This paper presents a matrix-based approach for modelling and analysing transportation networks using concepts from graph theory and linear algebra. The incidence matrix and its transformation into the adjacency matrix through the product MMT are employed to represent structural relationships within the network. Matrix operations and their powers are used to study both direct and indirect connectivity, while the reachability matrix provides an effective algebraic criterion for determining accessibility among nodes. The theoretical results establish a connection between matrix formulations and graph connectivity, offering a systematic framework for network analysis. The applicability of the proposed method is demonstrated through several transportation models, including regional and large-scale networks, where key hubs, connectivity patterns, and efficiency are identified. The study shows that matrix-based techniques provide a scalable and practical tool for transportation planning, route optimization, and analysis of complex network systems.

Dynamic optimization of hazardous materials vehicle transportation routes based on real-time risk

With rising customer demands for the timeliness and quality of refrigerated goods, the efficiency and fluidity of cold chain logistics remain inadequate, resulting in a notable imbalance between supply and demand in the cold chain market. To reduce the damage of fresh produce and lower logistics costs, this paper introduces multimodal transportation into the cold chain market and performs an analysis of optimizing multimodal transportation routes for refrigerated goods. This study constructs a mixed-integer programming model for cold chain multimodal transportation, aiming to minimize total costs while considering carbon emissions and uncertain demand. An improved adaptive large neighborhood search (ALNS) algorithm is developed to solve the mathematical model, featuring improved adaptive scoring and operator selection mechanisms. The algorithm’s performance is validated through a real-world multimodal transportation network in China. Furthermore, a sensitivity analysis is performed on rail freight rates, confidence levels, and ambient temperature, from which we derive managerial insights with practical significance.

With the increasing demand for cold chain logistics for food, medicine, and other industries, how to improve the transportation efficiency and inventory management level of cold chain logistics has become a research hotspot. This paper proposes a collaborative optimization model of cold chain logistics transportation and inventory based on an ant colony optimization algorithm (ACO) and Kernel Extreme Learning Machine (KELM). The core of this model is to combine transportation route optimization with the forecasting function of inventory management, optimize the transportation route through ACO, and use KELM to accurately forecast inventory demand to realize the dual optimization of transportation and inventory. The comprehensive optimization of transportation routes, inventory holding cost, out-of-stock rate, and other objectives are considered by establishing the collaborative optimization objective function of the cold chain logistics system. Then, combined with the characteristics of the ant colony algorithm and the KELM model, a collaborative fusion mechanism is proposed, and the interactive feedback process of path planning and inventory forecasting is optimized. The total cost of transportation route optimization is 58.74, indicating that cold chain logistics costs are relatively high under the current scheme. At the same time, in terms of inventory management, the inventory holding cost is 21.56, indicating that the inventory cost is relatively controllable. With the further improvement of path optimization, the transportation cost can be reduced to 87.39, while the inventory out-of-stock rate remains at 12.02. The low out-of-stock rate effectively ensures the stability of logistics services. After careful consideration, route selection and inventory management coordination further optimize energy consumption and guarantee efficient operation of cold chain logistics system. The transportation energy consumption is 45.63, while the system's overall energy efficiency is increased to 92.84, indicating that the energy consumption management of the cold chain logistics system has been effectively improved, and collaborative optimization of route and inventory helps improve overall operational efficiency.

This study presents a multi-objective optimization framework for China’s North-to-South Grain Transportation (NSGT), balancing costs, time, carbon emissions, and grain quality loss to promote sustainable logistics. We propose a hybrid algorithm combining genetic optimization with reinforcement learning to identify efficient routes and evaluate trade-offs. Compared to standard methods, our approach achieves better solution diversity and robustness, as validated by sensitivity analysis, scalability tests, and statistical comparisons. The findings advance carbon accounting in multimodal transport and provide practical guidance for policymakers to enhance eco-friendly grain distribution.

The research aims to evaluate the existing transportation practices of Walkaroo International Pvt. Ltd., identify inefficiencies in route planning, fleet utilization, and cost management, and propose strategies for optimization. The study also explores the challenges faced by the company, including high transportation costs, underutilization of vehicles, inadequate technological integration, and delivery delays. By examining these factors, the research seeks to enhance operational efficiency, reduce overall logistics costs, and improve delivery performance. Through data collection and analysis, the study highlights the importance of adopting modern Transportation management systems (TMS), GPS tracking, and route optimization software to streamline operations. It emphasizes how optimized transportation not only minimizes operational expenses but also strengthens customer satisfaction and supports sustainable business growth.

