Optimization in Routing and Vehicle Selection for E-commerce Last Mile Logistics: Bibliometric Analysis

The last-mile delivery problem is one of the most complex and resource-intensive aspects of modern logistics, especially within the growing e-commerce sector. As online shopping continues to expand, companies are under immense pressure to deliver goods more quickly, efficiently, and at lower costs, all while meeting the demands of increasingly time-sensitive customers. This has created a need for innovative solutions that can tackle challenges related to dynamic traffic patterns, fluctuating customer preferences, and operational constraints such as vehicle capacities and delivery windows. In response to these challenges, this paper explores the application of predictive analytics as a tool for optimizing last-mile delivery routes in real-time.The study begins by identifying the core challenges inherent in last-mile logistics, particularly in the U.S. e-commerce landscape, where the cost of last-mile delivery can represent up to 53% of total shipping costs. With traffic congestion, unpredictable customer availability, and delivery time constraints posing significant hurdles, conventional static route planning models often fall short. In this paper, predictive analytics is proposed as a solution to these challenges, utilizing real-time data to inform more efficient routing decisions. By processing vast amounts of real-time traffic data, customer preferences, and delivery constraints, predictive models can offer a more flexible and responsive approach to last-mile delivery.The research then presents a comprehensive literature review of existing route optimization methods, such as the traditional Vehicle Routing Problem (VRP) and its extensions, including VRP with Time Windows (VRPTW), Dynamic VRP (DVRP), and Capacitated VRP (CVRP). While these models have proven useful, their limitations are exposed when faced with real-time operational complexities in the e-commerce sector. Therefore, this study introduces an advanced dynamic routing model that integrates machine learning algorithms—such as decision trees and neural networks—with traditional VRP frameworks. These machine learning models, trained on historical data, are capable of predicting future traffic patterns, customer behavior, and delivery time windows.A case study is conducted using data from U.S.-based e-commerce companies to demonstrate the practical application of predictive analytics in optimizing last-mile delivery. The case study outlines how predictive models are used to dynamically adjust delivery routes based on real-time conditions, leading to significant improvements in efficiency, cost savings, and customer satisfaction. Key performance indicators such as delivery times, fuel consumption, and vehicle utilization are examined before and after the implementation of the predictive models, with the results showing a reduction in delivery time by 20% and fuel costs by 15%, alongside improved on-time delivery rates.The paper concludes by presenting the proposed dynamic route optimization model as a solution that combines the flexibility and responsiveness of predictive analytics with the robust framework of traditional VRP models. Through the integration of machine learning, real-time data processing, and dynamic routing, the model is shown to significantly improve last-mile delivery efficiency. This study's findings highlight the potential for predictive analytics to revolutionize the logistics industry, particularly in the high-demand e-commerce sector, where quick and reliable delivery is paramount. The research suggests that as e-commerce continues to grow, predictive analytics will play an increasingly critical role in ensuring that last-mile delivery is both cost-effective and responsive to the evolving needs of consumers.

The use of electric vehicles in urban transportation is increasing daily due to their energy efficiency and environmental friendliness. In last-mile logistics, route optimization must consider charging station locations while balancing operational costs and customer satisfaction. In this context, solutions for cost-oriented route optimization have been presented in the literature. On the other hand, customer satisfaction is also important for third-party logistics companies. This study discusses the Capacitated Electric Vehicle Routing Problem with Time Windows (CEVRPTW) encountered in last-mile logistics. This article defines the objective function of minimizing total tardiness and compares the routes between the service provider logistics company and the customer receiving the service. In this study, the CEVRPTW was solved for the minimum total tardiness objective function with the hybrid adaptive large neighborhood search (ALNS) algorithm. The success of ALNS was proven by comparing the differences between the optimal solutions obtained with the CPLEX Solver and the ALNS solutions. Tardiness objective function-specific operators for ALNS are proposed and supported by local search and VNS algorithms. The findings of this study contribute to the literature by analyzing the balance trade-offs between customer-oriented and cost-oriented and the effect of time windows on the number of vehicles.

