Optimal Transportation Routes Research Articles

Evaluating Strategies for Cost Reduction in Supply Chain Management Relating to Exports and Imports

This study explores different approaches to supply chain management cost reduction, with an emphasis on imports and exports. As the economy becomes more globalized, controlling supply chain expenses is essential to preserving profitability and competitive advantage. According to the study, procurement, transportation, inventory control, and technology integration are important areas where cost reductions can be made. Highlighting the main cost drivers in international trade, the paper starts with a review of the body of research on supply chain cost management. After that, it looks at particular tactics like utilizing economies of scale, transport route optimization, and shipment consolidation. The potential of technology to simplify processes and cut expenses is examined, including the use of automation, supply chain management software, and block chain technology for transparency. For an organization to keep a competitive edge in a globalized economy, effective supply chain management is essential. Exports and imports are the main focus of the current research paper, which provides a thorough assessment of various cost-cutting strategies in supply chain management. Transportation optimization, inventory management, supplier collaboration, technology integration, process improvement, regulatory compliance, sustainability, risk management, and continuous improvement are all topics covered in this study in an effort to give a comprehensive overview of effective cost-reduction strategies.

Decision-making optimization for post-disaster restoration of multimodal transport networks in terms of resilience

IntroductionPublic health emergencies can have a ripple effect on the resilience of multimodal transport network, which will lead to problems such as route disruptions or blockages, route selection change and information transmission delay spreading to the whole network, further hindering the transportation planning and operational efficiency of the network. MethodsThis study constructs a multimodal transport route optimization model under uncertainty with the objective of the sum of transportation cost, transshipment cost, penalty cost and carbon emission cost. To enhance the computational efficiency of the model, a novel invasive weed optimization with memory and encoding value clustering capabilities is proposed. In addition, by fusing the Q-learning algorithm in reinforcement learning with the novel invasive weed algorithm, the action-value function table obtained from the training facilitates the selection of optimal routes. Based on empirical data, explore the sensitivity analysis of node disruptions, time windows, and fuzzy demand on route decision-making under public health emergencies. ResultsThe transport network is affected by public health emergencies, which makes the optimal route deviate from the expected goal, resulting in an increase in the total cost. The proportion of total cost is determined by the position of nodes in the network, with critical nodes suffering more losses than ordinary nodes. Reasonable setting of time windows and fuzzy demand intervals is an effective way to improve the resilience and transportation efficiency of multimodal transport network. ConclusionsThis study provides more applicable decision-making references for enterprises to prevent the risk of supply chain disruptions caused by public health emergencies.

Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM

Supply chain management is changing rapidly due to increasing complexity, uncertain demand, and the requirement for sustainable methods. Advanced technologies like Bidirectional Long Short-Term Memory networks (BiLSTM) and Convolutional Neural Networks (CNNs) can enhance supply chain processes. This paper proposes integrating CNNs and BiLSTM models to improve supply chain efficiency and sustainability. The proposed model employs CNNs to optimize resource allocation, uncover trends, and evaluate supply chain spatial linkages. Using BiLSTM models to capture temporal correlations allows accurate demand forecasting and proactive decision-making. Combining these models explains supply chain dynamics. CNNs and BiLSTM models' adaptive learning and real-time monitoring boost efficiency by responding quickly to changing situations. Predictive analytics optimizes inventory, lowers stock outs, and cuts lead times. Sustainability factors include transportation route optimization, carbon footprint minimization, and intelligent green-sourcing decision assistance. The proposed Hybrid Model achieved 94.65 % Specificity, 96.57 % Accuracy, 95.67 % Sensitivity and 0.85 % MCC. The result analysis demonstrates that the proposed model significantly improved the accuracy level. This research sheds light on supply chain difficulties from all sides. CNNs and BiLSTM models can boost operational efficiency and link supply chain practices with sustainability goals to produce a more sustainable global supply network.

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.

Opportunity Analysis of Phosphorus Recovery from Municipal Wastewater for Cropland Based on the Simulated Vehicle Transport Distance in the Yangtze River Delta, China.

