Search Results (294)

The increasing demand for rapid and sustainable parcel delivery has motivated the exploration of innovative logistics systems that integrate drones with traditional ground vehicles. Among these, the single-truck, multiple-drone last-mile delivery configuration has attracted significant attention due to its potential to reduce both delivery time and environmental impact. However, optimizing such systems remains computationally challenging because of the nonlinear energy consumption behavior of drones, which depends on factors such as payload weight and travel time, among others. This study investigates the energy-aware optimization of truck–drone collaborative delivery systems, with a particular focus on the mathematical formulation as mixed-integer nonlinear problem (MINLP) formulations and linearization of drone energy consumption constraints. Building upon prior models proposed in the literature in the field, we analyze the MINLP computational complexity and introduce alternative linearization strategies that preserve model accuracy while improving performance solvability. The resulting linearized mixed-integer linear problem (MILP) formulations are solved using the PuLP software, a Python library solver, to evaluate the efficacy of linearization on computation time and solution quality across diverse problem instance sizes from a benchmark of instances in the literature. Thus, extensive computational results drawn from a standard dataset benchmark from the literature by running the solver in a cluster infrastructure demonstrated that the designed linearization methods can reduce optimization time of nonlinear solvers to several orders of magnitude without compromising energy estimation accuracy, enabling the model to handle larger problem instances effectively. This performance improvement opens the door to a real-time or near-real-time solution of the problem, allowing the delivery system to dynamically react to operational changes and uncertainties during delivery. Full article

A key barrier to achieving sustainability in 15 minute cities is the efficiency of supply-chain logistics, particularly in historic urban districts characterized by dense and heritage-protected urban forms. This article presents a data-driven urban methodology to optimize last-mile logistics in Ciutat Vella (Valencia, Spain). Within the ENACT 15 min cities project, a Spatial Decision Support System (SDSS) was developed, combining iterative geospatial adjustments to the logistics network under changing boundary conditions with a demand-estimation model derived from the Valencia open-data platform. Using cadastral and field-survey data, the workflow simulates and optimizes the selection of vacant commercial premises as urban logistics hubs. A genetic algorithm minimizes oversupply, maximizes demand coverage, and improves spatial balance. The methodology also estimates the resulting carbon footprint, demonstrating that the optimized configuration enhances sustainability and service efficiency in dense historic settings. The approach is generalized to other urban contexts. Full article

The growing demand for efficient last-mile delivery has made routing optimization a critical challenge for logistics providers. Traditional vehicle routing models typically minimize a single criterion, such as travel distance or time, without considering broader social and environmental impacts. This paper proposes a novel Multi-Criteria Adaptive Hybrid Ant Colony Optimization (MCAH-ACO) algorithm for solving the delivery vehicle routing problem formulated as a Multiple Traveling Salesman Problem (MTSP). The proposed MCAH-ACO introduces three key innovations: a multi-criteria pheromone decomposition strategy that maintains separate pheromone matrices for each optimization objective, an adaptive weight balancing mechanism that dynamically adjusts criterion weights to prevent dominance by any single objective, and a 2-opt local search enhancement integrated with elite archive diversity preservation. A comprehensive cost function is designed to integrate four categories of factors: distance, time, social-environmental impact, and safety. Extensive experiments on real-world data from the Greater Toronto Area demonstrate that MCAH-ACO significantly outperforms existing approaches including Genetic Algorithm (GA), Adaptive GA, and standard Max–Min Ant System (MMAS), achieving 12.3% lower total cost and 18.7% fewer safety-critical events compared with the best baseline while maintaining computational efficiency. Full article

Urban delivery demand continues to rise, intensifying last-mile logistics challenges and accelerating the transition from manual delivery to autonomous delivery robots (ADRs). This study investigates the behavioral mechanisms underlying consumers’ migration toward ADRs. Grounded in the socio-technical systems perspective, we integrate the Push–Pull–Mooring (PPM) model with Social Cognitive Theory (SCT) to explain how technological and social stimuli shape switching and continuance intentions through cognitive and emotional pathways. Survey data from 786 Chinese consumers, analyzed using second-order structural equation modeling, support the proposed framework. The results indicate that dissatisfaction with manual delivery (push) and perceived benefits of ADRs (pull) significantly enhance both switching and continuance intentions. Outcome expectancy positively predicts switching intention but negatively predicts continuance intention. Technophobia reduces switching intention but does not significantly influence continuance. Moreover, social norms moderate key relationships, highlighting the role of external social influence in technology transition. This study extends PPM research into the smart logistics context, introduces socio-cognitive mechanisms into technology switching analysis, and conceptually distinguishes switching and continuance intentions as separate constructs. The findings offer practical guidance for ADR developers and policymakers by emphasizing strategies to reduce emotional resistance, enhance social endorsement, and promote the sustainable adoption of autonomous delivery technologies. Full article

