Logistics Routing Research Articles

Implementation of AI Transportation Routing in Reverse Logistics to Reduce CO2 Footprint

Purpose: This study explores the implementation of artificial intelligence (AI) in transportation routing within reverse logistics to reduce CO2 emissions. By optimizing routing processes, we aim to enhance the efficiency of logistics operations while minimizing environmental impact. Methodology: we analyze the implications of improved transportation strategies. Furthermore, we discuss the intersection of technological innovation and environmental economics in shaping sustainable practices. Findings: The findings underscore the importance of AI-driven decision-making frameworks (D81) and their role in advancing IT management in logistics. The study on AI-driven transportation routing in reverse logistics highlights significant implications for both businesses and the environment. By optimizing routes, AI reduces fuel consumption and CO₂ emissions, helping companies meet sustainability goals. This leads to cost savings, as AI improves route efficiency and fleet utilization, while also enhancing operational efficiency. AI’s ability to predict and adjust to real-time conditions ensures scalability and adaptability, making it easier to handle increasing returns volumes in e-commerce. Moreover, AI enables better inventory management and resource planning, reducing the need for urgent shipments. It also paves the way for innovations like autonomous vehicles, further transforming reverse logistics. Unique Contribution to Theory, Practice and Policy: The study shows how AI can help businesses comply with environmental regulations, providing a competitive edge. Ultimately, AI integration in reverse logistics contributes to both operational improvements and environmental sustainability, reinforcing corporate social responsibility.

Foreign Policy of the Union State: Latin-Caribbean Direction of the Russia-Belarus Integration

The research is aimed at studying the Union State foreign policy in the Latin-Caribbean direction in new geopolitical conditions and its role in the emerging architecture of world economy and geopolitics. The mechanism for implementing the Union State foreign policy has been determined. It is shown that the external contour of the Russia-Belarus integration is not the sum of foreign policies, but is being implemented within the framework of their legal framework: pursuing a coordinated foreign policy, the parties maintain a flexible approach and are not limited by the framework of the foreign policy institutions of the Union. The author presents basic areas of project cooperation between Russia, Belarus and the states of the Latin-Caribbean region establishing priorities of cooperation.Aim. To appraise the significance and potential of Latin-Caribbean direction in the Union State foreign policy in the current conditions of economic turbulence and geopolitical escalation.Tasks. To identify the main areas of cooperation, to analyze current trends and influencing factors, and to determine the prospects for interaction.Methods. A combination of general scientific theoretical and special methods is used. The method of comparative analysis and the logical method are of prime importance. When working with regulatory and legislative sources, the normative method is used.Results. The potential of cooperation between the Union State and the countries of Latin-Caribbean America has not been fully realized, however, an accelerated diversification of partnership between the countries is being practiced as well as an expansion of areas of interaction based on economic feasibility and political motivation.Conclusions. Under current conditions of global political and economic turbulence, the Latin-Caribbean direction of the Union’s foreign policy is an important part in building effective international cooperation and developing new trans-regional logistics routes.

Концептуальні засади визначення ефективності виконання завдань і заходів державних цільових програм розвитку для підвищення ефективності управління автомобільними дорогами

Introduction. Effective functioning of road transport is only possible when the quantitative growth, quality level, and technical condition of roads meet the demands of road transport. This combination of road network characteristics can only be achieved with adequate funding to implement all planned works. Investments in road infrastructure can become a catalyst for economic recovery and growth, as they will improve logistics capabilities, reduce freight and passenger transportation costs, and enhance regional competitiveness. Problem Statement. In the first decades of independence, the road industry faced chronic underfunding, which negatively affected its development. The situation worsened with the full-scale military aggression of the Russian Federation in 2024. According to the World Bank's “Rapid Damage and Needs Assessment (RDNA3)” report, post-war recovery needs for public roads and bridges alone are estimated at around USD 28.8 billion. For the rapid and quality restoration and development of Ukraine, this issue is critically important, as the state of the roads affects transportation costs, prices, employment levels, and the overall pace of economic development. Furthermore, the full-scale invasion by the Russian Federation raises questions about ensuring reliable logistics routes, prompt transportation of humanitarian aid, and the country's defense capabilities.