Reducing emissions in last-mile logistics is a growing priority, and digitalization is widely viewed as a pathway to greater efficiency and decarbonization. Proof of Delivery (POD) is central to digital transformation, yet its environmental impacts remain underexplored from a life cycle assessment (LCA) perspective. This study conducts a comparative LCA of four POD configurations—paper-based, digitally scanned, AI-assisted, and blockchain-integrated—using data from a Taiwanese third-party logistics provider. We quantify cradle-to-grave emissions from materials, energy use, and digital infrastructure, including AI inference and blockchain operations. The paper-based system shows the highest footprint (222.3 gCO2e/transaction). Digital scanning reduces emissions by 78.0%, AI-assisted systems by 73.0%, and blockchain-enabled systems by 63.8%, reflecting high consensus energy demand. A hybrid approach—retaining paper at delivery while digitizing back-office processes through AI-supported optical character recognition—emerges as a pragmatic transition pathway. These findings provide transaction-level LCA evidence to inform scalable decarbonization strategies in last-mile logistics.

Urban last-mile logistics systems must improve service responsiveness while reducing environmental impact. While micromobility-based delivery fleets offer significant emission advantages compared to conventional vans, their operational efficiency depends on adaptive, data-driven capacity allocation. We develop and analyze a real-time optimization framework that explicitly integrates sustainability considerations into zone-level fleet allocation decisions. The continuous-time backlog dynamics admit a closed-form discrete-time prediction, enabling computationally efficient rolling-horizon fleet reallocation. Sustainability is explicitly embedded through zone-specific emission factors and a multi-criteria objective function balancing backlog reduction, environmental impact, and operational stability. In a ten-zone numerical case study with a fleet of 40 vehicles, the proposed method reduced backlog in all zones within a 15-min interval while preserving strict feasibility and stability (spectral radius is less than 1). The framework also demonstrated a controllable emission–service trade-off via sensitivity analysis. These results suggest the practical applicability and real-time suitability of the proposed Industry 4.0-aligned optimization approach.

In the contemporary landscape of global logistics, the capacity to dynamically optimize delivery routes in real time is a critical determinant of operational efficiency, customer satisfaction, and environmental sustainability. Conventional routing algorithms, while effective in static or semi-dynamic contexts, often fail to adapt to rapid changes in traffic conditions, weather disruptions, road closures, and last-minute delivery requests. This paper proposes a novel Deep Learning-based architecture that leverages Long Short-Term Memory (LSTM) networks for real-time traffic forecasting and Deep Q-Networks (DQNs) for adaptive route decision-making. The system processes live inputs from GPS sensors, weather APIs, and traffic feeds to dynamically generate optimal delivery paths. Extensive simulations using synthetic and real-world datasets from urban logistics providers demonstrate substantial improvements in delivery time (up to 21%), fuel consumption (13%), and vehicle utilization rates (17%) compared to traditional heuristics-based methods. This research provides both theoretical contributions and practical implementation guidelines for logistics operators seeking intelligent, AI-driven solutions for route optimization.

In integrating artificial intelligence (AI) into autonomous drone technology, researcher seen how autonomous drone technology switched from last mile delivery for delivering goods to enhance efficiency, reduce the operational costs, and minimize environmental impact. In this study, researcher try to understand the applying of AI ways on navigation systems based on deep learning, reinforcement learning, and computer vision that make it possible to optimize a route and also to keep away from obstacles. In the design and testing of AI-powered drones, the research uses a mix of simulated environments and actual environments to determine the performance in urban and rural settings. Results show that AI based navigation greatly enhances the delivery speed, energy and accuracy as opposed to conventional delivery approaches. The discussion also enumerates the advantages and disadvantages of using AI powered drones given the regulatory barriers, weather constraints, public acceptance issues. Future research can be focused on making AI more adaptable in dynamic environments by blockchain integration for the secure tracking of package as well as use of 5G network for real-time control. The recommendations are: to develop standardized regulations for drone logistics, enhance the collaboration between the AI researcher and the policymakers and also improve the public awareness of the autonomous drone operations. The results lay the grounds for smart logistics to arrive at sustainable solutions for the last mile delivery of the future.

