Search Results (408)

Pharmaceutical cold-chain distribution must maintain timely access to temperature-sensitive medicines while limiting the energy demand and carbon emissions associated with refrigerated transport. This study proposes a sustainable dynamic route optimization method that integrates reinforcement learning (RL) with an improved neighborhood search (NS) algorithm to balance delivery timeliness and transportation carbon emissions. The NS algorithm is enhanced with carbon emission and timeliness operators, and RL adaptively adjusts their weights under dynamic events, including traffic congestion, vehicle failure, and order insertion. The method is evaluated using the Solomon Benchmark dataset and a warehouse-to-community-pharmacy last-mile distribution case for chronic-disease medicines. The RL-NS algorithm achieves an average computation time of 45.3 ms and a standard deviation of 2.7, outperforming the comparison algorithms. In the case study, it reduces transportation carbon emissions by approximately 18% and delivery time by approximately 12% relative to traditional routing. By reducing route redundancy and enabling rapid replanning, the method supports lower-emission and potentially more energy-efficient transport operations. The findings demonstrate its relevance to sustainable transportation, sustainable logistics, and resilient pharmaceutical cold-chain management. Full article

Existing truck–drone collaborative routing models predominantly assume fixed flight speeds, overlooking the non-linear coupling among speed, payload, and energy consumption, which limits urban delivery efficiency. To bridge this gap, this paper proposes the multiple flying sidekick traveling salesman problem with variable drone speed (mFSTSP-VDS). Formulating drone cruising speed as a continuous variable under strict non-linear energy constraints, we design a hybrid algorithm (ALNS-SA-VND) to jointly optimize routing, task allocation, and speed. Empirical analysis of Wuhan’s road network demonstrates the VDS strategy’s robustness. Specifically, VDS reduces the system makespan by up to 17.5% compared to rigid maximum-speed strategies, with consistent stability across varying load scenarios. By adaptively trading permissible battery capacity for temporal synchronization, VDS effectively mitigates unnecessary truck waiting times at rendezvous nodes. This study quantitatively validates the impact of sortie-specific speed adaptation on time efficiency, providing an exploratory theoretical baseline for tactical-level planning in smart logistics networks. Full article

The rapid expansion of e-commerce has reshaped global consumption systems by transforming production processes, logistics infrastructures, and consumer behaviour. While this transformation has generated significant economic opportunities, it has simultaneously intensified environmental pressures, particularly through last-mile delivery emissions, excessive packaging waste, and high return rates. At the same time, the growing diffusion of corporate sustainability reporting has raised increasing concerns about greenwashing, defined as the misrepresentation of environmental performance through selective disclosure or symbolic communication. This study aims to provide a comprehensive assessment of sustainability practices in e-commerce, focusing on the relationship between environmental performance, transparency, and economic outcomes. Particular attention is devoted to the role of blockchain technology as a potential mechanism for enhancing verifiable transparency in complex supply chains. The research adopts a multiple case study design grounded in the methodological frameworks and integrates qualitative analysis with a semi-quantitative evaluation model. Seven companies operating in different segments of the e-commerce ecosystem are analyzed through an extensive review of secondary data sources, including ESG reports, financial disclosures, NGO assessments, and industry benchmarks. The findings reveal a substantial gap between declared sustainability commitments and actual implementation, with significant heterogeneity across firms. Companies that embed sustainability into their strategic core demonstrate stronger alignment between environmental and economic performance, whereas firms relying primarily on communication-driven approaches exhibit higher implementation gaps. The study contributes to the literature by introducing an analytical framework centered on the concept of the implementation gap and by demonstrating the central role of transparency in determining sustainability effectiveness. It also highlights the potential, yet still largely unrealized, role of blockchain technology in addressing information asymmetry and reducing greenwashing in e-commerce. Full article

Low-altitude logistics is increasingly considered a promising solution for urban last-mile delivery, yet how urban morphology is associated with the simulated cost of drone logistics networks across cities remains unclear. This study examines model-based relationships between urban spatial form and the cost performance of drone logistics networks under unified simulation assumptions. A multi-tier facility location model is developed and applied to 101 Chinese cities, with simulated annealing used to obtain cost-minimizing configurations of drone take-off and landing facilities. An XGBoost model with SHAP analysis is employed to interpret nonlinear associations and interaction patterns between urban morphology indicators and simulated network cost, while K-means clustering is used to identify representative morphology–cost patterns. The results show that built-up area and landscape shape index are the most influential predictors in the adopted modeling setting, both exhibiting threshold-like sensitivity ranges. Simulated network costs increase more rapidly when built-up area exceeds approximately 1000 km² and when landscape shape index falls within 5–15, with a notable interaction between them. Three morphology–cost types are further identified, reflecting systematic differences in simulated network organization. These findings provide simulation-derived evidence for morphology-sensitive planning of low-altitude logistics infrastructure, while actual deployment decisions still require calibration with local demand, operational, regulatory, and airspace conditions. Full article

