Transport Routes Research Articles

A quadratically constrained mixed-integer non-linear programming model for multiple sink distributions

Rising traffic congestion and fuel costs pose significant challenges for supply chains with numerous retailers. This paper addresses these challenges by optimizing transportation routes for processed tomatoes within a long-haul and intercity distribution network. We use the heterogeneous capacitated vehicle routing problem framework to create a new quadratically constrained mixed-integer non-linear programming model that aims to meet demand at multiple destinations while minimizing transportation costs. Our model incorporates real-time data and route optimization strategies that consider traffic conditions based on freight time and route diversions for expedited deliveries. It aims to devise an optimal transportation schedule that minimizes fuel, operational, and maintenance costs while ensuring efficient delivery of tomato paste. By applying this model to a real-world case study, we estimate a significant 27.59% reduction in transportation costs, dropping them from GH¢20,270 ($1,638.91) to GH¢14,676 ($1,186.61) on average. This highlights the effectiveness of our strategy in lowering costs while maintaining smooth and efficient deliveries.

Green logistics optimization for coal supply in a power plant connecting maritime port

This study addresses the coal transportation and flow distribution challenges within a “port-before-plant” type thermal power plant, with a strong emphasis on green logistics principles. Despite significant advancements in clean energy, coal remains a dominant energy source in China, necessitating optimization in its usage and management to mitigate environmental impacts. This paper introduces an integrated optimization model grounded in green logistics and employs an Improved Particle Swarm Optimization (IPSO) algorithm to efficiently manage the coal supply chain from unloading at ports to loading into generators. The model incorporates new parameters and constraints that not only reflect the operational realities of coal logistics but also emphasize minimizing carbon emissions and energy consumption. Numerical experiments demonstrate the algorithm’s superior performance compared to traditional solvers like Gurobi, particularly in handling large-scale instances. Sensitivity analysis reveals the importance of prioritizing efficient and environmentally sustainable unloading and loading equipment, suggesting strategies for optimizing green coal transportation routes. Overall, this research provides valuable insights for policymakers and industry operators to enhance operational efficiency while ensuring environmental sustainability through the implementation of green logistics

Logistics Performance Index-driven in operational planning for logistics companies: A smart transportation approach

The responsibility of Logistics Companies (LCs) extends beyond simply handling logistical processes and choosing transportation routes; they focus on being competitive and efficient in their operations. In multiple origins and destinations networks, LCs attempt to select the most optimal routes from each point. To address this challenge and reduce the time needed to identify potential routes, a novel method has been developed in this research for LCs. In operational planning, additional factors need to be considered to increase efficiency, customer satisfaction, and competitiveness. To confront this issue, the study proposes a novel model and contributions, inspired by the LPI, to apply the transformative impact of smart ports, the integration of smart containers, the choices of clearing and forwarding agents and port operators, and time uncertainty in the multi-objective framework. Specifically, the mathematical model's objectives include cost, time, customer satisfaction, and environmental impact in the periodic multimodal network. An innovative customer satisfaction function is introduced by integrating the selection of smart containers and smart ports, emphasizing their impact on enhancing customer satisfaction, while addressing time uncertainty through a chance-constrained approach and coefficients of variation. The model is solved using goal programming, and the results show that smart ports and smart containers can majorly affect transportation time and customer satisfaction. Furthermore, comparing the results to other studies demonstrates its superiority in decision-making for LCs, particularly by including time uncertainty and the role of clearing and forwarding agents and port operators. Therefore, the model holds practical significance in lowering costs, enhancing customer satisfaction, and facilitating smart international logistics. This research offers insights that are not only useful for the LCs but also for other stakeholders in the transport industry.

Unveiling risk factors influencing the selection of the Northern Sea Route: A conjoint analysis approach for Japanese shippers

This paper explores how Japanese corporations assess risks associated with using the Northern Sea Route (NSR) as a new maritime transport route in Asia and identifies measures to promote its usage. We address two key questions: (1) What risks influence shippers' route choices in maritime transport? (2) Does risk evaluation vary by cargo type and company size? Using choice-based conjoint analysis and a conditional logit model with online survey data, we identified five main risk factors influencing shippers: route unavailability rate, delay probability, piracy probability, transport costs, and war conditions, ranked in importance from highest to lowest as war conditions, delay probability, transport costs, route unavailability rate, and piracy probability. Additionally, the risk factors significantly influencing route selection were found to be robust, showing minimal variation across company size, type of trade (import/export), and the nature of the goods transported. Our findings suggest several policy implications. Diplomatic efforts are crucial for safe vessel navigation on the NSR. Government initiatives should focus on reducing transport delays through technology investments and implementing strategies to decrease NSR delay rates without increasing shippers' costs. Additionally, emphasizing the lower piracy risks on the NSR compared to Suez Canal Route could boost its attractiveness.