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.

The rise in complexity of large scale IoT-enabled drone logistics networks would require intelligent computing systems that can autonomously make optimal scheduling and routing decisions. Since drones are highly important in real-time delivery and surveillance services, the planning of routes and flexible scheduling are critical to the improvement of the reliability and scalability of the operations. The current means have shortcomings in terms of lack of flexibility, routing algorithms that are fixed and lack the responsiveness to the dynamic aspects of the environment like weather, air traffic and the varying need to deliver goods. To eliminate these constraints, this paper proposes the Reinforcement Learning-Based Adaptive Route Optimization (RL-ARO) model, which incorporates Deep Reinforcement Learning into the IoT-based contextual information to optimize routes and schedule. The framework allows multi-agent drones to jointly discover the best flight paths because they constantly adapt to the current network conditions, resource limitations, and energy efficiency needs. The suggested RL-ARO algorithm is implemented in a smart healthcare delivery system for the real-time medical supply and vaccine delivery environment with minimal latency and energy usage. The results of the experiment show that the framework can dramatically increase the efficiency of the route, decrease the delivery time by more than 35%, and increase the resilience of the system in large-scale and dynamical domains. The proposed method achieves the average time gap after 150 missions, energy consumption reduction (850 wh), route optimality (> 90), adaptation response time (6 s), packet deliver ratio (96–99). The RL-ARO framework optimizes drone routes using reinforcement learning and IoT data for dynamic conditions. The system reduces delivery time by 35%, increases energy efficiency, and ensures high reliability in real-time logistics. RL-ARO provides scalable and resilient drone operations, adapting quickly to environmental and operational changes.

As an essential building block for smart grid, the industrial internet of things (IIoT) plays a significant role in providing powerful sensing capability and ubiquitous connectivity for differentiated power services. The rapid development of smart grid imposes higher data monitoring and transmission requirements in terms of delay and energy efficiency. However, due to the severe electromagnetic interference (EMI) caused by massive electrical equipment, the transmission performance of IIoT becomes inferior. The traditional single‐hop transmission mode evolves towards a multihop cooperation mode to satisfy differentiated quality of service (QoS) requirements. In this paper, we propose an upper confidence bound‐ (UCB‐) based joint route and power selection optimization algorithm to support multihop cooperation mode evolution, which adopts a software‐defined networking‐ (SDN‐) enabled IIoT network framework to simplify network configuration and management. Compared with existing local‐side‐information‐based route selection (LSI‐RS) and random route selection (RRS) algorithms, simulation results demonstrate that the proposed algorithm has superior performances in total delay, energy efficiency, and utility.

Energy efficiency in wireless sensor networks is an important concern of ‘green’ Internet of Things. The existing energy-saving routing protocols usually compute the route in isolation, which is disadvantageous to globally optimize the energy consumption. In this article, we propose a novel energy-saving route optimization solution based on the software-defined wireless sensor network. In our proposed solution, the controller obtains the overall information about the network topology and the existing routes. Based on the above information, this solution centrally optimizes the routes in a global manner. In contrast to the existing energy-saving routing protocols, the central route optimization better reduces the energy consumption from an overall perspective. The global route optimization tends to reduce the overall energy consumption using a small number of sensors, which can improve the effectiveness of the used sensors. However, using a small number of sensors leads to high energy consumption of related sensors. To solve the above problem, our proposed solution uses a routing evasion mechanism to balance the energy consumption between sensors.

This dissertation explores the intersection of Last-Mile Delivery, Autonomous Vehicles, and Multimodal Transport, collectively referred to as Autonomous Multimodal Last-Mile Delivery. The research applies Operational Research techniques to assess the potential benefits of autonomous vehicles, such as drones and self-driving robots, in overcoming the challenges faced in last-mile delivery. The main objective is to explore how service providers can leverage multimodal delivery systems, integrating trucks, drones, and self-driving robots, to enhance efficiency.<br/><br/>This dissertation follows two main directions. The first involves designing delivery systems that address urban last-mile logistics challenges. This includes optimizing interactions between different vehicles and modeling these systems using Mixed-Integer Linear Programs (MILPs). The second direction is developing and analyzing optimization techniques tailored to solve the autonomous multimodal last-mile delivery problems introduced earlier. <br/><br/>Through this research, we provide valuable insights into the integration of autonomous vehicles in multimodal delivery systems, offering potential solutions for improving efficiency and addressing the complexities of last-mile delivery in urban environments.