Optimization of household medical waste recycling logistics routes: Considering contamination risks.

The escalating generation of household medical waste, a byproduct of industrialization and global population growth, has rendered its transportation and logistics management a critical societal concern. This study delves into the optimization of routes for vehicles within the household medical waste logistics network, a response to the imperative of managing this waste effectively. The potential for environmental and public health hazards due to improper waste disposal is acknowledged, prompting the incorporation of contamination risk, influenced by transport duration, waste volume, and wind velocity, into the analysis. To enhance the realism of the simulation, traffic congestion is integrated into the vehicle speed function, reflecting the urban roads' variability. Subsequently, a Bi-objective mixed-integer programming model is formulated to concurrently minimize total operational costs and environmental pollution risks. The complexity inherent in the optimization problem has motivated the development of the Adaptive Hybrid Artificial Fish Swarming Algorithm with Non-Dominated Sorting (AH-NSAFSA). This algorithm employs a sophisticated approach, amalgamating congestion distance and individual ranking to discern optimal solutions from the population. It incorporates a decay function to facilitate an adaptive iterative process, enhancing the algorithm's convergence properties. Furthermore, it leverages the concept of crossover-induced elimination to preserve the genetic diversity and overall robustness of the solution set. The empirical evaluation of AH-NSAFSA is conducted using a test set derived from the Solomon dataset, demonstrating the algorithm's capability to generate feasible non-dominated solutions for household medical waste recycling path planning. Comparative analysis with the Non-dominated Sorted Artificial Fish Swarm Algorithm (NSAFSA) and Non-dominated Sorted Genetic Algorithm II (NSGA-II) across metrics such as MID, SM, NOS, and CT reveals that AH-NSAFSA excels in MID, SM, and NOS, and surpasses NSAFSA in CT, albeit slightly underperforming relative to NSGA-II. The study's holistic approach to waste recycling route planning, which integrates cost-effectiveness with pollution risk and traffic congestion considerations, offers substantial support for enterprises in formulating sustainable green development strategies. AH-NSAFSA offers an eco-efficient, holistic approach to medical waste recycling, advancing sustainable management practices.

ВПЛИВ ВІЙНИ НА МІЖНАРОДНІ ТРАНСПОРТНІ ПЕРЕВЕЗЕННЯ УКРАЇНИ

The article found that the full-scale war in Ukraine had a strong impact on international transportation. The structure of the volume of cargo transportation and the number of transported passengers were analyzed. It was determined that there is uncertainty in the market of international freight transportation of Ukraine during the studied period. On the one hand, the war had a negative impact on the country's economy and led to a decrease in the volume of cargo transportation. On the other hand, Ukraine is an important transit country through which key logistics routes connecting Europe and Asia pass. It was established that the blocking of air transport and sea trade, with the exception of the "Grain Initiative", led to a radical change in the structure of domestic import/export and the leadership of land corridors. Global transformations in world trade caused by the war in Ukraine also affected the configuration of existing supply chains, Ukrainian exports were completely reoriented towards the EU. It is indicated that Ukraine's signing of an agreement on visa-free transport cooperation with the European Union has opened up new prospects for bilateral international road transport, freeing Ukrainian carriers from the need to obtain permits for flights to EU countries. However, there are also negative trends that affect the growth of transport services and, as a result, decrease the competitiveness of transport companies. A list of legislative and regulatory innovations and organizational measures aimed at improving international transportation is given. It is argued that a successful solution for the recovery of the country will be the integration of the Ukrainian economy into the EU economic system with the help of various European logistics and infrastructure projects. It was determined that the key factors in the normalization of transportation were: the conclusion of the "Grain Initiative" with the UN and Turkey, which allowed to unblock the ports of Great Odesa; signing the Agreement on the Liberalization of Road Freight Transportation with the EU; development of border infrastructure, in particular, increasing the capacity of existing road and railway checkpoints, as well as the opening of new ones; increasing cargo handling in the ports of the Danube port cluster.