With the rapid depletion of phosphate rocks and increasing agricultural demand, establishing a phosphorus (P) flow "loop" rather than a one-way trajectory between cropland and urban areas was imperative. Recovering P from municipal wastewater stood as a viable strategy to mitigate reliance on traditional P-containing chemical fertilizer. This study analyzed the intricate relationships between the potentials of P recovery from municipal wastewater and the P demand of croplands in the populated Yangtze River Delta (YRD), China. An indicator of the P vehicle transport distance was constructed and calculated to estimate the potential to recover and reuse P in agriculture, applying the simulated annealing (SA) algorithm and road networks obtained from OpenStreetMap (OSM). The results indicated that, on a regional scale, recovered P from municipal wastewater could fulfill 14.0% of the cropland P demands in the YRD, with a median P vehicle transport distance of 3.1 km/Mg of P. Notably, the P vehicle transport distance varied largely depending upon the cropland distributions, road density, and P recovery potential from municipal wastewater. The novel methodology developed here determined the optimal transportation routes for P recovery from wastewater treatment plants (WWTPs) to cropland, which played a crucial role in refining the wastewater management strategies aligned with the United Nations Sustainable Development Goals.

The practice of customs logistics in the export and import of petroleum products

In Russia, oil production and refining are the leading industries. The customs logistics of the export and import of petroleum products has its own characteristics and reserves for improvement. In order to comply with the rule of law and increase the efficiency of foreign trade activities in relation to petroleum products, it is necessary to strictly control all stages of the logistics process. Effective customs logistics implies an integrated approach to solving problems from the perspective of identifying costs and reducing them in conjunction with all logistics links – from supply to distribution. In the course of the study, the specifics of the approach to customs operations in relation to petroleum products as an object of export and import logistics flows within the framework of their movement by various modes of transport were studied. The importance of the development of customs logistics for the development of foreign economic activity and the oil industry is also determined. The result of the study was the directions proposed by the authors to improve the efficiency of customs logistics in the export and import of petroleum products. They include: reduction of losses of oil and petroleum products during transportation; organization of the logistics process, taking into account the analysis of the best practices of oil processing enterprises; improvement of the efficiency of inventory management; optimization of transport routes and choice of vehicles; introduction of modern information technologies into logistics processes.

Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPS.

This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone's GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model's ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model's capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin-destination (OD) matrix to better understand how people move within the city.

Use of criteria for process optimization elevator complex routing

The goal of the work. The purpose of this work is to describe the optimality criteria for determining the direction of solving the problem of optimizing transport and technological routes of grain products at the elevator complex. Research methods. An analytical research method was applied to determine the technological characteristics of the elevator complex line and the dependence of the quality of transported products on the constructed transportation route. A mathematical method is applied to describe the constituent elements of the optimization criteria. Obtained results. One of the directions for increasing and improving the technological process at the elevator was determined, by improving the control algorithms in the direction of optimizing the technological processes of the elevator according to many criteria. According to optimization criteria, to ensure optimal transportation conditions, minimum losses, minimum transportation time, minimum energy consumption, and minimum length of the laid transportation path should be used, therefore, a description of the main criteria that can primarily affect the efficiency of the elevator complex was performed. Scientific news. The direction of solving the problem of optimization of transport and technological routes is determined. Analytical derivation of the minimum energy consumption criterion and the minimum grain fight criterion. Practical value. Optimization of the elevator complex to ensure variable transportation of grain masses, which will affect the productivity and quality of the technological process of the elevator complex.

A System of IoT Devices to Prevent the Underloading And Overloading of Railway Wagons

Coal transportation poses substantial obstacles for corporations such as Coal India Limited (CIL), including concerns with route selection, rehandling, precise weighing, demurrage costs, and underloading fines. CIL's failure to adopt optimal transportation routes has resulted in needless costs. Coal rehandling at depots has proven expensive, and there are disparities in coal transportation rates. The lack of weighbridges makes accurate weighing difficult, resulting in probable pilferage. Demurrage costs have been a financial hardship, and railway carriages underloaded have resulted in fines. To address these challenges, the proposed IoT solution seeks to reduce financial losses, improve operational efficiency, increase accountability, ensure regulatory compliance, provide real-time monitoring, automate alerts and penalties, improve communication, improve data security, provide multilingual support, and, ultimately, increase revenue. This idea aims to change coal transportation by increasing efficiency, transparency, and cost-effectiveness for CIL and the government. However, it incurs initial expenses and complications associated with IoT deployment, maintenance, and regulatory compliance. Keywords: CIL, IOT, efficiency, pilferage.

