Delivery in the Context of Smart Cities

Flying to your home yard: the mediation and moderation model of the intention to employ drones for last-mile delivery

Flying to your home yard: the mediation and moderation model of the intention to employ drones for last-mile delivery

Parcel-Locker-Sharing Model for E-Commerce Logistics Service Providers

In the e-commerce last-mile delivery process, the asset operators (logistics service providers who own parcel locker facilities) support their delivery service with parcel lockers, while the non-asset operators (logistics service providers without parcel lockers) perform door-to-door delivery. Due to demand fluctuation, asset operators’ parcel-locker slots may be left vacant, while non-asset operators are stuck with the high-cost door-to-door service. The exclusiveness of parcel-locker usage reduces resource utilization and service efficiency in last-mile delivery. Therefore, this paper proposes a parcel-locker-sharing model in which these two parties share the parcel-locker capacity in last-mile delivery. The asset operator rents the unused parcel lockers to the non-asset operator by charging a rental fee,while the non-asset operator rents the parcel lockers for delivery to save logistics costs. The motivation of this alliance is to increase the profits of both parties and that of the total supply chain. This study establishes the supply-chain profit model for the parcel-locker-sharing framework and finds that the profit or loss depends on the comparison of the operation cost savings and delivery-cost savings. A numerical analysis is conducted to validate the final result. The research further suggests the optimal rental quantity and price interval. This paper is the first to study the operational mechanism of sharing the parcel locker between two distinct types of logistics service providers and to offer recommendations for industrial application.