Background: The rapid growth of e-commerce has intensified the tension between customer expectations for fast, convenient delivery and the need for more sustainable last-mile logistics. While existing studies have examined speed, price, sustainability, and convenience as separate delivery attributes, less attention has been given to how these dimensions are combined and presented in consumer-facing delivery options. Methods: This study adopts a mixed-methods approach, combining a systematic literature review with structured analysis of publicly available delivery offers on websites across the UK retail and logistics sectors. Results: The findings show that delivery design remains strongly shaped by speed, price visibility, and convenience, while sustainability signals are rarely embedded at the point of customer choice. Although the literature highlights growing interest in green logistics, observed delivery menus suggest a persistent gap between sustainability commitments and their implementation at checkout. Five delivery strategy archetypes are identified, illustrating how firms configure trade-offs among fast delivery, affordability, sustainability signalling, and convenience. Conclusions: The study contributes a four-pillar choice architecture framework for understanding online delivery design. It highlights the need for clearer sustainability communication, greener default options, and stronger alignment among firm strategy, consumer decision-making, and policy support in last-mile delivery. Full article

Blockchain-based crowdsourcing logistics is a promising decentralized paradigm for solving the “last-mile delivery” problem, in which smart contracts automatically execute the business logic. Since crowdsourcing logistics inherently involves frequent fund transfers, its smart contracts are particularly susceptible to reentrancy vulnerabilities. Existing works address reentrancy by inserting a lock mechanism at design-time, which lacks dynamic responsiveness and incurs additional gas overhead. To overcome this limitation, we propose RE4SC, the first runtime enforcement framework for vulnerable smart contracts. RE4SC contains two components: off-Blockchain granularity segmentation and on-Blockchain granular block reordering. At the off-Blockchain level, bytecode is segmented into granular blocks through control flow analysis. This yields a finer granularity than conventional basic blocks in a control flow graph. These granular blocks are then organized into a tree structure that captures their hierarchical nesting relationships. A data flow analysis further ensures data dependency consistency after reordering. At the on-Blockchain level, a runtime enforcer retrieves the pre-computed reordering specifications from off-Blockchain analysis. It applies a depth-first reordering algorithm to reposition key state variable assignments before transfer operations, eliminating reentrancy vulnerabilities without introducing additional bytecode. We implement a prototype tool and make it open-source. Experiments on self-constructed crowdsourcing logistics contracts and three public datasets demonstrate that RE4SC repairs vulnerable contracts with zero gas overhead, outperforming existing approaches. Full article

Rural logistics faces persistent challenges such as high distribution costs, dispersed demand, and limited transport infrastructure, which hinder efficient last-mile delivery. To address these issues, this study proposes a bus–heterogeneous drone collaborative delivery system that integrates the fixed-route coverage of rural buses with the flexibility of multiple types of drones. The proposed system enables synchronized operations between buses and drones, where buses serve as mobile depots for drone launching and recovery along predefined routes. A mixed-integer programming (MIP) model is developed to jointly optimize bus schedules and drone routing under spatiotemporal synchronization constraints, considering drone endurance, payload capacity, energy consumption, and bus departure times. Due to the NP-hard nature of the problem, an Improved Genetic Algorithm (IGA) is designed, incorporating a three-layer encoding scheme, adaptive crossover and mutation operators, and a local search repair mechanism to enhance convergence and solution feasibility. A real-world case study from Baihe County, Shaanxi Province, China, is conducted to evaluate the performance of the proposed model and algorithm. Comparative experiments under the reported case-study setting show that the proposed bus–heterogeneous drone system achieves notable cost reduction and improved overall delivery performance. Sensitivity analyses further confirm the robustness of the model with respect to drone endurance, drone payload capacity, and bus stop quantity. This research contributes to the literature by bridging the methodological gap between truck–drone coordination and bus-based collaborative delivery, offering an innovative framework for sustainable rural logistics and multi-modal last-mile optimization. Full article

Urban last-mile delivery is an increasingly important source of transport-related emissions, yet evidence on low-altitude logistics under real-order demand and urban spatial constraints remains limited. Taking Shanghai as a representative megacity, this study integrates 185,673 real parcel orders with 3D urban spatial data to develop a unified unmanned aerial vehicle (UAV)–courier carbon accounting framework. The framework combines 3D UAV route-planning algorithms, UAV energy-consumption models, electric courier-vehicle energy models, and grid emission factors to compare carbon emissions between UAV and conventional delivery modes. The results show that, under the modeled operating assumptions, UAV delivery tends to provide lower per-delivery carbon emissions under lightweight and high-speed operating conditions. Scenario analysis further suggests that UAV deployment in Shanghai could reduce carbon emissions by approximately 343,300 t CO₂ annually by 2030. These findings provide quantitative support for urban low-altitude logistics planning, infrastructure deployment, and policy design for low-carbon last-mile delivery. The framework is transferable to other Chinese cities with similar urban conditions, but the numerical results require local recalibration of parcel demand, urban morphology, airspace constraints, and electricity-related carbon factors. Full article