Multimodal freight transportation is essential for enhancing logistics efficiency and reducing costs, with route optimization as its core component. However, uncertainties are prevalent in multimodal systems, posing challenges for the construction and validation of simulation models. Furthermore, the involvement of multiple stakeholders introduces conflicting optimization objectives. Effectively balancing these objectives to maximize overall benefits has become a critical issue that requires resolution. For the multimodal transport route optimization problem in hybrid uncertainty environments (e.g., there are uncertainties in transportation demand, transportation time, and transfer time), this study constructs a multiobjective optimization model based on fuzzy numbers. The complexity of the model is reduced by introducing the chance-constrained programming theory. To solve the model, a data-driven multi-objective evolutionary algorithm is designed, integrating Monte Carlo simulation with surrogate models to effectively reduce the computational cost of uncertainty estimation. Furthermore, a constraint prioritization strategy is developed to handle multiple conflict objectives, and complex constraints efficiently. Simulation results demonstrate that the proposed algorithm exhibits excellent performance across networks of varying scales, providing robust decision support for multimodal transportation decision-making.

Purpose. The purpose of this paper is to investigate ways to enhance the efficiency of transport and logistics systems in the context of global digital transformation and economic decarbonization. The study focuses on the optimization of transport routes using artificial intelligence technologies as a tool for reducing logistics costs and the carbon footprint of enterprises. Methodology. The research is based on a comprehensive methodological framework that integrates systemic, analytical, logical-structural, and comparative approaches. The study employs mathematical modeling, machine learning algorithms, big data analytics, and multi-criteria optimization techniques. To evaluate the model’s performance, a digital twin of the transport network was developed, allowing the simulation of various transportation scenarios in real time. The optimization model combines three key parameters-logistics costs, CO₂ emissions, and delivery time-using adjustable weighting coefficients according to enterprise-specific priorities. Findings. The results of simulation modeling demonstrate that the implementation of AI-driven routing technologies enables a reduction in fuel consumption by 18.7%, a decrease in average delivery time by 12.3%, a reduction in CO₂ emissions by 20.1%, and an overall decrease in logistics costs by 22.4%. These outcomes are consistent with global trends in the digital transformation of logistics and confirm the effectiveness of intelligent transport systems in achieving sustainable development objectives. The application of machine learning, genetic algorithms, and particle swarm optimization provided superior stability of solutions and adaptability to changing operational conditions. Practical implications. The developed model can be applied by enterprises of different sizes to increase competitiveness, reduce operational costs, and meet climate targets. The findings may also support the design of public policies aimed at promoting sustainable transport, digitalization, and decarbonization across the economy. Value / originality. The study contributes to the advancement of green logistics and the concept of sustainable digital supply chains by integrating intelligent optimization algorithms into transport management. It presents a conceptual and methodological framework for AI-based optimization of transport operations, which bridges economic efficiency and environmental sustainability. Future research should focus on developing industry standards for AI integration in logistics systems, creating hybrid optimization algorithms, and exploring the socio-economic impacts of digital transformation in the transport sector.

Container multimodal transport faces many uncertainties in practice. To improve operational efficiency and reduce carbon emissions in freight transport, this study develops a multi-objective optimization model for container multimodal routes that incorporates demand and time uncertainties as well as carbon emissions. The proximal policy optimization (PPO) algorithm identifies robust transport paths facing uncertainty and assesses the model’s sensitivity to price fluctuations and carbon tax rates. Empirical results for the Chongqing–Singapore container route demonstrate the strong applicability of the PPO algorithm. Compared with traditional routing methods, the algorithm yields a lower late-arrival rate and delivers clear advantages in risk avoidance and cost control, thereby effectively reducing carbon emissions in line with carbon-reduction policies and offering practical guidance for logistics firms. The model operates under the assumptions of indivisible cargo and single-visit constraints at nodes, which impose certain limitations. In addition, the current model requires substantial computational resources, which may limit its applicability for smaller companies. With continued optimization, however, the approach advances the industry toward data-driven, intelligent decision-making.

