Transport Logistics Research Articles

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.

The Situation of Freight Transport and Other Logistics Tasks in Laos

This study aims to analyse the obstacles and constraints in the shipping and logistics in Laos, a landlocked nation in Southeast Asia. Transportation and logistics play a pivotal role in Laos' national economy, acting as the lifeblood for the movement of goods and materials within the country and linking it to the international trade system. This case study highlights the significance of a robust logistics structure for Laos and its essential role in fostering economic development, agricultural growth, and connectivity. It also underscores the challenges faced by the country's logistics sector, such as limited-service frequency and road safety concerns. The study examines Laos transportation infrastructure, encompassing road, rail, and air networks, and sheds light on the obstacles and disparities that hinder value chain development in the country. These issues include disconnected stakeholder participation, the aging vehicle fleet, and financial limitations. Furthermore, the essay emphasizes the need for improved interconnectivity and inter-mobility to enhance the national, regional, and international connectivity of Laos and similar landlocked developing countries in Asia.

Logistics distribution route optimization of electric vehicles based on distributed intelligent system

Abstract The data management system of health cloud authentication plays an important role in the optimization of logistics vehicle routing. It can not only help logistics vehicles choose the best distribution path, but also save time and cost and improve economic efficiency. At present, logistics has not yet formed a complete service system. High distribution costs and low distribution efficiency limit the development of the entire logistics. The reduction of logistics costs and the improvement of distribution efficiency have become the top priorities of the society. The optimization of the distribution route is the key to cost saving and distribution logistics. It is particularly important to study and optimize the distribution route, because the distribution route affects the logistics transportation efficiency and the loss cost during transportation. Therefore, this paper adjusted the scheduling system of logistics vehicles through a distributed intelligent system, and optimized the path of logistics vehicles according to the improved genetic algorithm, thereby reducing the transportation cost of logistics and improving the efficiency of logistics distribution. This article first explains the definition, classification, and main components of the delivery vehicle routing problem. Then, using cloud authentication path optimization, a distributed intelligent system is constructed. Finally, an improved ant colony algorithm is used to analyze and study the distance constraints of vehicles. By improving the ant colony algorithm, it can be seen that the optimized path pheromone concentration and the optimized sub-function have gradually increased with time. The mean pheromone concentration was 40 %, and the seventh day was 15 % higher than the first. The mean value of the optimized subfunction was 0.34 %, and the seventh day was 20 % higher than the first. The distribution cost and distribution efficiency of the optimized logistics vehicle distribution path were much higher than those of the traditional logistics distribution path. Moreover, the distribution cost of the logistics distribution path was 9 % lower than the traditional one, and the distribution efficiency was 13 % higher. The average smoothness of the optimized logistics path is about 90 %, and the seventh day is 11 % higher than the first day. The average fitness of the optimized logistics path is 88 %, and the seventh day is 14 % higher than the first day. In a word, the data management system can uniformly schedule logistics vehicles and improve the efficiency of distribution.

Problems and development prospects of “green” logistics of maritime transport in the Russian Federation

Shipping is one of the industries that lags far behind others in environmental sustainability, especially when it comes to reducing the harmful impact on the environment, which makes the problem relevant. The article presents the concept of “green” logistics and its principles used in business practices. Current key problems of maritime transport related to environmental logistics and new requirements of the International Maritime Organisation are described. The experience of applying “green” technologies by foreign companies in the maritime industry has been analysed. It shows that electrification and decarbonisation can be successfully introduced into business practice and lead to improved environmental performance. The state of seaport infrastructure and vessels in the Russian Federation was studied. Certain Russian projects following “green” logistics trends are presented with description of the implemented systems, technologies, and new equipment. Conclusions are made about the potential of “green” logistics in Russia in the future: reduction of negative environmental impact, increase of competitiveness of Russian companies and new opportunities for business development.