The rapid growth of e-commerce has significantly increased direct-to-consumer deliveries, putting competitive and environmental pressure on urban last-mile logistics. Out-of-home (OOH) delivery options, particularly parcel lockers, are increasingly integrated into city mobility strategies to reduce congestion and emissions. However, the role of mobile applications front-ending these networks remains under-researched. This study aims to evaluate the user experience (UX) and functional adequacy across three major parcel-locker apps in Poland: InPost Mobile, DPD Mobile, and ORLEN Paczka. A cross-sectional, mixed-methods approach combining in situ corridor testing and structured post-task questionnaires was employed with 30 users at real locker locations in Katowice. The results indicate that interface simplicity, predictable information flow, and technical stability are the dimensions most consistently associated with higher user ratings. InPost Mobile consistently achieved the highest ratings due to its focus on core workflows, whereas applications emphasizing broader functional coverage (ORLEN Paczka) exhibited usability trade-offs, and DPD Mobile underperformed in speed and stability. Because the study relied on a small convenience sample (n = 30) in a single city and was skewed toward younger adults (18–24), the findings should be interpreted as exploratory and primarily reflective of a digitally proficient demographic rather than the broader user population. Full article

This paper investigates the strategic zone design problem for a bus-based passenger–parcel sharing delivery system. In the envisioned system, parcels are first transported along an existing bus line and transshipped at bus stops, after which dedicated vehicles perform last-mile deliveries from bus stops to customers. By leveraging the underutilized capacity of existing bus services to support parcel distribution, the system contributes to a more resource-efficient and less truck-dependent urban logistics structure, thereby supporting sustainability in urban transportation. The problem is to partition the corridor-level service area into multiple contiguous service zones along the corridor under an m-nearest feasibility requirement. A nonlinear integer programming model is developed that jointly captures parcel and passenger perspectives. On the parcel side, the objective combines zone compactness, parcel-demand balance, and parcel-delivery-distance balance; on the passenger side, it minimizes passenger impact. In this way, the model balances logistics efficiency with social-equity and service-quality considerations and operationalizes sustainability through measurable planning indicators embedded in the objective function. A tailored adaptive large neighborhood search algorithm is proposed to exploit the specific problem structure and solve the model. Case studies based on a real-world bus line in Yancheng, China, illustrate the effectiveness of the proposed method and yield managerial insights into the choice of the number of zones, the influence of passenger-flow patterns, and the role of objective-function weights in shaping trade-offs between parcel delivery efficiency and equity, as well as passenger service quality. Full article

With the rapid development of e-commerce and the increasing complexity of urban logistics, traditional delivery methods face significant challenges due to regional traffic restrictions and congestion. This paper presents a two-stage optimization approach for urban delivery routing, integrating the Split Delivery Vehicle Routing Problem (SDVRP) and truck-drone collaboration to address these challenges. In the first stage, a transportation route optimization model based on SDVRP is proposed, which accounts for regional constraints and vehicle capacity limitations. The model allows for demand splitting, reducing the number of vehicles required and minimizing transportation costs. In the second stage, a truck-drone collaborative delivery model is introduced to handle the “last mile” distribution, where drones complement trucks by delivering to areas with restricted vehicle access. The optimization model aims to minimize overall delivery costs while ensuring timely service. An enhanced genetic algorithm is further developed to solve this complex, multi-constrained model. Experimental results show that the proposed collaborative strategy reduces delivery costs by over 10% compared to truck-only delivery, and the improved algorithm achieves a 4.77% average cost reduction over traditional approaches. This study provides valuable insights for optimizing urban logistics systems under regional constraints, offering both theoretical and practical contributions to smart logistics development. Full article

Curbside saturation in dense commercial corridors compromises the sustainability of last mile logistics. This study investigates the impact of “authorized but non functional occupancy” (Class S (Service)), referring to service and tradespeople vehicles, on the operational capacity of loading and unloading zones (LUZ). Based on direct field observations of 474 real vehicle entries in a zone in Zaragoza (Spain), an Erlang B no wait queuing model (M/M/1/1) using weighted occupancy time was applied to contrast current saturation levels with a regulated functional scenario. The results demonstrate that the infrastructure is structurally sufficient: removing inefficient uses reduces traffic intensity from 1.31 to 0.48 Erlangs, increasing service potential by 121.84%. Class S was identified as consuming 36.62% of the lost capacity, exceeding the impact of unauthorized private cars. Total Hidden Carbon Emissions (HCE) amounted to 45.34 kg CO₂, establishing an environmental impact of 0.066 kg CO₂ per misused linear meter. The study concludes that proper utilization of loading zones is sufficient to accommodate logistics demand and effectively reduce CO₂ emissions. Full article

