Optimal Routing Algorithm Research Articles

Intelligent Waste Management Optimization Through Machine Learning Analytics

Waste management presents serious obstacles to making metropolitan regions more habitable. Conventional waste management techniques are not usually optimized, resulting in overflowing bins, wasteful waste collection trips, and various negative environmental effects. This study addresses these challenges by developing an intelligent system integrating the Internet of Things (IoT) and machine learning technologies. This study aims to develop an intelligent waste management system that optimizes waste collection routes and schedules through machine learning. (ML) models and Internet of Things (IoT) powered smart bins. The system utilized Support Vector Machines (SVM) and Artificial Neural Networks (ANN) for data analysis, complemented by dynamic route optimization algorithms. Data collection over 90 days across 47 sites encompassed bin fill levels, battery status, and environmental parameters such as temperature and humidity. Results demonstrated significant operational improvements, with the system achieving 89% accuracy in fill-level prediction and enabling a 35% reduction in collection frequency. Implementation led to a 42% decreased fuel consumption and a 2.4-hour reduction in daily collection times. Commercial zones exhibited 1.8 times higher fill rates than residential areas, while weekend waste generation peaked at 2.1 times weekday. The findings indicate that IoT-ML technology integration substantially enhances urban waste management efficiency through data-driven decision-making. Phased implementation, prioritizing high-waste-volume areas, integrating with existing metropolitan systems, and developing standardized data protocols are recommended. This research contributes to the growing body of evidence supporting smart technology adoption in urban waste management, offering a scalable solution for improved operational efficiency and environmental sustainability.

Application of Scene Perception and Object Detection Technology Based on Computer Vision in Autonomous Driving for Public Transportation Route Optimization

The aim of this article is to discuss the use of computer vision and GIS-based scene perception for autonomous driving in route planning for public transportation. Blending computer vision with GIS information, the design dynamically alters routes based on the environments dynamic variations, such as traffic volume, surface, and obstruction. The system design comprises data acquisition modules, computer vision engine, GIS connectivity, and adaptive route optimization algorithm. Results from virtual cities reveal significant savings in travel time, energy use and flexibility compared to fixed-route approaches. Furthermore, the system works well and offers high detection performance in all kinds of environments. We describe how it might benefit urban transport infrastructure by describing how AVs with real-time vision can optimize routes to speed up time, eliminate delays, and increase safety. Future studies will focus on computing burden and performance under adverse weather conditions, assessing how new technologies like 5G and IoT could provide scalability. This research helps build safer, smarter and more adaptive public transport in cities.

Improve customer service quality, reduce operating expenses, and improve energy sales by the K-MEANS method and path optimization algorithms

Managing consumer expectations was essential to maintaining customer satisfaction throughout the electricity contract. However, the service provided to customers was based on the location of electrical meters. In the absence of addressing in rural areas, it was too difficult to ensure a comprehensive survey of electrical meter indexes and intervene in time for troubleshooting. The method adopted was the choice of a site with a significant number of meters and energy transformers and the geolocation of electrical installations by a GPS that allowed the assignment of a universal address to electrical installations and facilitated the location of facilities for maintenance and emergency response. The study of optimization algorithms has directed the choice toward the algorithm of the nearest neighbor that remains fast and aims to other algorithms whose number of iterations can be exponential (n! iterations) but less exact. The integration of route optimization algorithms has improved the reactivity of technicians, reduced operational costs, and ensured accurate reading of indexes and transparent billing. The study presents a specific case in Morocco, where route optimization based on GPS coordinates of électric meters in reading indexes has significantly improved efficiency and customer satisfaction. In addition, the K-Means method was used to determine the centroids and clusters that represent respectively the transformation stations and the groups of electrical meters. These groupings allow the calculation of energy sales by transformer station to increase them by reducing energy losses

RRO: A Regularized Routing Optimization Algorithm for Enhanced Throughput and Low Latency with Efficient Complexity

RRO: A Regularized Routing Optimization Algorithm for Enhanced Throughput and Low Latency with Efficient Complexity

Automated Transport Management System: Route Optimization and Attendance Tracking for Institutions