Ammonia Sprays for Combustion: A Review

Ammonia is a globally transported chemical used for a variety of applications, most notably, the production of fertiliser. Over the past decade, attention has been afforded to the use of ammonia as an energy carrier, coupling global supply of renewable energy to demand regions. Ammonia’s advantages as an energy carrier include its ease of liquefaction and established international transportation routes; overcoming its low reactivity, excessive production of nitrogen oxides and its toxicity remain as challenges. For energy applications, fuel delivery is a critical aspect of effective combustion in boilers, burners and engines. Due to its adaptable phase change characteristics, ammonia fuel may be injected as a liquid or vapour, each with respective advantages or disadvantages. The focus of this review concerns the characterisation of liquid ammonia fuel injection for combustion, including recent research findings from experimental and simulation studies. Liquid ammonia injection can result in the highly dynamic so-called ‘flashing’ or ‘flash boiling’ phenomena. Research findings have been drawn from other related applications such as accidental hazardous releases. Bespoke optical experimental rigs together with diagnostic techniques and two-phase computational fluid dynamics (CFD) simulations have enabled studies of the flashing jets under various initial or final conditions, with recent work also examining ammonia spray combustion. The review concludes with an insight into future trends and requirements for liquid ammonia combustion. Reciprocating engines for marine propulsion are cited as potential early adopters of ammonia energy.

Pickup and Delivery Vehicle Routing Problem with Hybrid Missions and Third-Party Vehicle Rentals: A Case Study of Animal Feed Industry

The vehicle routing problem for small and medium-sized animal feed manufacturers is a complex challenge because each transport route can vary significantly between delivering products to customers and picking up raw materials to return to the factory. Currently, businesses lack well-planned management for utilizing a limited number of trucks. As a result, they often incur additional costs by relying on third-party vehicle rentals. This research aims to reduce transportation costs for small and medium-sized animal feed manufacturers by solving a formulated mixed-integer linear programming (MILP) model, using experimental data that accurately reflects the actual problems faced. The MILP model results help reduce transportation costs by up to 18.5% compared to current practices by optimizing route sequences and incorporating the use of third-party vehicle rentals. This also serves as a guideline for reducing transportation costs in other similar industries.

Protein Medium Facilitates Electron Transfer in Photosynthetic Heliobacterial Reaction Center.

This computational study addresses the question of how large membrane-bound proteins of electron transport chains facilitate fast vector-based charge transport. We study electron transfer reactions following ultrafast initial charge separation induced by absorption of light by P800 primary pair and leading to the electron localization at the A0 cofactor. Two subsequent, much slower reactions, electron transfer to the iron-sulfur cluster Fx and reduction of the menaquinone (MQ) cofactor, are studied by combining molecular dynamics simulations, electronic structure calculations, and theoretical modeling. The low value of the electronic coupling between A0 and Fx brings this reaction to the microsecond time scale even at the zero activation barrier. In contrast, A0-MQ electron transfer occurs on a subnanosecond time scale and might become the preferred route for charge transport. We elucidate mechanistic properties of the protein medium allowing fast, vectorial charge transfer. The electric field is high and inhomogeneous inside the protein and is coupled to high polarizabilities of cofactors to significantly lower the reaction barrier. The A0-MQ separation puts this reaction at the edge between the plateau characterizing the reaction dynamical control and exponential falloff due to electronic tunneling. A strong separation in relaxation times of the medium dynamics for the forward and backward reactions promotes vectorial charge transfer.

