Optimal Route Planning Research Articles

A 3D multiobjective multi-item eco-routing problem for refrigerated fresh products delivery using NSGA-II with hybrid chromosome

In a developing countries, say India, about 40 percent fresh foods are wasted during transportation and $14 billion of post harvest products are lost due to inefficiency of cold supply chain such as using non-refrigerated vehicle during last mile transport. Hence, temperature control through refrigerated transportation is very essential. But, the by-product of this process i.e., carbon emission (CE) due to transportation and refrigeration is detrimental for the world. Only logistic transportation contributes about 25 percent of global CE. Due to the world wide infrastructural development, there are several available routes for the road transportation among different cities. For the maintenance of these routes, there are some toll plazas for collection of money. Some routes also cross the rail lines. For maximum profit, a supplier distributes the products at the earliest as the retailers fix the product’s price with respect to its freshness. Again, this process generates more CE. With these realities, a 3D multiobjective multi-item eco-routing problems for refrigerated fresh products (3DMOMIERPsfRFPs) are developed, where a refrigerated vehicle starts from a depot (refrigerated) and comes back after distributing the fresh products to retailers as per their demands. The optimal routing plan, vehicle’s velocity and preservation rate are determined so that total profit and CE are respectively maximized and minimized simultaneously. For solution, an NSGA-II with different modified operators (NSGA-IIwDOs) is developed with hybrid chromosomes having discrete and continuous variables together, probabilistic selection, problem-specific crossover and generation-dependent mutation. Some performance metrics are presented through NSGA-IIwDOs on the standard TSPLIB problems. Results of 3DMOMIERPsfRFPs without and with specific delivery time slots, road-dependent vehicle velocity and time constraints are evaluated. Pareto front for multiobjective are depicted. As a particular case, results of a previous investigation are obtained. Some of the managerial decisions are observed.

UAV 2D Path Planning Considering Ground Suitability for Flying Safety in Digital Twin

Abstract. The utilization of various UAVs such as facility inspection and delivery is increasing. Ensuring safety in carrying out various UAV missions is an important issue related to human and property damage. Safe operation of UAVs flying autonomously along pre-located routes requires safe route planning to prevent collisions between aircraft in consideration of ground and air obstacles. Pass flight is also essential to minimize damage in the event of a crash. This paper deals with the optimal UAV 2D routing plan considering ground compatibility for flight safety. High ground compatibility here means that the damage caused by the crash is less than when it is not. In this paper, we first defined dynamic and static obstacles. Next, we derived ground compatibility considering dynamic and static obstacles. We used estimation algorithms and land use data to calculate ground compatibility values for flight safety, and through experiments, we calculated an optimal 2DUAV route plan for flight safety.

Stage-Specific Multi-Objective Five-Element Cycle Optimization Algorithm in Green Vehicle-Routing Problem with Symmetric Distance Matrix: Balancing Carbon Emissions and Customer Satisfaction

This study presents a bi-objective optimization model for the Green Vehicle-Routing Problem in cold chain logistics, with a focus on symmetric distance matrices, aiming to minimize total costs, including carbon emissions, while maximizing customer satisfaction. To address this complex challenge, we developed a Stage-Specific Multi-Objective Five-Element Cycle Optimization algorithm (MOFECO-SS), which dynamically adjusts optimization strategies across different stages of the process, thereby enhancing overall efficiency. Extensive comparative analyses with existing algorithms demonstrate that MOFECO-SS consistently outperforms in solving the multi-objective optimization model, particularly in reducing total costs and carbon emissions while maintaining high levels of customer satisfaction. The symmetric nature of the distance matrix further aids in achieving balanced and optimized route planning. The results highlight that MOFECO-SS offers decision-makers flexible route planning options that balance cost efficiency with environmental sustainability, ultimately improving the effectiveness of cold chain logistics operations.

Modeling the trajectories of a group of unmanned aerial vehicles based on the ADRRT-Connect algorithm in the problem of placing seismic sensors

Optimal route planning is a key aspect of successfully delivering geosensors using unmanned aerial vehicles (UAVs) to geographically distributed targets. The optimality of the route depends on the time required to plan the route and execute the flight to a target or group of targets, considering traffic safety and dynamic constraints. The trajectory calculation algorithm must always find a route, if one exists, and if it is impossible to find one, inform the operator about this circumstance. This article proposes a method based on a set of algorithms that ensure efficient distribution of targets among group members based on information about the targets and characteristics of the unmanned aerial vehicles used. The method involves constructing optimal routes considering minimizing path length, avoiding sharp turns and loops, as well as avoiding obstacles and avoiding collisions with other unmanned aerial vehicles within the group. To test the proposed method, software was developed using the ROS/Gazebo en-vironment. The proposed method is a comprehensive solution to the problem of optimal route planning for UAVs with an emphasis on efficiency, safety, and efficiency in the context of geosensor delivery using unmanned aerial vehicles.