This paper is dedicated to analysing sustainability and digitalisation in the transport systems of the European Union (EU) and Ukraine, with a particular focus on three representative subsectors: freight rail, urban public transport and last-mile postal logistics. It explores how technological innovation, operational efficiency and environmental responsibility interact within these sectors under distinct institutional and economic conditions: mature, market-based systems in the EU and resilience-driven systems in wartime Ukraine. This study applies a comparative, descriptive–analytical methodology using secondary data drawn from corporate sustainability reports, official statistics and sectoral databases for 2022. Quantitative KPls were complemented with a qualitative assessment of digitalisation maturity to ensure cross-country comparability. Through a comparative analysis of KPIs, such as freight volumes, emissions intensity, revenue efficiency and digital maturity, this study identifies structural and policy gaps that hinder progress toward sustainable mobility. This study develops a multi-dimensional framework combining operational, financial, environmental and digital indicators. In this paper, digital integration refers to the degree to which transport operators embed digital tools such as tracking, data management and automation into their core processes, while environmental efficiency denotes the ability to deliver transport services with minimal resource consumption and carbon emissions per operational unit. Institutional resilience is understood here as the capacity of transport organisations and governing institutions to maintain functionality, adapt and recover under crisis or systemic stress, which is particularly relevant for Ukraine’s wartime context. The findings demonstrate that while EU operators lead in transparency, digital integration and environmental performance, Ukrainian actors exhibit rapid adaptive innovation and significant potential for technological leapfrogging during reconstruction. This paper concludes that the EU must overcome regulatory inertia and infrastructure fatigue, while Ukraine should institutionalise resilience and transparency. Full article

Low-altitude logistics (LAL), supported by unmanned aerial vehicles (UAVs) and emerging urban air mobility operations within the low-altitude airspace (typically <1000 m), is rapidly reshaping last-mile distribution and time-critical delivery. However, LAL systems remain vulnerable to compound disruptions spanning weather, infrastructure, governance, and cybersecurity. Using a PRISMA-guided protocol, this systematic review synthesizes 1600 peer-reviewed studies published from 2020 to 2025 and combines bibliometric mapping (VOSviewer) with qualitative content analysis to consolidate the knowledge base on low-altitude logistics resilience (LALR). We conceptualize LALR via four coupled pillars, including robustness, adaptability, recoverability, and redundancy. The synthesize evidence across key vulnerability domains consists of platform reliability, communication and infrastructure readiness, regulatory fragmentation, cyber exposure, and weather-driven operational uncertainty. Building on the synthesis, we propose a Technology–Policy–Ecosystem roadmap that links (i) AI-enabled autonomy and risk-aware planning, (ii) adaptive governance tools such as regulatory sandboxes and dynamic airspace/UTM management, and (iii) ecosystem-level interventions, notably public–private partnerships and equity-oriented service design for underserved areas. We further outline a research agenda centered on measurable resilience metrics, activate redundancy design, climate-adaptive UAV operations, and digital-twin-enabled orchestration for scalable and sustainable LAL ecosystems. Full article

The growing demand for parcel delivery plays an important role in the integration of electromobility and urban logistics into urban delivery systems, especially in a mid-sized Central European city. This study investigates the challenges and opportunities of adopting electric vehicles (EVs) for last-mile delivery in the Miskolc region, Hungary. The author introduces a practical approach to describe the cost-based optimization of urban parcel delivery, formulated as an Electric Vehicle Routing Problem (EV-VRP) that builds on classical Vehicle Routing Problem (VRP) concepts. The developed model focuses on route and vehicle allocation and examines the impact of charging infrastructure and fleet composition on delivery performance, while explicitly evaluating five cost categories: vehicle (including maintenance and service), driver, infrastructure, operation center, and environmental energy. The numerical results validate the model and show that partial fleet electrification can improve cost efficiency and reduce environmental impact even in regions with limited charging capacity. The proposed approach makes it possible to analyze the operational costs of electromobility strategies on last-mile logistics under realistic routing, capacity, and energy constraints. The results confirm that the integration of electric vehicles into city logistics can contribute to more flexible, sustainable, and cost-effective delivery systems. The numerical analysis shows that under the conditions examined, the model results in approximately 20% lower total operational cost compared to the conventional vehicle fleet operating under similar conditions. The cost structure is dominated by labor and vehicle-related components, while infrastructure, operational management, and environmental–energy factors appear with lower intensity. Full article