TRANSPORT SYSTEMS AS ESSENTIAL PART OF GOVERNMENT POLICY

The paper discusses the significance of transport systems as a crucial compone nt of public policy in the majority of countries worldwide. Transport policy aims to achieve various objectives, including promoting economic growth, enhancing accessibility, improving safety, reducing environmental impacts, and promoting social equity. A significant aspect is the substantial funding required to construct and maintain transportation infrastructure, includin g roadways, railway tracks, airports, harbors, and publictransportation systems.Special emphasis is placed on international transport corridors such as the Trans-Caspia n International Transportation Route, which complements the Chinese Belt and Road initiative and presents new opportunities for trade between China and European nations. The paper emphasize s the importance of collaboration between different levels of government and internationa l stakeholders to address cross-border transportation challenges and harmonize regulations. The authors also discuss the role of the Shanghai Cooperation Organization in the development of regional transportation infrastructure and logistics. They highlight key issues such as outdate d infrastructure, high transportation costs, low efficiency, and the need for increased transit capacity. Additionally, they discuss other international initiatives, such as the Eurasian Economic Union and Central Asian Regional Economic Cooperation that aim to improve regional connectivity and facilitate trade.In conclusion, they emphasize the importance of strategic planning and investment in transportation systems to promote sustainable economic growth and enhance transportation links between Central Asian countries and other regions of the world.

Optimizing Road–Rail Multimodal Transport Schedule for Emergency Response with Congestion and Transshipment Sequence Selection

The current literature mainly uses hub capacity or transport route selection to manage the congestion of emergency multimodal transport and pays less attention to transshipment scheduling. This paper proposes an integrated optimization problem of transport routes and transshipment sequences (ITRTSP) and constructs a hybrid flow shop scheduling model to describe it. Based on this model, a recursive method is proposed to calculate the minimum waiting times that cargoes consume in queues at hubs, given the transport routes and transshipment sequences. Furthermore, a memetic algorithm is designed with route selection as the outer layer and transshipment sequence selection as the inner layer for solving ITRTSP. Compared with existing achievements, the model and algorithms can quantify the dependency between transshipment sequence selection and emergency transport time in multimodal transport network settings. The model and algorithms are applied to solve some real-scale examples and compared with the first-come-first-served (FCFS) rule commonly used in the current literature. The results indicate that the makespan is reduced by up to approximately 4.2%, saving 33.68 h. These findings demonstrate that even with given hub capacities and transport routes, congestion can still be managed and the schedule optimized through transshipment scheduling, further improving emergency transport efficiency.

Learning-based Pareto-optimum routing of ships incorporating uncertain meteorological and oceanographic forecasts

In modern shipping logistics, multi-objective ship route planning has attracted considerable attention in both academia and industry, with a primary focus on energy conservation and emission reduction. The core challenges in this field involve determining the optimal route and sailing speed for a given voyage under complex and variable meteorological and oceanographic conditions. Typically, the objectives revolve around optimizing fuel consumption, carbon emissions, duration time, energy efficiency, and other relevant factors. However, in the multi-objective route planning problem involving variable routes and speeds, the extensive solution space contains a substantial number of unevenly distributed feasible samples. Traditional heuristic optimization techniques, such as multi-objective evolutionary algorithms, which serve as the core component of optimization programs, suffer from inefficiencies in exploring the solution space. Consequently, these algorithms may tend to converge toward local optima during population iteration, resulting in a solution set characterized by sub-optimal convergence and limited diversity. This ultimately undermines the potential benefits of routing optimization. To address such challenging problem in route planning tasks, we propose a self-adaptive intelligent learning network aiming at capturing the potential evolutionary characteristics during population iteration, in order to achieve high-efficiency directed optimization of individuals. Additionally, an uncertainty-driven module is developed by incorporating ensemble forecasts of meteorological and oceanographic variables to form the Pareto frontier with more reliable solutions. Finally, the overall framework of the proposed learning-based multi-objective evolutionary algorithm is meticulously designed and validated through comprehensive analyses. Optimization results demonstrate its superiority in generating routing plans that effectively minimize costs, reduce emissions, and mitigate risks.

The Northern Sea Route as a shipping route and an investment transport megaproject

The Northern Sea Route as a shipping route and an investment transport megaproject

Multi-objective intermodal transportation planning with real-life application

ABSTRACT This paper explores the optimal route selection problem within an intermodal transportation network that comprises road, rail, sea, and inland waterways. Initially, we propose a multi-objective integer linear programming model that aims to minimize total cost, transport time, and carbon emissions. Subsequently, we apply two multi-objective programming approaches: Fuzzy Goal Programming and Compromise Programming. These approaches are implemented in a real-life case study, aiming to determine the optimal intermodal transportation route from Türkiye (Denizli) to Germany (Duisburg). Four out of the six methods suggest the same road and maritime pathway. Furthermore, this model has the potential to aid decision-makers in selecting routes based on customer requirements and serve as a valuable tool for policymakers in establishing an efficient intermodal transport network.