A Modeling Approach for Cargo Transportation Considering Energy Saving

This study presents a cargo transportation system management model that enables users to carry out the cargo process efficiently and economically. Cargo transportation is an important part of the transportation systems network. The advantages of cargo transportation are to ensure the safe delivery of products, save time and reduce costs. This study addresses the solution to the problems of high carbon dioxide emissions, and late and expensive deliveries caused by large distribution networks, by taking into account the density and logistics strategies of the cargo companies. The proposed cargo management system focuses on delivering cargo to destinations along the route that users are currently traveling on. This system is built around three main components: optimized route planning by an A* algorithm, a system model controllable through mobile and web interfaces, and nodes designated as drop-off and pick-up points for cargo. The A* algorithm runs with a reward matrix that also takes direction into account to calculate the optimal route. Users carry out the cargo management and tracking processes on mobile and web interfaces. Automatic nodes, which are the model prototype of the study, represent the cargo vending machines where users deliver/receive their cargo. Through this work, users can view and select cargo to carry while traveling between destinations and earn profits by acting as cargo carriers/drivers.

Development and Application of an Advanced Automatic Identification System (AIS)-Based Ship Trajectory Extraction Framework for Maritime Traffic Analysis

This study addresses the challenges of maritime traffic management in the western waters of Taiwan, a region characterized by substantial commercial shipping activity and ongoing environmental development. Using 2023 Automatic Identification System (AIS) data, this study develops a robust feature extraction framework involving data cleaning, anomaly trajectory point detection, trajectory compression, and advanced processing techniques. Dynamic Time Warping (DTW) and the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithms are applied to cluster the trajectory data, revealing 16 distinct maritime traffic patterns, key navigation routes, and intersections. The findings provide fresh perspectives on analyzing maritime traffic, identifying high-risk areas, and informing safety and spatial planning. In practical applications, the results help navigators optimize route planning, improve resource allocation for maritime authorities, and inform the development of infrastructure and navigational aids. Furthermore, these outcomes are essential for detecting abnormal ship behavior, and they highlight the potential of route extraction in maritime surveillance.

Minimal‐cost route planning via Fibonacci‐heap‐typed data structure

AbstractEfficient route planning in transportation networks, particularly under stochastic conditions like severe weather (i.e. snow or hail), poses a significant computational challenge. This article addresses this challenge by modeling the route planning problem as a Markov decision process (MDP) problem, establishing reachability criteria, and identifying the minimum‐weight arborescence in the directed graph. To achieve this, the reachability determination algorithm is designed to assess the courier company's reachability to all junctions based on the queue‐typed data structure and breadth‐first search idea. Subsequently, the minimal‐cost route planning algorithm is developed to find a feasible transport route with the minimal cost of clearing obstacles by resorting to the Edmonds' algorithm and some feasible data structures. In particular, the article introduces a Fibonacci‐heap‐typed data structure to the minimal‐cost route planning algorithm, resulting in a remarkable reduction of the time complexity from to , where and represent the cardinalities of the arc set and nodeset, respectively. Ultimately, the proposed method is applied to optimize route planning in a transportation network, providing a cost‐efficient solution for logistics and transportation planning.

The whole process route planning algorithm based on AIS spatio-temporal big data analysis

ABSTRACT This paper proposes a route planning algorithm that can comprehensively integrate ship safety, economic benefits and algorithm universality. Firstly, the Polynomial Approximation with the Exponential Kernel method was used to smooth the repaired trajectory and the similarity of the smoothed trajectory was evaluated using Hausdorff distance. The results showed that the similarity of ship trajectories was effectively improved by 36.53%. Using the Douglas Pucker algorithm to compress the smoothed trajectory, the compression rate exceeded 92%. Then, the HDBSCAN self-adjusting clustering analysis method was used to cluster the ship trajectory and the silhouette coefficient index was used to quantitatively evaluate the clustering effect, the calculation result was 0.9032. Finally, using the average centre method to extract the centres of each cluster, 299 key turning points were obtained and connected to construct an undirected route network. Based on this, the Dijkstra algorithm was used to achieve optimal route planning.