Clustering-based improved genetic algorithm for solving rural logistics route planning problem with conventional and new energy vehicles

Clustering-based improved genetic algorithm for solving rural logistics route planning problem with conventional and new energy vehicles

Ant Colony Optimization and Beam-Ant Colony Optimization on Traveling Salesman Problem with Traffic Congestion

The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem that has various implications in a variety of industries. Even the purest formulation of TSP has applications on from logistics routes to microchip manufacturing, unexpectedly, it can be used on DNA sequencing with slight modification as a sub-problem. In this paper, two versions of TSP were studied, a classical TSP and the TSP containing traffic congestion data. Two state-of-the-art solution methods were used, Ant Colony Optimization (ACO) and Beam-ACO. These algorithms were hybridized with 2-Opt local search and their performances compared on the same benchmark instances. The experimental results show the efficiency of Beam-ACO compared to ACO.

Integrated optimization of logistics routing problem considering chance preference

PurposeThis study aims to examine the impact of the decision makers’ risk preference on logistics routing problem, contributing to logistics behavior analysis and route integration optimization under uncertain environment. Due to the unexpected events and complex environment in modern logistics operations, the logistics process is full of uncertainty. Based on the chance function of satisfying the transportation time and cost requirements, this paper focuses on the fourth party logistics routing integrated optimization problem considering the chance preference of decision makers from the perspective of satisfaction.Design/methodology/approachThis study used the quantitative method to investigate the relationship between route decision making and human behavior. The cumulative prospect theory is used to describe the loss, gain and utility function based on confidence levels. A mathematical model and an improved ant colony algorithm are employed to solve the problems. Numerical examples show the effectiveness of the proposed model and algorithm.FindingsThe study’s findings reveal that the dual-population improvement strategy enhances the algorithm’s global search capability and the improved algorithm can solve the risk model quickly, verifying the effectiveness of the improvement method. Moreover, the decision-maker is more sensitive to losses, and the utility obtained when considering decision-makers' risk attitudes is greater than that obtained when the decision-maker exhibits risk neutrality.Practical implicationsIn an uncertain environment, the logistics decision maker’s risk preference directly affects decision making. Different parameter combinations in the proposed model could be set for decision-makers with different risk attitudes to fit their needs more accurately. This could help managers design effective transportation plans and improve service levels. In addition, the improved algorithm can solve the proposed problem quickly, stably and effectively, so as to help the decision maker to make the logistics path decision quickly according to the required confidence level.Originality/valueConsidering the uncertainty in logistics and the risk behavior of decision makers, this paper studies integrated routing problem from the perspective of opportunity preference. Based on the chance function of satisfying the transportation time and cost requirements, a fourth party logistics routing integrated optimization problem model considering the chance preference of decision makers is established. According to the characteristics of the problem, an improved dual-population ant colony algorithm is designed to solve the proposed model. Numerical examples show the effectiveness the proposed methods.

Моделі відновлення транспортно-логістичної інфраструктури скотарства на деокупованих територіях україни

In the conditions of de-occupation and restoration of the territories of Ukraine, an important task is the restoration of the transport and logistics infrastructure for the efficient functioning of the livestock industry. The study covers the analysis of existing problems related to the destruction of infrastructure due to military actions and proposes a set of models that contribute to the rapid and efficient recovery of this industry. The proposed models in the article are aimed at restoring production capacities, improving the efficiency of logistics processes, and ensuring the sustainable development of cattle breeding in the de-occupied territories of Ukraine. A comprehensive approach to solving the problems of infrastructure restoration will not only restore economic activity but also contribute to the long-term development of the regions. The de-occupation of the territories of Ukraine affected by military operations creates new challenges for the recovery of various sectors of the economy, including cattle breeding. The transport and logistics infrastructure, which is critical to the functioning of this industry, has suffered significant destruction. Therefore, restoring this infrastructure is critically important for ensuring the region’s food security, economic growth, and stability. Optimizing logistics routes is an essential component of infrastructure recovery. New realities of safety and accessibility must be considered, including alternative delivery routes and the use of local resources. Restoring the transport and logistics infrastructure of cattle breeding in the de-occupied territories of Ukraine is extremely important for the economic revival of the regions. The application of complex recovery models, considering modern technologies and innovative approaches, will ensure the sustainable development of agriculture and raise the standard of living of the local population. In the above-mentioned scientific study, all the main challenges and critical limitations related to the restoration and development of the transport infrastructure of Ukraine are highlighted. Keywords: transport and logistics chains, de-occupied territories, economic management, transport infrastructure, model, technologies, strategy.