Educational institutions face significant challenges in managing student transportation, such as inefficient route planning, manual attendance tracking, and the lack of real-time communication. These issues contribute to delays, operational inefficiencies, and safety concerns, leaving students, parents, and administrators with limited access to critical information about transportation logistics. Existing literature highlights advancements in transportation management through IoT, RFID, GPS, and AI, revealing both the potential of these technologies and their limitations in addressing hardware dependencies, network constraints, and manual interventions. Notable research includes IoT-based systems for bus tracking and attendance, Wi-Fi-enabled attendance solutions, and multiagent routing optimization algorithms, which underscore the effectiveness of real-time tracking and automation but reveal gaps in scalability, cost-effectiveness, and hardware reliability. To bridge these gaps, this project introduces the Automated Transport Management System (ATMS), a comprehensive platform designed to enhance efficiency, safety, and communication in student transportation. ATMS integrates GPS for real-time route visualization, barcode-based automated attendance logging, and proactive notifications accessible through a user-friendly web interface. This approach minimizes manual errors, reduces wait times, and ensures reliable service even in areas with variable connectivity, providing an efficient and flexible solution for educational institutions. By continuously evolving its features, ATMS aims to establish a new standard in transportation management, promoting student safety and operational effectiveness.

Optimizing food transportation processes for grocery stores across the US

In the rapidly evolving landscape of food logistics, optimizing transportation processes for grocery stores across the United States has become paramount to enhancing operational efficiency and ensuring the delivery of high-quality, perishable goods. This article examines the multifaceted challenges inherent in food transportation, including logistical complexities, environmental concerns, and rising operational costs. It highlights the importance of data-driven strategies, such as route optimization and machine learning algorithms, to improve delivery times and reduce waste. Additionally, the integration of advanced technologies, such as Internet of Things (IoT) sensors for temperature monitoring and automation in warehouse management, is explored to enhance the cold chain and ensure food safety. Moreover, the article emphasizes the critical role of strategic partnerships with third-party logistics providers and suppliers in streamlining operations and enhancing overall performance. Through in-depth case studies of successful grocery chains, it illustrates effective practices that have led to significant improvements in food transportation processes. The regulatory landscape surrounding food transportation is also analysed, underscoring compliance with safety standards and sustainability initiatives. Finally, the article looks ahead to future trends, including the potential impact of autonomous vehicles, blockchain technology, and sustainable practices on the food supply chain. By providing a comprehensive overview of best practices and innovative solutions, this article aims to equip grocery store operators and stakeholders with actionable insights to optimize their food transportation processes, ultimately leading to improved customer satisfaction and reduced environmental impact.

GTD3-NET: A deep reinforcement learning-based routing optimization algorithm for wireless networks

GTD3-NET: A deep reinforcement learning-based routing optimization algorithm for wireless networks

GSADDQN: Combining GraphSAGE and reinforcement learning for routing optimization in software-defined optical transport network

As a result of rapid development of network communication technology, optical transport network (OTN) traffic has also experienced rapid growth in terms of information volume scale, traffic complexity, and spatiotemporal distribution dynamics. Because the OTN traffic demand has complex spatiotemporal fluctuations, the traditional deep reinforcement learning (DRL) algorithm has been applied to the routing optimization of software-defined OTNs. However, the traditional DRL algorithm has problems such as slow convergence, weak generalization ability, and load imbalance when performing routine tasks. To address these issues, we propose a graph sampling and aggregation (GraphSAGE)-based dueling deep Q-network (GSADDQN) algorithm for software-defined OTN routing optimization. First, we design a DRL-based routing decision model to find the best routing strategy for each optical network’s source–destination traffic demand. Second, considering the sparse connection characteristics of optical network nodes, we use sampling neighbors and a deep aggregation mechanism as the neural network model of the Dueling Deep Q-Network (Dueling DQN) algorithm so that the reinforcement learning agent can consciously aggregate important network information and improve the model’s convergence performance and generalization ability. Finally, we design simulation routing experiments based on Gym and evaluate the algorithm’s load balancing and generalization capabilities for different network topologies. The experimental results show that the GSADDQN algorithm has good convergence performance and load balancing ability in routing optimization of optical transmission networks and can generalize new network structures, maintaining good decision-making ability even during network node failures.

Q-Learning-Based Routing Optimization Algorithm for Underwater Sensor Networks

Q-Learning-Based Routing Optimization Algorithm for Underwater Sensor Networks

Combining OpenStreetMap mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile.