As the core industry of energy supply, the efficiency of storage and logistics management of thermal power plant has a direct impact on the safety, economy and sustainability of power generation. At present, the traditional thermal power plant warehousing and logistics system is faced with decentralized management, information lag, high dependence on manual and insufficient cost control, which cannot meet the needs of modern management. The in-depth application of Internet of Things (IoT) technology provides technical support for the digital upgrade of warehousing and logistics system, but there are still challenges in data integration depth, prediction model accuracy and adaptability to complex environment. The intelligent warehouse and logistics management system developed in this study for thermal power plants integrates IoT, AI, and automation technologies to create a smart management platform that covers full lifecycle tracking of material information, automated warehouse scheduling, intelligent logistics path optimization, and multidimensional data analysis. By deploying RFID tags, smart sensor terminals, and AGV logistics equipment, combined with recursive neural networks and reinforcement learning algorithms, the system achieves real-time material status monitoring, precise inventory demand forecasting, and dynamic optimization of transportation routes.

Hazardous materials transportation route optimization considering dynamic wind speeds and risk equity

To reduce the carbon emissions from waste collection vehicles, taking the representative Baolang Economic Development Zone in Shiyan City as an example, a two-stage urban domestic waste logistics collection and transportation model was established. In the first stage, the goal was to minimize the total distance, determining the optimal location for waste transfer stations. In the second stage, with the goal of minimizing total costs, and based on the determined optimal location of the transfer stations, the optimal waste collection vehicle routing was established. A genetic algorithm was designed to solve the optimal results for both stages, obtaining the best location for waste transfer stations and the optimal transportation routes for the Baolang District in Shiyan City. The optimization was compared and showed significant results. Finally, in light of the actual situation of domestic waste in the urban area of Shiyan City, suggestions were made to optimize the urban domestic waste collection network.

With the rapid development of the e-commerce industry, the demand for logistics has increased sharply, and the management efficiency issues of sorting centers have become increasingly prominent. This article provides daily and hourly cargo volume predictions, transportation route optimization, and staff scheduling for 57 centers over the next 30 days to meet user demands and transmission speed requirements. This paper proposes a comprehensive framework that combines the LSTM-ARIMA model for cargo volume prediction, calculates the cargo volume change rate based on the actual sorting center data through route comparison for correction, and optimizes staff arrangement using the linear regression model. This framework effectively addresses issues such as insufficient accuracy in cargo volume prediction, cargo volume deviations caused by changes in transportation routes, as well as excessively high total person-days and unbalanced hourly person-efficiency in employee scheduling.

The article considers the features of the cluster model of the organization of emergency medical care and ways to improve the tools of public administration to ensure the efficiency, coordination and availability of medical services. It is determined that the analysis of budget financing of primary health care and emergency care services is important for assessing the ability of the state to ensure the timeliness and quality of medical interventions. Medical care, centralized dispatch services and training departments contribute to the standardization of procedures, prompt redistribution of resources, and increase the efficiency of interregional coordination. It has been established that the application of the cluster approach involves the optimization of patient transportation routes, centralization of funding, standardization of processes and integration of digital solutions, including telemedicine facilities and call monitoring systems. It is noted that the legal regulation of the functioning of emergency medical care teams and departments forms the basis for the continuity of care, coordination between structures and adaptation to social, demographic and territorial characteristics. It is noted that strategic planning of the activities of institutions within hospital clusters ensures the concentration of management decisions on long-term structural changes and increases the resilience of the health care system. It has been revealed that the introduction of a cluster model of emergency medical care organization provides a synergistic effect of interaction between institutions, increases the efficiency, accessibility and quality of medical services, and also contributes to bringing the health care system of Ukraine to international standards of quality of medical care.

The strategic importance of green logistics in supply chain management has been proven, leading to a reduction in the negative impact of companiesʼ activities on the environment and ensuring a balance between economic efficiency, environmental safety, and social impact. A hypothesis has been formulated regarding the presence of synergy between green marketing and green logistics for fostering environmental awareness, implementing green initiatives, and ensuring the sustainable development of trade enterprises. The areas for implementing green logistics strategies have been identified, inclu­ding the optimization of transportation routes, the introduction of energy-efficient technologies, the use of alternative energy sources, waste management, and the reuse of packaging mate­rials. Particular emphasis is placed on fostering environmental awareness among supply chain participants through the synergistic interaction of green communications and green logistics solutions, which contributes to the increasing sustainable consumer preferences, enhancing partner aware­ness, and encouraging respon­sible behaviour. The conditions for the effective implementation of green logistics strategies have been considered, including the development of unified assessment standards, the formation of standardized indicators, the monitoring of indi­rect environmental impacts, and the integra­tion of economic, environmental, social, and techno­logical criteria into the supply chain management system. The proposed approach will enable trading companies to minimise environ­mental risks, improve the efficiency of logis­tics processes, streng­then competitive positions, and create additional value for all participants in the supply chain