Feasibility of the Northern Sea Route: Impact of Sea Ice Thickness Uncertainty on Navigation

With the accelerated melting of the Arctic sea ice, the opening of the Northern Sea Route (NSR) of the Arctic is becoming increasingly accessible. The purpose of this paper was to examine the impact of uncertainty in sea ice thickness (SIT) data on the opening of the NSR and to quantify the extent of this impact, which is essential to the regularized operation of polar shipping. A quantitative assessment framework was proposed to examine the influence of SIT data uncertainty on navigational uncertainty through three aspects: the navigational spatiotemporal windows, the distribution of safe sailing speeds, and the uncertainty in optimal route planning by employing four distinct SIT datasets. Furthermore, the sensitivity of navigational spatiotemporal windows, the distribution of safe sailing speeds, and route planning to variations in SIT were also evaluated. Results show that experiment results based on CS2SMOS exhibit a more aggressive profile, while results based on CPOM are more conservative. The difference in SIT data has a significant impact on the proportion of operations subject to special consideration areas, such as safety speed, sailing time, and distance in NEP. A 0.1 m discrepancy in sea ice thickness data results in an approximate 0.067 change in the proportion of operations within special consideration areas. This discrepancy also leads to an approximate speed change of 0.89 knots, a navigation duration change of approximately 4 days, and a distance change of 152 km within specified limits.

A Multi-Criteria Decision Approach for Optimized Route Planning in Retail Distribution

A Multi-Criteria Decision Approach for Optimized Route Planning in Retail Distribution

Biometric Breakthroughs for Sustainable Travel: Transforming Public Transportation through Secure Identification

This study investigates the use of biometric technology in public transit to improve trip safety and effectiveness. The incorporation of biometric technology into transit networks improves efficiency and security but also poses substantial challenges of privacy, standardisation, and public acceptability. Conducted via a poll of 25 specialists in Lithuania, the objective was to assess the level of acceptability and practicality of using biometric identification for both drivers and passengers. The results suggest a divided view about the specific demographic that biometric applications should target. However, there is agreement on the considerable potential of these technologies to enhance transportation safety. Face recognition has been the favoured approach due to its non-intrusive nature and simplicity of integration. The statistical research demonstrated significant positive correlations between different biometric approaches, indicating that a multimodal strategy is effective for providing full security coverage. The research highlights the significance of resolving privacy issues, emphasising that public acceptability depends on the open management and strong safeguarding of biometric data. The findings support the deliberate use of biometric technologies in sustainable public transportation, emphasising their ability to improve safety, optimise operations, and even revolutionise the passenger experience. This emphasises the equitable examination of technology, security, and privacy in the progress of sustainable public transportation systems. Biometric technology in public transport, especially for monitoring driver health and ensuring passenger safety, is supported by experts as a means to enhance service quality, reduce accidents, and optimize route planning.

FedGen: Federated learning-based green edge computing for optimal route selection using genetic algorithm in Internet of Vehicular Things

Time-efficient route planning is a significant research area of Internet of Vehicular Things. Optimal route selection is important to reach the destination in minimal time. Further, energy efficiency is vital for route planning in a sustainable environment. To address these issues, this paper proposes a federated learning and genetic algorithm-based green edge computing framework for optimal route planning in Internet of Vehicular Things. The vehicles are connected to the road side unit. The road side unit processes the image and video of the road, and predicts the number of vehicles on the road. For video processing Region-based Convolutional Neural Network is used. The road side units send the result and the local model parameters to the regional server. The regional server determines the optimal route using modified genetic algorithm, and sends it to the vehicles and the cloud. Also, the regional server updates its model and sends the updated model parameters to the road side units. The road side units update their local models accordingly. The regional server also sends the model parameters to the cloud, and the cloud updates the global model. The cloud sends the updated model parameters to the regional servers. The regional servers update their models accordingly. The results present that above 90% accuracy is achieved by the proposed model. The results also present that using modified GA the proposed approach reduces time and power consumption to find the optimal route by ∼62% and ∼66% than the cloud-only model.