Community health programs are gaining relevance within national health systems and becoming inherently more complex. To ensure that community health programs lead to equitable geographic access to care, the WHO recommends adapting the target population and workload of community health workers (CHWs) according to the local geographic context and population size of the communities they serve. Geographic optimization could be particularly beneficial for those activities that require CHWs to visit households door-to-door for last mile delivery of care. The goal of this study was to demonstrate how geographic optimization can be applied to inform community health programs in rural areas of the developing world. We developed a decision-making tool based on OpenStreetMap mapping and route optimization algorithms in order to inform the micro-planning and implementation of two kinds of community health interventions requiring door-to-door delivery: mass distribution campaigns and proactive community case management (proCCM) programs. We applied the Vehicle Routing Problem with Time Windows (VRPTW) algorithm to optimize the on-foot routes that CHWs take to visit households in their catchment, using a geographic dataset obtained from mapping on OpenStreetMap comprising over 100,000 buildings and 20,000 km of footpaths in the rural district of Ifanadiana, Madagascar. We found that personnel-day requirements ranged from less than 15 to over 60 per CHW catchment for mass distribution campaigns, and from less than 5 to over 20 for proCCM programs, assuming 1 visit per month. To illustrate how these VRPTW algorithms can be used by operational teams, we developed an "e-health" platform to visualize resource requirements, CHW optimal schedules and itineraries according to customizable intervention designs and hypotheses. Further development and scale-up of these tools could help optimize community health programs and other last mile delivery activities, in line with WHO recommendations, linking a new era of big data analytics with the most basic forms of frontline care in resource poor areas.

Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review

Numerous devices, including sensors, RF ID, and other types of smart devices, have been developed as a result of the Artificial Intelligence (AI), and Internet of Things (IoT) revolution. Urban areas can become smart by monitoring and collecting data about their surroundings through the deployment of technologies with powerful computational capabilities and those that are converted into intelligent things. Waste management is among the most significant issues in smart cities, a rise in metropolitan regions and faster increases in population are the main reasons. When it comes to gathering data about waste management, intelligent services can serve as the front line. Waste management with IoT support is a common example of a service offered by smart cities. Various duties, like gathering, processing, and use of waste in appropriate facilities, are included in waste management. The present study proposed an updated waste management system architecture design after reviewing existing artificial intelligence and IoT-based waste management systems and automation in smart cities. The proposed system architecture deals with the automation of municipality trash in smarter urban areas, using IoT technology and sending notification messages based on sensor data relating to the dustbin state, such as full or empty. The notifications are sent simultaneously to the municipality office and the waste carrier vehicle driver, so that waste can be emptied on time. The proposed system architecture represents a scalable and adaptable model for municipalities that aim to transform their waste collection processes and play a key step in minimizing municipality waste in smart cities. By deploying this proposed system architecture with smart sensors and IoT devices, municipalities can monitor waste levels to ensure that bins are emptied when it is necessary. This reduces the frequency of waste collection, lowers fuel consumption, and minimizes operational costs. The Route optimization algorithms further enhance efficiency by determining the most efficient paths for waste collection trucks, so they can reduce travel time and fuel emissions.

UAV p­­­arcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization

Unmanned Aerial Vehicles (UAVs), or drones, have recently become a favorable solution for fast parcel delivery due to their maneuverability and advances in navigation technologies. With the limitation of battery capacity and payload of drones, it is crucial to consider both efficiency and cost while conducting the tasks. Meanwhile, UAVs should not collide with each other while traveling to customers. In this article, we propose a UAV parcel delivery system involving deep reinforcement learning (DRL) approach for collision avoidance and a genetic algorithm for route optimization. Specifically, a delivery center generates near-optimal routes, loading UAV with parcels according to demands. Each UAV takes charge of delivering packages in compliance with the assigned route while avoiding collision with each other. We utilize DRL to achieve collision avoidance without having prior knowledge about the trajectories of other UAVs. Additionally, we adopt a genetic algorithm to obtain the lowest energy cost path for each UAV. To find such an optimized path, we solve a capacitated vehicle routing problem (CVRP) with a modified cost function and extra constraints. Realistic simulations using a physics engine and software-in-the-loop (SITL) are conducted to evaluate the feasibility of the proposed methods.