The increasing adoption of electric vehicles (EVs) for last-mile delivery requires adapting existing routes designed for internal combustion engine (ICE) vehicles. This study introduces an enhanced Ant Colony System (ACS) that optimizes EV routing by dynamically incorporating state of charge (SOC), charging station proximity, and time constraints. Unlike traditional methods, our approach adjusts pheromone deposition in real time, prioritizing charging stops only when necessary, significantly improving adherence to delivery times. Using real-world delivery data from Paris, our results show that routes under 90 km tend to remain energetically feasible, although intermediate time-window violations may occur due to cumulative charging delays. For longer routes, the need for additional charging stops introduces a risk of delays, but the system effectively manages these constraints to minimize disruption. These results provide fleet operators with a practical decision-support tool to identify which pre-optimized routes can be efficiently adapted to EVs, thus offering a pathway for the integration of electric vehicles into existing logistics without significant operational disruption. Future work will focus on enhancing the system by incorporating real-time traffic updates and charging station availability to further optimize the routing process. Full article

Background: In the era of rapid digital transformation, efficient food logistics (FL) is critical for sustainability and competitiveness. Maintaining food quality, minimizing waste, and optimizing costs are complex challenges that advanced digital technologies aim to address, particularly amid growing e-commerce and last-mile delivery demands. This underscores the need for a structured, quantitative evaluation of technological solutions to ensure operational reliability, efficiency, and sustainability. Methods: This study employs a Multi-Criteria Decision Making (MCDM) model combining Criterion Impact LOSs (CILOS) and Multi-Objective Optimization on the basis of Simple Ratio Analysis (MOOSRA) to evaluate key FL technologies: IoT, blockchain, Big Data analytics, automation and robotics, and cloud/edge computing. Nine evaluation criteria relevant to logistics providers were used, covering operational efficiency, flexibility, sustainability, food safety, data reliability, KPI support, scalability, costs, and implementation speed. CILOS determined criteria weights by considering interdependencies, and MOOSRA ranked technologies by benefits-to-costs ratios. Sensitivity analysis validated result robustness. Results: Automation and robotics ranked highest for enhancing efficiency, reducing errors, and improving handling and safety. Blockchain was second, supporting traceability and data security. Big Data analytics was third, enabling demand prediction and inventory optimization. IoT ranked fourth, providing real-time monitoring, while cloud/edge computing ranked fifth due to indirect operational impact. Conclusions: The CILOS–MOOSRA model enables transparent, structured evaluation, integrating quantitative metrics with logistics providers’ priorities. Results highlight technologies that enhance efficiency, reliability, and sustainability while revealing integration challenges, providing a strategic foundation for digital transformation in FL. Full article

Drones have emerged as critical infrastructure for enhancing logistics efficiency in the emerging low-altitude economy, particularly in collaborative vehicle–drone research. However, existing research often neglects the impact of fair task allocation on workload balance among formations in large-scale routing scenarios, which compromises service quality. To address this gap, we introduce the Multi-vehicle with drones Collaborative Routing Problem with Large-scale Packages (MCRPLP), formulated as a bi-objective model aiming to minimize both operational cost and workload imbalance. A Hybrid Strategy-assisted Multi-objective Optimization Algorithm (HSMOA) is developed to overcome the limitations of existing methods, which struggle with balancing solution quality and computational efficiency in solving large-scale routing. Based on a Non-dominated Sorting Genetic Algorithm (NSGA-II), the HSMOA integrates a heuristic task assignment strategy that greedily reassigns packages between adjacent clusters. Then, by integrating a Pareto-front superiority evaluation model, an elite individual supplement strategy is designed to dynamically prune sub-optimal solution subspaces while enhancing the search within high-quality Pareto-front subspaces in HSMOA. Extensive experiments demonstrate the effectiveness of HSMOA in terms of solution quality and computational efficiency compared to multiple state-of-the-art methods. Further sensitivity analysis and managerial insights derived from a real-world case are also provided to support practical logistics implementation. Full article

This study examines how platformized e-commerce logistics reshapes urban land use at the neighborhood scale, using Shanghai as an empirical case. It argues that last-mile logistics infrastructure operates through two intertwined mechanisms: as physical service nodes that generate localized pedestrian flows sustaining neighborhood retail, and as neighborhood-level execution points within a digitally coordinated logistics system that produces citywide substitution pressures and restructures commercial spaces, particularly community-oriented shopping malls. Theoretically, the study advances platform and logistics urbanism by reconceptualizing last-mile infrastructure as a dual-role urban system with scale-dependent land-use effects. Methodologically, it combines street-segment regression analysis with shopping-mall case studies to link logistics proximity to fine-grained spatial outcomes. Empirically, the findings reveal complementary effects for street retail alongside accelerated restructuring and functional repurposing in community malls—patterns not captured by uniform displacement models. Planning analysis further identifies a governance mismatch in Shanghai’s 2017–2035 Master Plan, underscoring the need for platform-responsive planning to address emerging hybrid commercial–logistics spaces. Full article