Developed Status and Forecast of Intelligent Ship Application

The concept of smart ships is not limited to ship autonomous driving, it covers a broader scope, including the full life cycle of ship design, construction, operation and maintenance. By integrating advanced sensors, navigation systems and data analysis platforms, smart ships are able to monitor ship status and the Marine environment in real time, optimize route planning and improve fuel efficiency, thereby reducing energy consumption and greenhouse gas emissions. In addition, smart ships can revolutionize the shipping industry by improving maritime safety and reducing human error through remote control and autonomous decision-making. In recent years, smart ship technology has gradually moved from the theoretical research stage to practical application, and has attracted wide attention from the global shipping industry. A number of international cooperation and research projects have been launched to explore the practical application potential and promotion paths of smart ships. However, the development of smart ships also faces many challenges, including technical difficulties, legal and regulatory restrictions, and public acceptance issues. This paper will deeply discuss the current development status of smart ships, analyze the main challenges it faces, and forecast the future development trend, in order to provide reference and inspiration for the innovative development of the shipping industry.

Future Trends: Predicting the Evolution of Rail and Road Transportation in Logistics

The logistics industry represents the vital circulatory system of the global economy, responsible for the efficient movement of goods across sprawling supply chain networks. Historically, rail and road transportation modes have been the workhorses of logistics operations, facilitated by their extensive infrastructure and versatility in cargo handling. However, the convergence of rapid technological advancements, evolving consumer expectations, and mounting environmental pressures is catalyzing a transformative shift in the future evolution of these pivotal transportation sectors. This comprehensive study delves into the emerging trends that are poised to reshape the rail and road transportation landscape within the logistics realm. By synthesizing insights from industry leaders, academic research, and cutting-edge technological developments, the study aims to provide a holistic perspective on the forthcoming changes and their far-reaching implications. The study’s main objective is to integrate semi-autonomous and autonomous cars into fleets of transportation vehicles. The advent of self-driving trucks and trains promises to revolutionize logistics operations by enhancing efficiency, reducing human error, and optimizing route planning. Concurrently, this transition raises critical concerns related to workforce displacement, regulatory frameworks, and cybersecurity vulnerabilities that necessitate proactive mitigation strategies. Moreover, the logistics industry faces mounting pressure to embrace sustainable practices and curtail its environmental footprint. Consequently, the rail sector is anticipated to experience a renaissance fueled by its inherent energy efficiency and lower carbon emissions compared to road transportation. Innovations in electric and hydrogen-powered locomotives, coupled with advancements in rail infrastructure, could position rail as a greener and more cost-effective solution for long-haul freight movement. Concurrently, the road transportation sector is poised for a transformation driven by the proliferation of electric and alternative fuel vehicles. This adoption is projected to accelerate as battery technology improves, charging infrastructure expands, and governments implement stricter emissions regulations. Furthermore, the integration of advanced telematics systems and realtime data analytics could optimize route planning, load management, and vehicle maintenance, further enhancing the operational efficiency of road-based logistics.

Enhancing Safety in Cargo Handling Units Using Thermal and Motion IoT Sensors

To improve supply chain efficiency by establishing real-time communication via Internet of Things (IoT) devices between cargo units and Logistics Control Systems (LCS). The method makes it possible to continuously monitor characteristics like location, temperature, humidity, and handling affects by carefully placing IoT sensors within cargo containers. These Internet of Things (IoT) devices work together to create a network that sends data in real time to a centralized LCS platform. This data gives logistics operators fast insights for proactive disruption response, risk mitigation, and optimized route planning. The research uses encryption techniques, redundancy plans, and flexible software interfaces to address issues with data security, device dependability, and integration into current infrastructure. Furthermore, by integrating machine learning and predictive analytics, the LCS can anticipate potential problems, which helps promotes increased customer happiness, decreased losses, and greater operational performance in contemporary logistics and freight transportation.

AI Enabled MED Drone for Healthcare Application

The project proposes the development of an AI-enabled MedDrone for medication delivery, introducing an innovative and efficient solution to enhance healthcare logistics. The MedDrone, equipped with artificial intelligence capabilities, aims to revolutionize the process of medication delivery by leveraging autonomous aerial technology. The system integrates advanced algorithms to optimize route planning, ensuring timely and accurate delivery of medications to designated locations. The AI component enables the drone to adapt to real-time variables such as weather conditions and traffic, enhancing the reliability and responsiveness of the delivery process. This project addresses the challenges of traditional medication distribution, offering a futuristic and intelligent approach to healthcare logistics through the deployment of AI-enabled autonomous drones for efficient and timely medication delivery. The proposed system integrates AI algorithms for real-time route optimization, obstacle avoidance, and decision-making, enabling the drone to autonomously navigate complex environments and deliver essential medical supplies such as first aid kits, defibrillators, and medications to the point of need. Through the utilization of machine learning techniques, the drone continuously learns from its interactions with the environment, enhancing its adaptability and performance over time. Additionally, the system incorporates advanced communication technologies to enable seamless coordination with emergency responders and healthcare professionals, ensuring timely delivery and proper utilization of resources. The efficacy of the proposed AI-based medical drone system is demonstrated through simulations and real-world trials, highlighting its potential to revolutionize emergency response efforts and improve healthcare accessibility, particularly in challenging or resource-constrained environments.