ARIMA-based Freight Forecasting and Network Optimization in E-commerce Logistics

Considering the proliferation of e-commerce platforms exemplified by Taobao and JD, the paradigm of online shopping has evolved into an integral facet of contemporary societal existence. To enhance network transport performance, it is essential to predict freight volume, assess existing capacity, and establish new sites to alleviate network pressure. This study focuses on a logistics transportation network, utilizing daily freight turnover data from January 1, 2021, to December 31, 2022. The ARIMA time series method is employed to forecast freight volumes at different sites within the e-commerce logistics network. Taking three logistics site route combinations, namely DC14→DC10, DC20→DC35, and DC25→DC62, as examples, two differencing operations are applied to meet the stationarity requirement of the ARIMA model. Autocorrelation and partial autocorrelation coefficients are used for preliminary model order determination. By minimizing the Akaike Information Criterion (AIC) value, an ARIMA (5) model is established. Single and multiple-step predictions are conducted, resulting in forecast curves for future freight volumes. Subsequently, an evaluation of the transportation network is performed, considering the importance of nodes and routes. The entropy weight method is applied to determine the weights of evaluation indicators. Importance indices for nodes and routes are calculated, leading to a ranking. For the assessment and selection of new site capabilities, Fisher's discriminant function is employed to classify different sites. The valuation level for site selection is determined, providing a scientific basis for the systematic choice of new sites.

Utilizing Amphibious AGVs to Optimize Container Transshipment for Deep Sea and Hinterland Operations

Transshipment is a key component of modern-day shipping logistics. Container supply chains rely on tran-shipment hubs to access remote locations. With globalisation driving growth in container trade, maritime congestion is rising at container terminals in ports worldwide. This is expected to worsen as demand con-tinues to grow. This research explores novel maritime equipment designs that can contribute to solving problems in the trans-shipment chain. One such idea is that of the Amphibious Automated Guided Vehicle, an innovative concept that travels on both land and sea. Envisioned as a tool to minimise the rehandling of containers, the Amphibious AGV forms the heart of the new changes that this research proposes for the fu-ture of trans-shipment. Complementing swifter trans-shipment, the research also proposes complementary design concepts such as floating terminals to add more flexibility for container ships and Amphibious AGVs applied to exchange containers offshore. To validate these ideas, an agent-based modelling methodology was used and replicated in the environment of the Hong Kong- Pearl River Delta. This work, therefore, opens up an intriguing future scope for maritime transshipment that is both sustainable and adaptable while also discussing limitations and concerns that need to be carefully considered.

ASSESSMENT OF FRUIT SUPPLY CHAIN RISK

The main aim of this research is to study gain a better understanding of the fruit supply chain and the individuals that work inside it, from farmers to distributors to retailers. Agricultural landscape in India relies heavily on fruit supply chains to ensure a consistent flow of nutritious produce from farmers to consumers. However, challenges such as post-harvest losses, inadequate infrastructure, and market fragmentation persist, impacting both economic prosperity and environmental sustainability. This paper advocates for the development of a comprehensive assessment model for fruit supply chains to address these challenges effectively. By examining production processes, transportation logistics, market dynamics, and stakeholder engagement, the proposed model aims to identify inefficiencies and improvement opportunities. Collaboration among diverse stakeholders is emphasized to ensure the model's adaptability and relevance across different production systems and regions. Furthermore, insights from the global fruit market underscore the importance of addressing emerging trends such as changing consumer preferences and technological innovations. By leveraging digital technologies and promoting sustainable practices, the fruit industry can navigate challenges and capitalize on opportunities for growth and resilience in the years to come. KEYWORDS Fruit supply chains risk, Rik assessment, Failure Mode and Effect Analysis

Optimizing the process of sewage sludge collection for bioenergy generation and biofertilizer production