Abstract: The aim of this study is to develop a Mobile-Based Smart Transport Route Optimization and Hazard Reporting System that could improve navigation, minimize travel time, and increase road safety in San Juan City. The complex road system of the city and the ever-changing traffic rules, such as one-way and two-way rules based on time, pose great challenges to drivers, which often result in congestion and unintended traffic violations. This study solves these problems by giving a localized and adaptive navigation solution. The proposed system uses a combination of Dijkstra's Algorithm, Google Maps Application Programming Interface and real-time traffic information to produce the most efficient and accurate routes. It also has a hazard reporting feature that allows users to share updates on accidents, road closures, police presence, and other disruptions. Other features include English and Tagalog voice navigation, a built-in speedometer with overspeed warnings, two-factor authentication for secure access, a favorites feature for easy recall of commonly traveled routes. The system was developed following an iterative prototyping approach with user feedback and validated and filtered with data collected from local drivers and the San Juan City Traffic and Parking Management Office to maintain accuracy and usability. Its area of focus is private vehicles, motorcycles, and taxis that are operating within San Juan City. Findings indicate that the system offers better route guidance, minimizes traveling time, and improves the safety of commuters by combining algorithmic route optimization and community-based hazard reporting. This study illustrates the role of localized systems in ensuring safer and efficient mobility in urban settings. This study shows the need to design navigation tools based on the actual community needs and conditions. Through the case of San Juan City, the system demonstrates how technology can directly address daily traffic issues, can offer a safer traveling experience, and can also serve as an example that can be copied in other cities with complicated traffic schemes.

The article analyzes the institutional foundations of the organization of air transportation in the context of the current state and prospects for further development. High-speed delivery of goods by aircraft provides advantages over other popular types of transportation by land and water vehicles. The development of the air transport industry becomes the main factor in strengthening the competitiveness of the transport complex of the national economy. The latest modern information and communication tools of technological processes of processing, analysis and provision of information provide wider opportunities for improving the quality of delivery of goods from the seller to the buyer. At the same time, the issues of institutional support for international transportation, which involve the location of departure and destination points on the territory of two different states or one country with an intermediate stop on the territory of another state, while national transportation is carried out within the borders of one country by licensed business entities on a paid basis, require in-depth study. own or leased aircraft. After all, achieving efficiency from the organizational activity of air transportation is possible thanks to the functioning of the institutional base, the development of which is becoming an extremely urgent problem today. The purpose of our work is to study the institutional foundations of the organization of international and national air transport of passengers and cargo in the context of sustainable development of the national economy of an individual state and globalization processes at the world level. The primary task of our research is to identify the challenges and prospects of institutional support for air carriers to improve the quality of passenger service and international cargo delivery. The disclosure of the purpose and the solution of the set tasks was carried out on the basis of comparative legal and operational methodological approaches with a set of methods of abstraction, concretization, formalization and analogy, induction and deduction. The statutes of international aviation organizations, normative legal acts and rules, scientific developments of domestic and foreign scientists served as reference material. Based on the results of the study, three directions for the formation of the institutional foundations of the organization of air transportation of goods at the international level were identified: development and guarantee of the fulfillment of contractual conditions by air carriers when transporting goods by several modes of transport; compliance with international quality standards for the provision of transport services by air carriers in the context of fast and safe delivery of goods from the seller to the buyer; increasing the efficiency of the transport activity of air carriers due to the optimization of cargo transportation routes taking into account external and internal factors influencing the existing institutional environment. The prospects for the development of institutional support for air transportation through the implementation of the best global experience in the organization of air transportation are characterized; active participation in international aviation organizations regarding the development of institutional norms and rules for air transportation of cargo and passengers; strengthening of international economic cooperation, balancing the interests of business entities of different countries, for which the rules of concluding international contracts and conducting commercial transactions are important elements. The key provisions of the institutional foundations of the organization of air transportation are international aviation organizations and regulatory provisions regarding improving the quality of service and flight safety, institutional support for the development of multimodal transportation and the creation of large transnational transport hubs with the latest information and technological equipment.