Comparative Analysis of the MDORA and AODV Routing Protocols for Vehicles Operating at Constant and Variable Speeds

The primary focuses of investigation have been and continue to be strategies for traffic management at intersections and measures to enhance road safety. An essential remedy for this issue is a Vehicular Ad-hoc Network (VANET). At VANET, a variety of protocols are used to route communications between vehicles. At this research, we utilize the Multipath Dynamic Optimized Routing Algorithm (MDORA), which is an acronym for position-based maximum distance on-demand routing. By choosing the vehicle closest to the destination vehicle, this protocol has the advantage of choosing the next best jumping path, which lowers the number of hops. In two scenarios—vehicle travel at a dynamic speed and constant speed—this paper compares the MDORA and AODV protocols. three standards: Factors including the time spent on communication, the pace at which packets are delivered, and the time it takes for information to travel from one end to another, were used to assess how well the two protocols performed. Finally, a comparison of the number of missed packets between the MDORA and AODV protocols was done for moving vehicles at constant and dynamic speeds, The MDORA protocol can identify the optimal route even when cars are moving at varying speeds, resulting in minimal packet loss.

Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors.

Wireless sensor networks (WSNs) are structured for monitoring an area with distributed sensors and built-in batteries. However, most of their battery energy is consumed during the data transmission process. In recent years, several methodologies, like routing optimization, topology control, and sleep scheduling algorithms, have been introduced to improve the energy efficiency of WSNs. This study introduces a novel method based on a deep learning approach that utilizes variational autoencoders (VAEs) to improve the energy efficiency of WSNs by compressing transmission data. The VAE approach is customized in this work for compressing WSN data by retaining its important features. This is achieved by analyzing the statistical structure of the sensor data rather than providing a fixed-size latent representation. The performance of the proposed model is verified using a MATLAB simulation platform, integrating a pre-trained variational autoencoder model with openly available wireless sensor data. The performance of the proposed model is found to be satisfactory in comparison to traditional methods, like the compressed sensing technique, lightweight temporal compression, and the autoencoder, in terms of having an average compression rate of 1.5572. The WSN simulation also indicates that the VAE-incorporated architecture attains a maximum network lifetime of 1491 s and suggests that VAE could be used for compression-based transmission using WSNs, as its reconstruction rate is 0.9902, which is better than results from all the other techniques.

An interaction-enhanced co-evolutionary algorithm for electric vehicle routing optimization

Recently, the electric vehicle routing problem (EVRP) has emerged as a significant challenge in the transportation field. Unlike traditional vehicle routing problems, EVRP requires optimizing not only the customer route but also the charging scheme. This implies that routes must be planned while considering the availability of charging stations, and charging decisions must be made based on the specific route structure. Although the dual-population-based co-evolutionary algorithm (DPCA) has been proposed to address this coupling relationship, there is still room for further performance improvement. Therefore, this paper proposes an interaction-enhanced co-evolutionary algorithm (IECA) that facilitates a more effective collaborative search between routing and charging optimizations. For routing optimization, we propose an improved ant colony optimization method with a novel pheromone update approach to generate diverse route solutions. Specifically, we utilize information from the charging population to guide the pheromone update process. In contrast to DPCA, which solely relies on the best solution, we establish interaction among multiple solutions from both the routing and charging populations, which can better contribute the information interaction between two populations. Furthermore, we devise a novel population update strategy that enhances the selection of promising solutions. Experimental results validate the effectiveness of the proposed algorithm by demonstrating its ability to avoid local optima. Moreover, it achieves a reduction of approximately 4% in the average route distance across two public test suites. The source code is available at https://github.com/ShouLiang-Z/IECA.

Logistics Routing Intelligence based on Improved Ant Colony Algorithm and Dijkstra Algorithm

As the domestic logistics industry still faces challenges in terms of efficiency and cost-effectiveness, the optimization of logistics routes is crucial to address these challenges. The purpose of this study is to explore the potential of using ant colony algorithm and route optimization algorithm to realize intelligent optimization of logistics routes. It first introduces the principle of ant colony algorithm and its application in path optimization, and then proposes a path intelligent optimization method based on ant colony algorithm by introducing pheromone concentration and heuristic information, which improves the efficiency and accuracy of the algorithm. Finally, the experimental results show that the improved ant colony algorithm can better plan the path than the traditional ant colony algorithm, and can improve the convergence speed of calculation, and can achieve higher optimization accuracy and calculation efficiency, so that it is suitable for the practical application in the field of logistics path intelligent optimization.