The conversion of sludge from sewage treatment plants (STPs) into bioenergy is a promising approach to sustainable energy generation and effective waste management. This process occurs through anaerobic digestion in the biodigesters, resulting in biogas production. However, given that the sludge comes from various STPs in different cities, identifying the ideal locations requires careful selection due to the complexity of sludge management, associated costs, and transportation logistics. In this context, this work aims to formulate a mathematical optimization model to determine the best locations for the installation of sludge processing centers, considering the minimization of the economic, environmental, and social costs. The model is implemented in MATLAB software, and geospatial analysis of the regions of interest is performed using QGIS software. For model validation, three distinct instances are considered. The results obtained demonstrate significant potential in terms of biogas and electricity production, with the highest values for the third instance being 61,997,501 Nm³/year and 149,275 MWh/year, respectively. The proposed methodology represents a crucial step toward sustainability, as it promotes the generation and use of bioenergy from STP sludge and contributes to improved waste management and the prevention of air, soil, and water pollution.

Advantages of high cell concentration prior to cryopreservation of initial leukapheresis in CAR-T cell therapy.

Chimeric antigen receptor (CAR) T-cell therapy is increasingly used in patients affected by B-cell lymphoma and acute lymphoblastic leukemia. For logistical reasons, initial apheresis products may be cryopreserved for shipment to manufacturing centers. Due to the characteristics of these patients, cells are often collected in large volumes, meaning more bags must be cryopreserved. This requires increased storage, time and monetary costs. In this context, we aimed to evaluate a high cell concentration cryopreservation protocol by centrifugation to standardize the initial CAR-T manufacturing procedure. Sixty-eight processes of leukapheresis of 57 patients affected by refractory/relapsed B cell lymphoma and 9 patients affected by acute lymphoblastic leukemia who were eligible for anti-CD19 CAR-T cell treatment performed between June 2019 and October 2022 were analyzed. Whole blood count, percentage and number of T cells were assessed on the apheresis final product. The apheresis product, which was alternatively stored overnight at 4°C, was centrifuged, adjusting the volume to approximately 40 mL. The product was immediately cryopreserved to achieve a final cell concentration of 50-200×106 cells/ml for cryopreservation. Leukapheresis volume was reduced by almost fivefold (median: 185 to 40 mL), resulting in a higher product concentration in one bag. In addition, the number of non-target cells (monocytes, platelets and erythrocytes) was also reduced during the development of CAR-T cell therapy, thereby maintaining T lymphocyte levels and providing a purer starting material. The advantages of the protocol include reducing economic costs, saving storage space, simplifying the manufacturing process, and facilitating shipping logistics. In conclusion, we present a validated, simple, and cost-effective cell enrichment processing protocol that provides high-quality cryopreserved products as starting material for the CAR-T cell manufacturing process.

Identifying the influence of land logistic driver cognitive energy impact on supply chain performance through agent-based simulation

Logistic transports link demand generators, distributors, and producers in a supply chain network (SCN). The existence of logistic transports is critical to ensure whole nodes’ economic sustainability. This study explores the impact of human factors on SCN performance through cognitive energy expenditure (CEE) tracking. Agent-based model (ABM) simulation was used to analyze the impact of CEE from truck driver’s electroencephalography (EEG) data to obtain the postsynaptic potential values, which were then transformed to calorific energy. The fleet agents, retailers and distributor models were built based on the East Java, Indonesia, logistic transport route around Karanglo, Gempol, Bungurasih, and Gubeng. The frequency and the peak value of the EEG data, postsynaptic potential, and energy data indicate the same information. All data indicate that more challenging routes have higher frequency and higher peak values. The ABM simulation of the fleet agents shows balanced CEE throughout entire routes due to the precise rest period and eat scheduling. The average delivery success rate was 8 out of 30 or 26.7 % in each simulation time step. Hence, most goods delivery tasks can be completed by fleet agents in a balanced system. As a consequence, the SCN performance is also balanced due to the fluid inventory shift without overstock and stockouts. The rest and eat periods of a fleet agent were scheduled after the CEE has been peaked. The time lag between rest periods and transport operations has to be maintained to overcome fleet agents task buildup. Task buildup has a potential to decay both transport safety and inventory shift rates. Therefore, the upgrade in SCN performance is possible through proper fleet agents scheduling