AI integration in supply chain and operations management: Enhancing efficiency and resilience

Artificial intelligence (AI) has become a transformative force in supply chain and operations management, offering significant enhancements in efficiency and resilience. This paper examines the integration of AI technologies such as machine learning, predictive analytics, and real-time data processing in demand forecasting, inventory management, logistics, and risk mitigation. By analyzing diverse data sources, AI improves demand forecasting accuracy, reduces inventory costs, optimizes logistics routes, and enhances supply chain visibility. Case studies and data-driven insights demonstrate how AI-driven systems enable companies to adapt to market dynamics, prevent disruptions, and achieve substantial cost savings. The findings suggest that embracing AI is essential for businesses aiming to optimize their supply chain operations and build robust, resilient frameworks capable of withstanding future challenges.

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

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

Prospects for the development of transport and logistic routes connecting the BRICS Countries

This article examines the current metrics and future prospects for the development of transportation and logistics routes connecting BRICS member countries (Brazil, Russia, India, China, South Africa, Egypt, Ethiopia, UAE, Saudi Arabia and Iran). It analyses railway transport indicators, the dynamics and structure of mutual trade, as well as the main destinations of Russian exports. A special focus is placed on infrastructure projects and trends in multimodal transportation. The study highlights the significance of investment in infrastructure and the optimization of logistics processes, emphasizing the potential for enhanced economic cooperation among BRICS members.

Institutional peculiarities of the functioning of the economy of Ukraine in the conditions of martial law

The paper shows that the peculiarities of the state of war force the state to reorient its resources and attention to solving critical tasks related to ensuring national security and overcoming the consequences of war. As a result, the priorities of formation, redistribution and use of resources change, which affects the socio-economic model of the state. However, the limiting conditions for adjusting the model of the national economy in the conditions of war are: preserving its economic potential and maintaining viability, which will ensure the future development of the country and its post-war recovery. It is shown that since the beginning of the full-scale invasion of the Russian Federation into Ukraine, the share of the military-industrial complex and related industries in the overall structure of the economy has increased. These changes caused significant difficulties for the economy of Ukraine, but also opened up new opportunities for adaptation and development in crisis conditions. Increasing investment in the military industry, developing new logistics routes and diversifying the economy can become part of the country's recovery and development strategies after the war. It has been proven that positive changes in regulatory and legal support, aimed at facilitating business conditions, are necessary, but not sufficient. In order to solve the fundamental problems of the loss of sales markets and opportunities for continued economic activity, strategic measures are needed, including the restoration of infrastructure, support for exporters, stimulation of business relocation and support of labor resources. It is substantiated that the institutional features of the functioning of the economy of Ukraine under martial law are related to the formation of a hybrid model, which is a synthesis of two models, namely: the model of the military economy, designed to meet military needs and vital goods and services, as well as the model of the civilian economy, designed to meet the needs of the population. A comprehensive solution to the outlined problems requires coordination of the efforts of the government, business and international partners to ensure the stability and development of Ukraine's economy in the conditions of the war and after its end, as well as to ensure its sustainable development in the future.

Round-trip multimodal transportation routes planning for foldable vehicle racks

ABSTRACT Over the past decade, the expansion of China’s auto exports and pulp imports has presented challenges to the international shipping sector. This study examines the features inherent in the novel paradigm employed by COSCO Shipping Specialized Carriers. Specifically, it involves the transportation of Foldable Vehicle Racks (FVRs) using multipurpose pulp carriers on the outbound trip, and the conveyance of pulp alongside empty FVRs on the return voyage. Building upon this foundation, we propose a dual-objective route planning model and construct a round-trip multimodal transportation network. Subsequently, we calculate the optimal scheme, encompassing shipping routes, changeover nodes, and transport modes, using a hybrid genetic and ant colony algorithm. The paper scrutinizes the computational results, validates the effectiveness of the hybrid algorithm, and analyzes the scheme variations across different parameter scenarios, along with conducting a sensitivity analysis. The optimal solution calculated in this paper is found to reduce transit time by 24.77% while improving logistics benefits by 4.11% compared to the firm’s existing shipping schedule. Furthermore, we explore alternative optimal logistics routes in various parameter variation scenarios and considering benefit-time preferences. Eventually, six managerial insights are proposed for the sustainable development of COSCO’s FVR and pulp business based on the experimental results.