Comparative analysis of Bellman-Ford and Dijkstra’s algorithms for optimal evacuation route planning in multi-floor buildings

Comparative analysis of Bellman-Ford and Dijkstra’s algorithms for optimal evacuation route planning in multi-floor buildings

Enhancing Business Competitiveness: Strategic Adaptation and Business Model Canvas Application in PT Maritim Maju

In this detailed analysis, we explore the strategic repositioning of PT Maritim Maju within the context of the global maritime industry’s ongoing transformation, influenced by technological advancements and economic shifts. This study meticulously applies the Business Model Canvas framework, incorporating Resource-Based View, VRIO, and SWOT analyses, to diagnose and propose necessary strategic enhancements for PT Maritim Maju. It underscores the imperative for the company to diversify its services beyond traditional maritime activities, suggesting an expansion into maritime consulting and logistics technology. Additionally, the research advocates for the integration of advanced technologies such as AI in fleet management, aiming to optimize route planning and implement predictive maintenance, thereby achieving cost efficiencies, enhanced service reliability, and superior customer satisfaction. Strategic partnerships are highlighted as crucial for leveraging technological innovation and expanding service capabilities. The study also recommends a reevaluation of marketing strategies, emphasizing the adoption of digital marketing techniques to better position PT Maritim Maju on the global stage, highlighting its commitment to safety, reliability, and customer service excellence. Furthermore, it stresses the importance of continuous staff development in leadership and maritime technology to foster a culture of innovation and strategic agility. Compliance with international regulations and a proactive risk management approach are advised to mitigate operational risks. This comprehensive strategic plan aims to guide PT Maritim Maju through the intricacies of the modern maritime landscape, capitalizing on new opportunities for growth and establishing a foundation for enduring competitiveness and resilience.

Average Displacement of Multi-Beam Measurement Vessel based on 2D Particle Swarm Algorithm

To address the optimization of a survey vessel's navigation route, this study uses historical sea depth data from a maritime area several years ago as an example. The research employs the two-dimensional Particle Swarm Optimization (2DPSO) algorithm to calculate the direction and distance for vessel navigation while adhering to constraints, aiming to obtain the optimal route for the survey vessel. Traditional Particle Swarm Optimization exhibits rapid early-stage convergence, but slows in the later stages and is susceptible to local optima. Hence, we opt for the 2DPSO algorithm, known for its simpler structure and incorporation of penalty functions to constrain particle values. This enhances algorithm performance and minimizes the risk of falling into local optima, offering a better solution to optimal route planning.

An integrated vehicle routing model to optimize agricultural products distribution in retail chains

The Vehicle Routing Problem (VRP) represents a thoroughly investigated domain within operations research, yielding substantial cost savings in global transportation. The fundamental objective of the VRP is to determine the optimal route plan that minimizes the overall distance traveled. This study employs VRP to address the challenge of distributing fresh agricultural products within retail chains. It introduces an integrated bi-objective VRP model that concurrently optimizes resource allocation, order scheduling, and route planning. The proposed model incorporates two objective functions with the goals of minimizing total distribution costs and ensuring timely product deliveries to retail outlets. Real-world characteristics are integrated to enhance practical applicability. All solution algorithms and the developed VRP model undergo testing using data from one of Sri Lanka's largest retail chains. Numerical experiments showcase the efficiency of the proposed algorithm in solving real-world VRP problems. Moreover, the proposed VRP model achieves a 19% reduction in daily distribution costs, including a 24% saving in fuel costs. This not only provides financial benefits but also contributes to the reduction of the carbon footprint of retail chains. The model ensures on-time deliveries to 95% of retail outlets, which is crucial for maintaining the quality of fresh food. The findings of this study underscore the significant cost savings, enhanced sustainability, and improved quality associated with the efficient distribution of fresh agricultural products within retail chains.