Конъюнктура мирового рынка СПГ в условиях санкционного давления

The article reveals aspects of the situation on the global LNG market in the context of sanctions pressure on Russian LNG enterprises, and suggests ways for Russian LNG enterprises to adapt to these conditions. The conditions of sanctions pressure on Russian LNG enterprises are described and the impact of sanctions on the LNG industry is revealed. The conducted research is based on statistical data from Russian and foreign sources. Aspects of the global LNG market environment include: an increase in the scale of LNG production in the world, the use of innovative gas liquefaction technologies, the development of a system for organizing and managing the transportation of liquefied gas, an increase in the number of states purchasing LNG for their needs, changes in the volume of LNG production and consumption in the world. The analysis presented in the article of the prospects and directions of development of this market in the future, as well as the main economic and technical factors influencing its functioning, and its conditions over the past five years have shown that this market is actively developing. The authors come to the conclusion: Russian LNG exporting enterprises have managed to adapt to the current competitive market conditions of global LNG trade, diversify their capacities and transport logistics, and have also significantly expanded the possibilities of transporting LNG along the Northern Sea Route.

Explainable artificial intelligence in transport Logistics: Risk analysis for road accidents

Automobile traffic accidents represent a significant threat to global public safety, resulting in numerous injuries and fatalities annually. This paper introduces a comprehensive, explainable artificial intelligence (XAI) artifact design, integrating accident data for utilization by diverse stakeholders and decision-makers. It proposes responsible, explanatory, and interpretable models with a systems-level taxonomy categorizing aspects of driver-related behaviors associated with varying injury severity levels, thereby contributing theoretically to explainable analytics. In the initial phase, we employed various advanced techniques such as data missing at random (MAR) with Bayesian dynamic conditional imputation for addressing missing records, synthetic minority oversampling technique for data imbalance issues, and categorical boosting (CatBoost) combined with SHapley Additive exPlanations (SHAP) for determining and analyzing the importance and dependence of risk factors on injury severity. Additionally, exploratory feature analysis was conducted to uncover hidden spatiotemporal elements influencing traffic accidents and injury severity levels. We developed several predictive models in the second phase, including eXtreme Gradient Boosting (XGBoost), random forest (RF), deep neural networks (DNN), and fine-tuned parameters. Using the SHAP approach, we employed model-agnostic interpretation techniques to separate explanations from models. In the final phase, we provided an analysis and summary of the system-level taxonomy across feature categories. This involved classifying crash data into high-level causal factors using aggregate SHAP scores, illustrating how each risk factor contributes to different injury severity levels.

THE STRATEGIC PLANNING OF TRANSPORT INFRASTRUCTURE AND MANAGEMENT OF LOGISTICS SOLUTIONS IN CONDITIONS OF WAR

This article examines the challenges faced by Ukraine, which is embroiled in a military conflict within its borders, particularly the imperative to ensure the efficiency of its transport infrastructure and logistics processes. It has been identified that military actions result in damage to transport networks, restricted access to critical resources, and alterations in logistics operations. It has been established that in wartime conditions, strategic planning of transport infrastructure and effective management of logistics solutions become particularly pertinent as they dictate the country's capacity to meet its own needs and accomplish strategic objectives. Furthermore, it has been determined that Ukraine's future integration into Europe necessitates reforming its transport and logistics system to align with EU standards. Strategic approaches to restoring the transport network and addressing logistics challenges during conflict are discussed, primarily focusing on alternative routes, the development of effective supply chain management systems, and enhancing system redundancy. Strategies for adaptation and response to changes aimed at ensuring the reliability and efficiency of transport and logistics processes in Ukraine have been explored.