Research on the Optimization of Cross-Border Logistics Routes under the Belt and Road Initiative

Within the framework of the Belt and Road Initiative, optimizing cross-border logistics routes is essential for enhancing the efficiency of logistics services and reducing transportation costs. This paper analyzes the current state of logistics networks in countries along the Belt and Road route, identifying major obstacles to the efficiency of cross-border logistics such as uneven infrastructure development, complex transnational policies and regulations, and significant geographical and political risks. The study further introduces the theoretical foundation of logistics route optimization and discusses the application of various optimization models and algorithms, such as linear programming and network flow analysis. The paper also highlights the role of modern technologies, such as artificial intelligence and the Internet of Things, in route optimization. These technologies enhance route selection and freight scheduling through real-time data processing and predictive models. Based on these analyses, specific strategies and implementation suggestions are proposed to reduce transportation time and costs by optimizing the logistics network configuration.

STUDYING THE IMPACT OF THE VOLUME OF WORK AT THE RAILCAR TRANSFER POINT ON THE NEED FOR TRACK STORAGE CAPACITY FOR BOGIES

Purpose. In the context of military aggression by moscow, export cargo flows, which were largely transported by sea from Ukraine's Black Sea ports, were directed towards western land crossings. At the same time, exporters were trying not only to find new logistics routes but also to increase exports to European countries. This resulted in longer queues at land borders, both road and rail, and the turnover of railcars and their individual components became uncontrollable and unpredictable. The reason for the downtime of cargo in railroad cars at the borders was the limited processing capacity of railcar interchange points and the transshipment capacity of cargo terminals at border stations. One of the elements of the cargo border crossing system, the increase of which will increase the volume of export cargo flow, is the points of change of wagons from 1435 mm gauge to 1520 mm gauge. Therefore, the aim of this study is to determine the required track capacity for storing 1520 mm and 1435 mm gauge bogies under conditions of daily uneven transportation and the stochastic nature of the turnover of cars on the territory of European railways. Methodology. Simulation modeling, mathematical statistics, and inventory management methods were used to study the impact of the workload of a railcar transfer point on track capacity. Results. As a result of the research, the numerical characteristics of a random variable of the number of cars transferred to the transfer point during the day, as well as the value of car turnover in Europe, were obtained. A methodology for determining the required track capacity for storing bogies based on simulation modeling of the operation of the railcar interchange points was developed. The dependence of the guaranteed stock of bogies of 1435 mm gauge at the station on the daily number of cars serviced at the railroad car transfer point was obtained. Practical significance. The obtained dependences of the required capacity of the tracks for storing bogies on the daily number of cars arriving at the transfer point will allow us to check and, if necessary, bring the existing track development of border stations into line with the calculated volumes of work.

Predictive analytics on artificial intelligence in supply chain optimization

AI-powered predictive analytics is among the most important ways of optimizing supply chains. This paper on AI-powered predictive analytics will address improving the competitiveness and effectiveness of supply chain operations. Nevertheless, current methods are not always scalable or adaptable to complex supply networks and changing market environments. Therefore, this paper posits that Supply Chain Optimization using Artificial Intelligence (SCO-AI) systems can help with these concerns. SCO-AI employs real-time data analysis and advanced machine learning algorithms which results to reduced response time, enhanced logistics route optimization, improved demand planning as well as real-time inventory control. Thus, the idea herein suggested fits smoothly into existing supply chain frameworks for data-driven decisions that make companies remain agile in ever-changing market dynamics. SCO-AI implementation has seen significant improvements in inventory turnover rate, rates of on-time delivery as well as overall supply chain costs. In this period of high business turbulence, such kind of research builds up the robustness of a given supply chain while at the same time minimizing operational risks by means of simulations and case studies