Projected changes to Arctic shipping routes after stratospheric aerosol deployment in the ARISE-SAI scenarios

Projected changes to Arctic shipping routes after stratospheric aerosol deployment in the ARISE-SAI scenarios

Economic and environmental benefits of optimized waste transportation routes in Khulna

Urban transportation is a big source of pollution and greenhouse gas emissions. Khulna City handles the waste of 1.23 million people daily, using Heavy Duty Diesel Vehicles (HDDV) that release major environmental pollutants. The current waste transportation routes lack proper planning, leading to extra fuel consumption and pollutant emissions. This study aims to reduce fuel consumption and socio-environmental costs by improving waste transportation routes. Present collection routes, collecting points, and the speed of 30 waste collector vehicles were tracked using GPS devices for ten months between July 2023 and April 2024. The network dataset was modified according to four types of waste collector vehicles. ArcGIS Pro was used to find the optimal paths and their lengths. Fuel usage and prices were estimated using simple fuel consumption rate calculations. The lengths of current and optimal routes were used in COPERT to measure pollutant emissions. The results show that improved routes lower fuel consumption costs by approximately USD 55.39 per day and CO2 emissions by about 38.242 tonnes per year. Reductions in CO, CH4, NMVOC, NO2, PM10, and PM2.5 were roughly 9.3%, 9.53%, 9.21%, 9.29%, 9.35%, and 9.3%, respectively. This study demonstrates a comprehensive approach for improving solid waste management covering both financial efficiency and environmental effects.

Connectivity Analysis of Non-Container Ports at PT Pelindo Multi Terminal Using SNA and GIS / Connectivity Analysis of Non-Container Ports at PT Pelindo Multi Terminal Using SNA and GIS

This study aims to analyze the connectivity of non-containerized ports, particularly in the sub-commodity of vegetable/animal oils and fats, within the operational area of PT Pelindo Multi Terminal. Using Social Network Analysis (SNA) and Geographic Information System (GIS) techniques, this study builds a network based on nodes (ports) and assesses inter-node connections with metrics such as degree centrality, betweenness centrality, and hub index. Gephi application was used for the calculation of network metrics, while QGIS visualized the shipping routes. The research revealed that high traffic ports such as Belawan, Dumai, and Tanjung Priok play a crucial role in the network. The findings support recommendations for infrastructure development and implementation of an integrated Terminal Operations System (TOS) to improve logistics efficiency.

Enhancement of tunneling electroresistance in metal/two-dimensional ferroelectric tunnel junctions: route for polarization-modulated interface transport

In fabricating ferroelectric tunnel junction (FTJ) devices, it is essential to employ low-resistance metals as electrodes interfacing with two-dimensional (2D) ferroelectric materials. For FTJs with a top contact configuration, two interfaces for charge transport are present, namely the vertical interface between the metal electrode and the 2D ferroelectric material, and the lateral interface between the electrode and the central scattering region. These interfaces significantly influence the tunneling electroresistance (TER) of FTJs. However, there exists a notable deficiency in comprehension concerning the physics of charge transport at the interface. In this work, we explore the interface transport properties in FTJs featuring a top contact configuration between metal and the typicalα-In2Se3monolayer. By employing the non-equilibrium Green's function method, we observe a TER ratio of1.15×105% for the Pd top contact interfacing with anα-In2Se3monolayer. The significant TER effect is attributed to polarization-controlled interface transport, which is further elucidated through an analysis of the transport mechanisms influenced by the out-of-plane polarization ofα-In2Se3at the vertical interface and the in-plane polarization at the lateral interface. This investigation of the fundamental physical mechanisms of polarization-controlled interface transport demonstrates significant potential for enhancing non-volatile memory devices.