Distributed Lag Non-linear Model을 이용한 Baltic Dry Index 예측 및 요인 별 시차 영향도 도출에 관한 연구

The Baltic Dry Index (BDI) is a crucial and representative indicator in the shipping logistics industry sector for evaluating the market conditions, providing valuable information on the movement of global trade and production operations. The BDI, known for its significant volatility, has been the focus of several research initiatives utilizing machine learning and deep learning approaches to improve accuracy. Nevertheless, these approaches frequently encounter difficulties in understanding the results of the predictions. This study provides a framework that utilizes the Distributed Lag Non-linear Model to enhance the predictive accuracy of the BDI. The proposed model has the capability of capturing lag effects based on different factors to improve the prediction results. To evaluate the efficacy of the suggested approach, we carried out comparison experiments utilizing four performance metrics that have been widely utilized in previous studies to compare with different machine learning and deep learning models. The results showed enhanced prediction accuracy in comparison with existing methodologies. The lag effects by factor have been examined using the trained DLNM model, and we verified that the analyzed results are in good agreement with prior research by comparing them with previous research

The Impact of Varying Mixing Rates in a Surfactant-Free Fuel Emulsion Mixer on the Efficiency and Emissions of a Diesel Engine

The research focuses on water diesel emulsion (WDE), a topic that has captivated researchers for an extended period. While previous studies predominantly employed surfactants to enhance mixing efficiency, their non-economic feasibility in transportation logistics has prompted a shift in recent investigations. This study presents experiments utilizing a cost-effective WDE comprising 15% water and a mixer devoid of surfactants to investigate the impact of mixer blade rotation on engine performance, fuel consumption, and NOx emissions. NOx emission tests were conducted under a constant engine speed of 2,000 rpm and a 75% load (3,23 kW). The optimal brake-specific fuel consumption (BSFC) for the 15% WDE fuel occurred at a blade rotation speed of 3,000 rpm, resulting in a 1% power reduction (from 4,41 kW to 4,38 kW), a 13.3% decrease in BSFC (from 694,98 gr/kW.h to 602,52 gr/kW.h), and a 30% reduction in NOx emissions (from 54 ppm to 38 ppm). This discovery holds promise for future advancements in green energy applications within the transportation sector.

Simulated logistical transport effects on textural change of wax apple using different packaging types during storage time

The textural damage caused by logistics transport is a major problem during the post-harvest logistics and storage time of agricultural products. To address the issue, this study investigated the impact on the textural change of wax apple with different packaging types by conducting international safe transit association 6-amazon. com-sioc type A (ISTA 6 A) tests and measured the changes in related indicators over the eight-day storage time period at 15 ℃. The results demonstrated that compared to non-vibrated wax apple of the same packaging type, the damage index of the vibration-treated wax apple increased to 0.39 at the end of storage time. This led to more noticeable damage to fruit texture in the vibration-treated wax apple. With the same outer packaging type, different types of expanded polyethylene (EPE) cushioning materials presented varying degrees of textural damage following vibration treatment. The order was double-layer foam net (DL-FN, 0.23) < single-layer foam net (SL-FN, 0.38) < EPE liner (EPE-L, 0.50). The superior cushioning performance of DL-FN could distribute vibratory energy across the entire wax apple to reduce partial textural injury, but SL-FN provided weaker cushioning protection. Furthermore, it was more beneficial for the dissipation of respiratory heat to suppress the related cell-wall enzymes activities and prevented internal cottony softening of wax apple by maintaining an appropriate environment within the packaging, while EPE-L prevented respiratory heat dissipation and water diffusion. Using the same cushioning type, no significant difference was observed in the damage of fruit texture between the wax apple packaging by foam box (FB, 0.25) and corrugated box (CB, 0.23). The finding suggested that when the outer package had sufficient supporting performance, the cushioning type required not only adequate cushioning property, but also excellent vapor and gas permeability in order to facilitate respiratory heat diffusion and better protect the textural integrity of the wax apple in the process of logistics. This protection was crucial for the development of wax apple in e-commerce logistics.