Modern logistics requires effective solutions for the optimization of intermodal transportation, providing cost reduction and improvement of transport flows. This paper proposes a multi-objective optimization method for intermodal freight transportation planning within the framework of sustainable service network design. The approach aims to balance economic efficiency and environmental sustainability by minimizing both transportation costs and delivery time. A bi-criteria mathematical model is developed and solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), which is well-suited for handling complex, large-scale optimization problems under multiple constraints. The aim of the study is to develop and implement this technology that balances economic efficiency, environmental sustainability and manageability of operational processes. The research includes the development of a two-criteria model that takes into account both temporal and economic parameters of the routes. The optimization method employs the NSGA-III, a well-known metaheuristic that generates a diverse set of near-optimal Pareto-efficient solutions. This enables the selection of trade-off alternatives depending on the decision-maker’s preferences and specific operational constraints. Simulation results show that the implementation of the proposed technology can reduce the costs of intermodal operators by an average of 8% and the duration of transportation by up to 50% compared to traditional planning methods. In addition, the automation of the process contributes to a more rational use of resources, reducing carbon emissions and increasing the sustainability of transportation networks. This approach is in line with the principles of sustainable economic development, as it improves the efficiency of logistics operations, reduces pressure on infrastructure and minimizes the environmental impact of the transport sector. Route optimization and digitalization of transport processes can increase resource efficiency, improve freight flow management and contribute to the long-term stability of transport systems. The developed technology of automated planning of intermodal transportation is oriented to application in large-scale production systems, providing effective management of cargo flows within complex logistics chains. The proposed method supports the principles of sustainable development by providing a formal decision-making framework that balances transportation cost, delivery time and environmental objectives. Instead of optimizing for a single goal, the model enables the identification of efficient trade-offs between economic performance and ecological impact. Moreover, by generating multiple routing scenarios under varying operational constraints, the approach enhances the adaptability and robustness of freight transportation systems in dynamic and uncertain environments.

Uninhabited Aerial Vehicles will require autonomous collisio n detection and avoidance capabilities in order to gain wide acceptance and usage in the National Airspace System. The collision detection and avoidance problem is primarily a motion estimation problem, relying on the accuracy of the sensing, processing, actuation and control algorithms onboard the vehicle. Computer vision is a growing field in robotics and autonomous vehicles, and has made vision a feasible technology for object detection in different platforms. In this work, we investigate the feasibil ity of using optic flow techniques in order to design an altitude hold controller for a fixed wing aircraft flying a nap -of -the -earth trajectory in the presence of variable terrain. Simulated results yield an error of approximately 1.5% in altitude variat ion for steady level flight (approximately 15 cm) for terrain disturbances of maximum amplitude 4 m. Experimental results obtained from flight data using the UIUC UAV platform are used to validate the controller, and sources of error along with potential improvements in equipment and procedures are cited. I. Introduction N essential challenge inherent to the integration of uninhabited aerial vehicles (UAVs) into the National Airspace System (NAS) is the inability to provide adequate safety assurances for th ese vehicles in a verifiable manner. The FAA requires that UAVs be capable of see -and -avoid functionality, unfortunately most solutions to the problem of autonomous surveillance, guidance and navigation are insufficiently scalable, reliable, verifiable or cost -effective. Implementation of safe and reliable object avoidance is crucial for the survival of autonomous vehicles intended to navigate in densely populated environments such as the NAS. In order to meet these stringent requirements, UAVs must be eq uipped with reactive sensing technologies that enable real -time conflict detection and object avoidance. Numerous techniques have been developed in the past few years to optimize the decision making process and solve the path planning problem in real -time , while accounting for aircraft dynamics, obstacle avoidance constraints and safe flight considerations. Regrettably, many of these techniques underestimate the difficulty of accurately sensing a dynamic and imperfect environment in an accurate and practic al manner. Making decisions based on distorted estimations of the surrounding environs is a pitfall that can destabilize even the most robust of path planning algorithms. Presently, state -of -the -art conflict detection and resolution systems employ a combi nation of radar, sonar and laser systems: while reliable, these systems are often prohibitive due to weight, size and equipment cost for certain UAV applications. Video recording devices are another alternative for environmental sensing, and have become standard equipment on many UAVs due to their exploratory nature. Although video images from this source are often intended for secondary mission purposes, such as target tracking and surveillance, they have been shown capable of providing valuable informati on for more important tasks, like collision avoidance. However, the nature of a fixed wing UAV platform brings rigorous safety requirements and constraints to the field of autonomous conflict detection and resolution: limited minimum flying speeds and res tricted maneuverability, thereby making the need for real -time sensing and decision -making paramount for safety purposes. In this paper we investigate the applicability of c omputer vision techniques that have been developed in order to estimate motion from two dimensional images. In the next section we provide a brief discussion of optical flow theory, and section 3 provides a direct implementation of the optical flow theory to an aircraft altitude controlle r. Section 4 provides the setup and simulation of several scenarios used to evaluate the performance of the controller designed in the previous section. Experimental video data taken from actual flight tests is used in order to asses the 1