Optimizing last-mile delivery in E-Commerce logistics a multi-faceted approach

Efficient last-mile delivery is crucial in e-commerce logistics, impacting customer satisfaction, costs, and sustainability. This abstract explores strategies for optimization, utilizing a multi-faceted approach. Advanced technologies like route optimization algorithms and real-time tracking systems enhance delivery efficiency by minimizing transit times and optimizing resource use. Data analytics further refine routes based on variables like traffic and weather. Collaborative partnerships and decentralized logistics networks reduce individual deliveries and environmental impact. Micro-fulfilment centres in urban areas expedite deliveries, especially in densely populated regions. Customer-centric strategies include personalized delivery options and proactive communication. Time-specific delivery windows and alternative delivery locations cater to diverse preferences, enhancing satisfaction and loyalty. Transparent tracking mechanisms reduce failed deliveries and returns. In general, a multi-faceted approach combining technological innovation, collaboration, and customer-centricity is vital for optimizing last-mile delivery. By synergistically implementing these strategies, e-commerce stakeholders can overcome challenges, driving efficiency, satisfaction, and sustainability in the last mile.

Distributed Routing Optimization Algorithm for FANET Based on Multiagent Reinforcement Learning

Distributed Routing Optimization Algorithm for FANET Based on Multiagent Reinforcement Learning

Waste management 2.0 leveraging internet of things for an efficient and eco-friendly smart city solution.

Waste management poses a major challenge for cities worldwide, with significant environmental, economic, and social impacts. This paper proposes a novel waste management system leveraging recent advances in the Internet of Things (IoT), algorithms, and cloud analytics to enable more efficient, sustainable, and eco-friendly waste collection and processing in smart cities. An ultrasonic sensor prototype is tailored for reliable fill-level monitoring. A LoRaWAN and cellular network architecture provides city-wide connectivity. A cloud platform handles sensor data storage, processing, and analytics. Dynamic route optimization algorithms minimize time, distance, and fuel use based on real-time bin data. Extensive pilot studies in 10 different locations across Lahore, Pakistan, validated the system, processing over 200 million data points. The results showed a 32% improvement in route efficiency, a 29% decrease in fuel consumption and emissions, a 33% increase in waste processing throughput, and 18% vehicle maintenance savings versus conventional practices. This demonstrates quantifiable benefits across operational, economic, and sustainability dimensions. The proposed IoT-enabled waste management system represents a significant advancement towards sustainable and ecologically responsible waste practices in smart cities worldwide. This research provides a replicable model for holistic smart city solutions integrating sensing, algorithms, and analytics to transition civic operations towards data-driven, efficient paradigms. It represents a significant advancement in sustainable waste practices for smart cities worldwide. Further work could apply emerging technologies like automation and artificial intelligence to create waste management 3.0.

Levy flight based momentum search and Circle Rock Hyrax swarm optimization algorithms for energy effective clustering and optimal routing in wireless body area networks

In recent years, the connectivity of a new dimension has been brought to Wireless Body Area Networks (WBANs) through the concept of the Wearable Internet of Things (WIoT). WIoT offers valuable functionalities like data collection, analysis, and transmission for real-time health monitoring services. However, WBANs are characterized by a large number of battery-powered sensor nodes, making them resource-constrained. While keeping standard WSN abilities, WBAN's significant obstacles are data communication, social mobility, high interference in dense deployment, latency, and energy consumption of sensor nodes. As a result, the implementation and design of an application require dependable data transfer and an energy-efficient architecture. It proved critical to have as little delay as possible while delivering correct data. To overcome these issues, this study developed a novel energy-efficient clustered and optimized routing scheme in WBAN. Due to its effective scalability, this work employs an Improved Self-Executing Active Cross-Propagated (ISAC) clustering approach to form clusters in Wireless Body Area Networks (WBANs). Subsequently, the selection of the best Cluster Heads (CHs) is facilitated by the Levy Flight-based Momentum Search Algorithm (LFMSA). The Circle Rock Hyrax Swarm Optimization (CRH) algorithm is then utilized for energy-efficient routing. In determining the CH in each cluster, consideration is given to both residual energy and connectivity. The proposed framework minimizes the number of transmissions and overall transmission distance, optimizing inter and intra-cluster transmission distances. Additionally, the framework ensures energy-efficient packet delivery from CHs to the sink node. Performance comparisons, conducted using various statistical measures and simulated on the NS-2 platform, demonstrate that the proposed framework outperforms existing routing protocols and clustering methodologies. In comparison to previous methodologies, the proposed framework consistently achieves superior results.