The dynamic development of the food delivery sector and the resulting increase in last-mile distribution operations generate the need to simultaneously improve the efficiency of delivery processes and reduce the environmental impacts of urban logistics. In this context, shortening delivery time contributes not only to higher service quality and competitiveness but also to lower energy consumption and carbon dioxide emissions, which are key elements of sustainable urban mobility and logistics. Therefore, the aim of this study is to develop a delivery time optimization algorithm for the food delivery sector using selected machine learning methods, supporting the implementation of sustainable development principles in the operations of transport enterprises. This study presents an integrated approach to modelling delivery time in food distribution as a tool for building the competitive advantage of logistics enterprises under the conditions of implementing sustainable development principles. The study combines a literature review on sustainable last-mile logistics and data-driven optimization with an empirical analysis using traditional methods such as multiple regression and selected machine learning methods: decision trees, the Gradient Boosting Machine (GBM) method, and the XGBoost algorithm. The operational data include parameters related to delivery execution, such as supplier characteristics, vehicle type, order execution date, weather conditions and traffic situation. The developed mathematical models enable high-accuracy prediction of delivery time and the identification of the most important factors affecting both timeliness and potential energy consumption in the delivery process. The comparative assessment of the applied methods makes it possible to indicate the algorithms that provide the best forecast quality and practical usefulness in logistics decision-making. The proposed delivery time optimization algorithm supports data-driven decision-making that leads to shorter delivery times and lower energy intensity and thus to a reduction in the carbon footprint of last-mile operations, simultaneously strengthening the competitiveness and environmental responsibility of logistics enterprises. The results contribute to the development of sustainable urban logistics by linking predictive modelling with the economic, environmental and operational dimensions of efficiency in last-mile transport processes. Overall, this study offers an original, high-quality contribution to sustainable last-mile food delivery by integrating large-scale operational data with advanced machine learning models to deliver practically relevant, highly accurate delivery time predictions for logistics enterprises. Full article

The urban remoteness of warehouses and distribution centres, known as logistics sprawl, has been observed for several decades. According to some, this increase in distances between logistics facilities and hypercentres contributes to the environmental worsening of transport operations, especially in densely populated places such as the Paris metropolitan area. Therefore, the question of logistics tightening—the opposite phenomenon—arises in the context of reducing pollutant emissions in the territories concerned. The objective of this work is to clarify the “hidden” mechanisms of freight transport services. It evaluates, through a simulation, the carbon footprint and operational efficiency of logistics tightening in the city of Paris. The input data we use comes from a large courier service company that can be regarded as an interesting case study when it comes to the Paris region. In our scenario, the ecological consistency of the journeys and the logistical requirements of the transport chain may be contested. Indeed, the inner resettlement of hubs for greener deliveries suggests the actual scheme of the company gets closer to optimum and ironically illustrates the relevance of the current locations. Logistics tightening mainly focuses on the last mile, but such a problem is complex, as each link of the chain has its own peculiarities, meaning the sustainability of one can undermine that of another. Full article

Last-mile delivery companies can face demand surges where large-volume order requests exceed daily courier capacity. In such cases fast and robust feasibility-first planning becomes more practical and valuable than building optimal routes. This paper proposes a hierarchical, computationally feasible decomposition pipeline that produces shift-feasible clusters under a strict shift-duration limit using travel-time-based duration estimates. While decomposition methods for large-scale VRPs are well established, they typically remain oriented toward route-construction quality within a single operational day or toward balancing customer counts, demand, or Euclidean territory partitions. In contrast, the proposed method targets a different decision problem: rapid feasibility-first strategic capacity planning for one-time extreme demand surges, where the primary requirement is to estimate, within seconds, a conservative upper bound on the number of courier shifts under a strict shift-duration limit. When end-to-end latency is evaluated from raw geographic points, including distance-matrix preparation for monolithic baselines, the proposed pipeline becomes 187 to 1315 times faster than matrix-based monolithic optimization on the common benchmark sizes. Methodologically, the contribution lies in combining (i) topology-preserving spatial linearization with a Hilbert Space-Filling Curve, (ii) adaptive greedy microclustering driven by empirical travel-time quantiles, and (iii) lexicographic dynamic-programming merge that minimizes the number of shifts first and total travel time second. This yields a planning-oriented decomposition mechanism that is distinct from classical route-quality-centered hierarchical VRP approaches. Full article