Impact of green logistics activities and service quality in the e-commerce sector on customer satisfaction

Measurement research on the impact of green logistics activities and logistics service quality in the field of e-commerce on customer satisfaction is based on 5 factors measuring green logistics activities, including green transportation, warehousing, green information, green packaging, and reverse logistics. In addition, the factors considered to measure the quality of logistics services are punctuality, order status, order accuracy, and handling of order discrepancies. Research has shown that green logistics factors have a stronger impact on customer satisfaction than service quality in the field of e-commerce. The research results were conducted by surveying 350 consumers by age and region. By analysing and processing through SPSS 26 and Amos 20 software, the study has shown that green logistics factors and service quality in the field of e-commerce have an impact on customer satisfaction. From this result, the article proposes some solutions to help businesses in the field of e-commerce improve logistics activities and service quality increasingly better in response to increasing market requirements.

An Adaptive Large Neighborhood Search Algorithm for Equipment Scheduling in the Railway Yard of an Automated Container Terminal

In container sea–rail combined transport, the railway yard in an automated container terminal (RYACT) is the link in the whole logistics transportation process, and its operation and scheduling efficiency directly affect the efficiency of logistics. To improve the equipment scheduling efficiency of an RYACT, this study examines the “RYACT–train” cooperative optimization problem in the mode of “unloading before loading” for train containers. A mixed-integer programming model with the objective of minimizing the maximum completion time of automated rail-mounted gantry crane (ARMG) tasks is established. An adaptive large neighborhood search (ALNS) algorithm and random search algorithm (RSA) are designed to solve the abovementioned problem, and the feasibility of the model and algorithm is verified by experiments. At the same time, the target value and calculation time of the model and algorithms are compared. The experimental results show that the model and the proposed algorithms are feasible and can effectively solve the “RYACT–train” cooperative optimization problem. The model only obtains the optimal solution of the “RYACT–train” cooperative scheduling problem with no more than 50 tasks within a limited time, and the ALNS algorithm can solve examples of various scales within a reasonable amount of time. The target value of the ALNS solution is smaller than that of the RSA solution.

Development and Research of Safety Video Monitoring System for Loading and Unloading of Chemical Products in Railway Transportation

As the important position of chemical railroad transportation in modern logistics is becoming more and more prominent, its safety problem has attracted wide attention. To improve the safety of transporting chemicals by rail, this paper strives to develop an advanced safety monitoring system. The main background of this paper is the serious potential results of chemical products railroad transportation accidents which require an effective monitoring method to prevent. The purpose of this research is to design and realize an efficient and reliable video monitoring system to monitor the loading and unloading of chemicals transported by railroads in real-time. To achieve this goal, we adopted comprehensive methods, including video image recognition technology, data analysis technology, communication network technology, etc., which have significant meaning in preventing railroad accidents of chemical product transportation and safeguarding people’s life and property.

A multiobjective approach for weekly Green Home Health Care routing and scheduling problem with care continuity and synchronized services

Home Health Care (HHC) services are essential for delivering healthcare programs to patients in their homes, with the goal of reducing hospitalization rates and improving patients’ quality of life. However, HHC organizations face significant challenges in scheduling and routing caregivers for home care visits due to complex criteria and constraints. This paper addresses these challenges by considering both caregiver assignments and transportation logistics. The objective is to minimize the total travel distance and CO2 emissions while ensuring a balanced workload for caregivers, meeting patients’ preferences, synchronization, precedence, and availability constraints. To tackle this problem, we propose a multiperiodic Green Home Health Care (GHHC) framework. In the first stage, we utilize multiobjective programming and the NSGA-II algorithm to generate Pareto front solutions that consider travel distance and CO2 emissions. In the second stage, a Mixed-Integer Linear Programming (MILP) model is proposed to balance caregivers’ workload by assigning them to the patient routes generated in the first stage. The results highlight the trade-off between shorter routes and lower emissions. Furthermore, we examine the impact of prioritizing continuity of care and patient satisfaction. This research provides valuable insights into addressing the scheduling and routing challenges in HHC services, contributing to a more efficient and environmentally friendly healthcare delivery.