Selection and planning of hazardous materials transportation routes based on explosion accident risks

The explosiveness of hazardous materials determines that once an accident occurs during transportation, severe damage will be imposed on surrounding targets. However, the increasing complexity of the road network significantly augments the risk of damage occurrence. To maintain low accident risks in transporting hazardous materials, this study proposes a method for selection and planning of transportation routes based on explosion accident risks. Firstly, the road network topology is associated with vehicle status to establish a relationship between the accident probability and speed gradient. Meanwhile, an independent grid-based approach is used to perform multi-level damage quantification on various objects. Secondly, route risks are quantitatively characterized, and a comparison system for multiple risk metrics is established. Based on search algorithms, route planning and risk ranking are achieved. Finally, the method is validated. It has been confirmed that this method can quickly and accurately obtain route ranking and risk distribution. The results offer practical guidance for relevant organizations to improve risk management and strategy development, and provide reference for future research.

Artificial Intelligence in Maritime Transportation: A Comprehensive Review of Safety and Risk Management Applications

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä’s Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI’s potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations.

Accumulation modes and effects of differentially charged polystyrene nano/microplastics in water spinach (Ipomoea aquatica F.)

There is widespread concern about the risk of nano/microplastics (N/MPs) entering the food chain through higher plants. However, the primary factors that influence the absorption of N/MPs by higher plants remain largely unclear. This study examined the impact of Europium-doped N/MPs with different particle sizes and surface charges by water spinach (Ipomoea aquatica F.) to address this knowledge gap. N/MPs were visualized and quantitatively analyzed using laser confocal microscopy, scanning electron microscopy, and inductively coupled plasma-mass spectrometry. N/MPs with different surface charges were absorbed by the roots, with the apoplastic pathway as the major route of transport. After 28 days of exposure to 50 mg L−1 N/MPs, N/MPs-COOH caused the highest levels of oxidative stress and damage to the roots. The plants accumulated NPs-COOH the most (average 1640.16 mg L−1), while they accumulated NPs-NH2 the least (average 253.70 mg L−1). Particle size was the main factor influencing the translocation of N/MPs from the root to the stem, while the Zeta potential mainly influenced particle entry into the roots from the hydroponic solution as well as stem-to-leaf translocation. Different charged N/MPs induced osmotic stress in the roots. A small amount of N/MPs in the leaves significantly stimulated the production of chlorophyll, while excessive N/MPs significantly reduced its content. These results provide new insights into the mechanism of interaction between N/MPs and plants.

Metagenomic survey reveals hydrocarbon biodegradation potential of Canadian high Arctic beaches

BackgroundDecreasing sea ice coverage across the Arctic Ocean due to climate change is expected to increase shipping activity through previously inaccessible shipping routes, including the Northwest Passage (NWP). Changing weather conditions typically encountered in the Arctic will still pose a risk for ships which could lead to an accident and the uncontrolled release of hydrocarbons onto NWP shorelines. We performed a metagenomic survey to characterize the microbial communities of various NWP shorelines and to determine whether there is a metabolic potential for hydrocarbon degradation in these microbiomes.ResultsWe observed taxonomic and functional gene evidence supporting the potential of NWP beach microbes to degrade various types of hydrocarbons. The metagenomic and metagenome-assembled genome (MAG) taxonomy showed that known hydrocarbon-degrading taxa are present in these beaches. Additionally, we detected the presence of biomarker genes of aerobic and anaerobic degradation pathways of alkane and aromatic hydrocarbons along with complete degradation pathways for aerobic alkane degradation. Alkane degradation genes were present in all samples and were also more abundant (33.8 ± 34.5 hits per million genes, HPM) than their aromatic hydrocarbon counterparts (11.7 ± 12.3 HPM). Due to the ubiquity of MAGs from the genus Rhodococcus (23.8% of the MAGs), we compared our MAGs with Rhodococcus genomes from NWP isolates obtained using hydrocarbons as the carbon source to corroborate our results and to develop a pangenome of Arctic Rhodococcus. Our analysis revealed that the biodegradation of alkanes is part of the core pangenome of this genus. We also detected nitrogen and sulfur pathways as additional energy sources and electron donors as well as carbon pathways providing alternative carbon sources. These pathways occur in the absence of hydrocarbons allowing microbes to survive in these nutrient-poor beaches.ConclusionsOur metagenomic analyses detected the genetic potential for hydrocarbon biodegradation in these NWP shoreline microbiomes. Alkane metabolism was the most prevalent type of hydrocarbon degradation observed in these tidal beach ecosystems. Our results indicate that bioremediation could be used as a cleanup strategy, but the addition of adequate amounts of N and P fertilizers, should be considered to help bacteria overcome the oligotrophic nature of NWP shorelines.