Efficient last-mile delivery remains a critical challenge for rural agricultural logistics, globally, particularly in cold-climate regions with dispersed agricultural operations. Truck–drone hybrids can reduce delivery times but face payload limits, cold-weather battery loss, and beyond-visual-line-of-sight regulations. This review evaluates the potential of GIS-enabled truck–drone hybrid systems to overcome infrastructural, environmental, and operational barriers in such settings. This study uses the state of North Dakota (USA) as a representative case because of its cold climate, low density, and weak connectivity. These conditions require different routing and system assumptions than typical regions. The study conducts a systematic review of 81 high-quality publications. It identifies seven interconnected research domains: GIS analytics, truck–drone coordination, smart agriculture integration, rural implementation, sustainability assessment, strategic design, and data security. The findings stipulate that GIS enhances hybrid logistics through route optimization, launch site planning, and real-time monitoring. Additionally, this study emphasizes the rural, low-density context and identifies specific gaps related to cold-weather performance, restrictions to line-of-sight operations, and economic feasibility in ultra-low-density delivery networks. The study concludes with a roadmap for research and policy development to enable practical deployment in cold-climate agricultural regions. Full article

The accelerated growth of e-commerce and ongoing urban expansion have intensified the challenges associated with last-mile delivery, making it a critical issue in sustainable urban logistics. Therefore, our paper presents a scoping review to systematically delineate the current state of research on smart and sustainable last-mile delivery systems. We explore both innovative technologies—such as artificial intelligence, autonomous vehicles, the Internet of Things, and digital twins—and human-centered dimensions, including urban design, policy development, and collaborative stakeholder engagement. Using the PRISMA-ScR-based methodology, 140 peer-reviewed articles (2015–2025) have been analyzed to highlight key trends, gaps, and prospective directions. The study underlines how the technologies of Industry 4.0 have improved visibility and operational efficiency, but holistic thinking that incorporates environmental, human, and policy factors remains undeveloped. Based on these findings, this article provides a conceptual framework for smart and sustainable last-mile delivery, focusing on the intersection of digital and simulation tools and human-centric governance to achieve optimized efficiency, environmental performance, and equity. This framework helps both academics and decision-makers to advance data-driven, resilient, and integrative city logistic ecosystems. Full article

Background: nowadays, traditional delivery options are challenging to the urban last-mile logistics and sustainability goals. The purpose of this study is to investigate the practical factors that drive frequent e-shoppers to actively switch their intention from conventional delivery options to utilizing smart lockers. Methods: the hypothetical framework tested integrating constructs from the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), and supplementary constructs such as privacy and convenience. Data were collected via a structured online questionnaire from 513 respondents in major Egyptian cities, including Alexandria and Cairo. The framework was tested using Structural Equation Modeling (SEM) via SmartPLS 4.0 software to assess the relationship between constructs and switching intention. Results: the analysis confirms that switching intention to use smart lockers is positively driven by Perceived Usefulness, Perceived Ease of Use, Convenience, Privacy, and Perceived Behavioral Control. Notably, a positive attitude towards smart lockers was found to have a non-significant effect on the intention to switch in the Egyptian context. Conclusions: this research contributes to addressing the gap in the extant literature by focusing on analyzing the unique contextual determinants in the emerging last-mile logistics within a developing market context. Full article

The role of last-mile delivery professionals is becoming increasingly vital in modern urban logistics, driven by the rapid expansion of e-commerce and rising consumer expectations for fast and reliable services. This study aimed to analyse the decision-making patterns of last-mile delivery professionals through stated preference data. To achieve this, a stated-preference questionnaire was conducted with 333 riders aged 18–65 from Croatia, Cyprus, Greece, Italy and Slovenia. A random parameter logit (RPL) model was applied to evaluate the influence of factors such as driving behaviour, delivery time and salary type on decision-making in hypothetical scenarios. Results showed that driving behaviour, trip duration and salary type significantly affected respondents’ preferences. Participants displayed a strong preference for flat salaries, indicating the importance of income stability over performance-based pay. Driving behaviour was also crucial, as respondents favoured legal and safe practices. Interestingly, while shorter delivery times were generally preferred, several scenarios revealed a tolerance for longer durations, possibly reflecting perceived benefits such as safer routes or reduced stress. Comparative analyses also revealed regional differences in vehicle use, work patterns and safety perceptions. The study highlights the need for tailored training programs on safety compliance, route optimization and time management, alongside hybrid salary structures balancing stability and productivity. Full article