During the pandemic, the amount of infectious medical waste has increased dramatically. Currently, the medical waste recycling process generally suffers from defects such as long distances, high costs, and a lack of emergency response mechanisms. This paper addresses the problem of medical waste collection and route optimization for regions with multiple vehicle types and stages. It comprehensively considers factors such as transportation costs, distance, vehicle allocation, and contamination risks during the collection and distribution of medical waste. The goal is to minimize transportation costs and risks, with constraints including uniqueness, connectivity between nodes, and vehicle load capacity. A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. Given the NP-hard nature of the problem, a location allocation method based on minimum envelope clustering analysis is employed, and an improved NSGA-II algorithm incorporating a fast non-dominated sorting mechanism is designed to obtain Pareto optimal solutions. Comparing with the results of traditional genetic algorithms through simulation, the results show that using the improved NSGA-II to solve practical problems: 1. When the production of medical waste is flat (1 disposal center, 4 backup transfer points, 58 producing points), the total cost is reduced by 13.94%, the total mileage is reduced by 7.17%, the full load rate is increased by 6.14%, and the convergence time is 26 seconds. 2. When the production of medical waste increased significantly (1 disposal center, multiple backup transfer points, 58 producing points), the total cost, total mileage, and transportation risk were reduced by 9.50%, 10.35%, and 2.03%, respectively, and the full load rate increased by 5.98%. The final results also indicate that compared to the results obtained by traditional genetic algorithms, the improved NSGA-II algorithm performs better in solving the optimization problem of infectious medical waste transportation routes.

During the pandemic, the amount of infectious medical waste has increased dramatically. Currently, the medical waste recycling process generally suffers from defects such as long distances, high costs, and a lack of emergency response mechanisms. This paper addresses the problem of medical waste collection and route optimization for regions with multiple vehicle types and stages. It comprehensively considers factors such as transportation costs, distance, vehicle allocation, and contamination risks during the collection and distribution of medical waste. The goal is to minimize transportation costs and risks, with constraints including uniqueness, connectivity between nodes, and vehicle load capacity. A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. Given the NP-hard nature of the problem, a location allocation method based on minimum envelope clustering analysis is employed, and an improved NSGA-II algorithm incorporating a fast non-dominated sorting mechanism is designed to obtain Pareto optimal solutions. Comparing with the results of traditional genetic algorithms through simulation, the results show that using the improved NSGA-II to solve practical problems: 1. When the production of medical waste is flat (1 disposal center, 4 backup transfer points, 58 producing points), the total cost is reduced by 13.94%, the total mileage is reduced by 7.17%, the full load rate is increased by 6.14%, and the convergence time is 26 seconds. 2. When the production of medical waste increased significantly (1 disposal center, multiple backup transfer points, 58 producing points), the total cost, total mileage, and transportation risk were reduced by 9.50%, 10.35%, and 2.03%, respectively, and the full load rate increased by 5.98%. The final results also indicate that compared to the results obtained by traditional genetic algorithms, the improved NSGA-II algorithm performs better in solving the optimization problem of infectious medical waste transportation routes.

Relay transport networks design plays an important role in improving logistics efficiency, involving the determination of the number of relay points, costs of constructing these points, and constraints on the distance between relay points. The configuration of these unique characteristics must consider multiple aspects of sustainability, including economic, environmental, and social factors. In this study, we aim to determine the optimal relay network configurations and transportation routes through a multiobjective optimization model, considering three key objectives: maximizing freight demand for relay transport, minimizing total costs, and maximizing CO 2 emission reduction. The proposed model was solved using the Gurobi optimization solver. A case study conducted in Japan revealed that the optimal relay network configuration consists of 23 relay points, including 21 small, 1 medium, and 1 large point. This optimized configuration yielded three significant contributions for environmental, economic, and social aspects, namely 34.72% reduction in CO 2 emissions, 3.65% reduction in transportation costs, 27.47% reduction in overtime for short-haul trips, and 14.92% reduction in overnight stays for long-haul trips. Our findings recommend the following policies: (i) trucking companies should set the distance constraints between two adjacent relay points as 150 km for short-haul trips and 450 km for long-haul trips to balance environmental, social, and economic priorities; (ii) the Japanese government’s proposed budget (2.5 billion yen) for relay point construction is relatively sufficient, exceeding our estimated construction cost (2.095 billion yen); and (iii) relay transport implemented as a standalone measure has the potential to help achieve the Japanese trucking industry’s CO 2 emission reduction target.