ANALYSIS OF THE EXPORT AND IMPORT OPERATIONS DYNAMICS IN UKRAINE AND THE EU

Objective. The objective of the article is to analyze the current trends in export-import transactions between Ukraine and the EU and to determine their features during the large-scale invasion. Methods. During the research, the following methods are applied: the dialectical method of learning processes and phenomena (for a qualitative study of the dynamics of export-import operations between Ukraine and the EU); graphical method (for graphical presentation of current dynamics and forecast values of export-import operations between Ukraine and the EU); index (to estimate the share of imports and exports between Ukraine and the EU in the total volume of imports and exports of Ukraine, respectively); one-factor and three-factor autoregressive forecasting method (for forecasting the volume and share of export-import transactions between Ukraine and the EU). Results. Before the large-scale invasion, the EU was one of Ukraine's key foreign trade partners. As of 2021, the EU accounted for 50% of Ukrainian exports. In the case of imports, the share of the EU was 63% of all imports to Ukraine. Since the beginning of the large-scale invasion, the share of exports from Ukraine to the EU has increased to 70%, underscoring the importance of the EU as a major trading partner that not only contributes to economic stability, but also provides important logistics routes for the export of Ukrainian goods to global markets. Forecasting the share of imports and exports between Ukraine and the EU shows that, despite the decrease in exports and imports due to the large-scale invasion, the share of exports to the EU in total exports of Ukraine will increase, while the share of imports from the EU will remain constant. This testifies to the stability and importance of trade relations between Ukraine and the EU in the difficult conditions of modern global challenges. The executed forecast is based on the assumption that the large-scale invasion will continue. If the situation changes and the period of post-war recovery comes, it will be necessary to build a new forecast that will reflect new prospects for economic growth and integration of Ukraine into the world economy.

Deceptive Path Planning via Count-Based Reinforcement Learning under Specific Time Constraint

Deceptive path planning (DPP) aims to find a path that minimizes the probability of the observer identifying the real goal of the observed before it reaches. It is important for addressing issues such as public safety, strategic path planning, and logistics route privacy protection. Existing traditional methods often rely on “dissimulation”—hiding the truth—to obscure paths while ignoring the time constraints. Building upon the theory of probabilistic goal recognition based on cost difference, we proposed a DPP method, DPP_Q, based on count-based Q-learning for solving the DPP problems in discrete path-planning domains under specific time constraints. Furthermore, to extend this method to continuous domains, we proposed a new model of probabilistic goal recognition called the Approximate Goal Recognition Model (AGRM) and verified its feasibility in discrete path-planning domains. Finally, we also proposed a DPP method based on proximal policy optimization for continuous path-planning domains under specific time constraints called DPP_PPO. DPP methods like DPP_Q and DPP_PPO are types of research that have not yet been explored in the field of path planning. Experimental results show that, in discrete domains, compared to traditional methods, DPP_Q exhibits better effectiveness in enhancing the average deceptiveness of paths. (Improved on average by 12.53% compared to traditional methods). In continuous domains, DPP_PPO shows significant advantages over random walk methods. Both DPP_Q and DPP_PPO demonstrate good applicability in path-planning domains with uncomplicated obstacles.

A low-carbon route optimization method for cold chain logistics considering traffic status in China

In the paper of cold chain logistics, it is necessary to consider both traffic status and carbon reduction simultaneously. In this paper, an improved BPR (Bureau of Public Roads) road impedance function is designed, which reflects the traffic status in China under congested status more accurately. A low-carbon cold chain logistics route optimization model considering the traffic status in China is constructed, considering the multiple impacts of traffic status on total delivery cost, freshness of fresh products, and carbon emissions. To address the challenges posed by this multidimensional and discrete model, an improved discrete firefly algorithm (IDFA) is proposed. The numerical result demonstrates that IDFA has higher solving efficiency and better quality of the obtained solutions. Moreover, this paper also analyzes the impact of traffic status, vehicle types, and carbon trading prices on cold chain logistics delivery, as well as the comprehensive impact of traffic status and carbon trading prices. The proposed model is suitable for cold chain logistics enterprises with a single delivery center, active emission reduction willingness, and mostly delivering in congested road sections with high traffic volume. It offers valuable guidance for decision making and management in low-carbon cold chain logistics delivery.