This paper comprehensively examines passive and active energy retrofit strategies as a highly effective approach for reducing building energy consumption and mitigating CO2 emissions while enhancing comfort and sustainability. The paper further examines energy simulation software and assesses the integration of renewable energy systems in building to improve energy efficiency. The review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, ensuring a rigorous and comprehensive analysis. In addition, the study utilized bibliometric analysis with VOSviewer to provide valuable insights into the research trends and influential publications in building energy retrofits. Bibliometric analysis reveals strong collaboration among 17 authors, emphasizing their significant contributions. Keywords like energy retrofitting and efficiency are prominent, indicating their importance in academic literature. Findings show passive strategies are more effective in reducing energy consumption, though a combined approach with active strategies can yield optimal results. Retrofitting presents challenges, such as substantial initial costs and regulatory barriers. User acceptance is crucial, considering potential disruptions. The review highlights the importance of energy simulation software, with tools like EnergyPlus, eQUEST, and IES VE identified for evaluating and identifying cost-effective retrofit measures in building performance. By providing comprehensive insights into the various strategies and tools available for retrofitting buildings to achieve energy efficiency and sustainability goals, this review serves as an authoritative resource for building owners, managers, and professionals in the building industry. It offers invaluable guidance for informed decision-making and facilitates implementing effective, energy-efficient, and sustainable building retrofitting practices.

Purpose This study explores renewable energy research in Oman, focusing on growth, trends and economic impacts and identifies key contributors, emerging themes and economic ties influencing development. Design/methodology/approach A bibliometric analysis of 204 studies was conducted to evaluate collaboration networks, citation trends and research performance. Additionally, a vector autoregression (VAR) model was used to examine the associations between economic variables such as economic growth, foreign direct investment and energy consumption. Findings The findings indicate a consistent increase in research on renewable energy, with publications increasing by 6.3% annually from 2020. Keyword and trend analyses highlight a growing emphasis on regulatory frameworks, with terms such as “law” emerging. A keyword network analysis revealed that renewable energy research in Oman is significantly influenced by regional economic and environmental factors. The VAR model further supports the role of renewable energy in Oman’s economic growth, showing strong correlations between energy consumption, foreign direct investment (FDI) and economic expansion. Research limitations/implications The study's data were limited by secondary databases and VAR analysis, suggesting the need for larger datasets and advanced modelling techniques for more accurate results. Originality/value This study explores renewable energy research and its economic impact in Oman, providing insights for researchers, policymakers and industry stakeholders on sustainable energy transitions and economic growth. It uniquely combines bibliometric and VAR analyses for a country-specific assessment, quantifying interrelationships between energy consumption, investment and economic output. The findings offer a replicable framework for emerging economies, delivering long-term guidance for policy, investment and sustainable energy planning internationally.