Resource allocation and route planning under the collection price uncertainty for the biomass supply chain

The escalating severity of global warming has drawn worldwide attention to ecological problems. Nevertheless, with the introduction of environmental policies, biomass resources have been effectively developed as a renewable energy source. This paper investigates an advanced biomass supply chain design wherein biomass resources are initially converted into bio-oil through widely distributed fast pyrolysis facilities, and are subsequently transported to a centralised biorefinery for further refining into biofuels. This novel biomass supply chain addressed three key issues: (1) The number of fast pyrolysis facilities, (2) The allocation of resources, and (3) The routes of resources transport. In respect of these problems, a two-stage stochastic mixed integer programming model is established to minimise the total cost of the biomass supply chain considering the uncertainty collection price of fast pyrolysis facilities. A hybrid simulated annealing algorithm which incorporates the sample average approximation method is proposed to solve the stochastic model and is effectiveness for large-scale examples. Finally, a sensitivity analysis is performed using the proposed algorithm and the results show that the proposed stochastic model outperforms the deterministic model under uncertain collection price. The model allows optimising the biomass supply chain economic performances and minimise financial risk on investment by determining the fast pyrolysis facility locations, reasonable resource allocation and optimal transport routes under uncertain collection price.

Exploring logistics transport route optimization: An algorithmic study based on RFID technology

With the rapid growth of e-commerce, logistics companies face challenges in efficient routing and scheduling to meet dynamic delivery demands. This paper proposes a novel logistics scheduling model to optimize vehicle routing using Radio Frequency Identification (RFID) technology. A vehicle scheduling model is developed. The random customer demand and service time are solved using an adaptive taboo search algorithm combined with a nearest neighbor algorithm. Comparative experiments demonstrate the performance of the improved method in completing tasks and reducing queueing time compared to other methods. A case study of route optimization for a logistics company shows the model can recommend optimized routes that reduce total transportation cost by over 25% compared to using RFID alone. The results highlight the potential of the proposed technique to enhance logistics efficiency. Limitations and future work are discussed.

Route Optimization of Hazardous Material (Hazmat) Transportation with Risk Equity and Time-Varying Risk

This paper strives for route optimization of hazardous material (hazmat) for given probabilities and consequences. We aim to not only develop a model to determine the route with the least total risk and cost, but also pursue risk equity from three perspectives of government, enterprises, and society in the route optimization. Specifically, this paper reduces the differences of population exposure risk among road segments by using the threshold setting method and decreases the differences of environmental risk by minimizing the risk compensation cost caused by the additional environmental risk exceeding its average for each road segment. In addition, this paper analyzes the impact of the time-varying characteristics of risk and risk equity and proposes a multi-objective route optimization model which considers the minimization of total risk, cost, and risk differences under time-varying conditions. Finally, the ε-constraint method and an improved genetic algorithm are used to optimize the model, which is applied to a case study involving hazardous materials transportation route optimization in Shanghai, China. The resulting analyses indicate that risk equity can be improved by setting a proper threshold to acquire feasible paths and by minimizing the compensation cost to control the differences of environmental risk. Considering variance of time in the route optimization may lead to a route with lower risk and better risk equity.

Multimodal network path optimization based on a two‐stage algorithm in the perspective of sustainable transport development

AbstractThe environmental issues brought on by carbon emissions from transport have risen to prominence in recent years. More and more academics are using the multi‐objective path optimization method to solve the multimodal optimization problem from the standpoint of sustainable development in order to address the environmental issues brought on by the transport process. The research proposes a two‐stage method to handle multi‐objective optimization convergence and simplify multimodal transport path optimization. In the first stage, a fuzzy C clustering model is established, and based on the clustering results, the multimodal transport network nodes are identified. In the second stage, a multimodal transport multi‐objective path optimization model is established, and the optimal path is solved using a genetic algorithm. The research method was applied in the Bohai Rim region. Results indicated that the fuzzy C‐clustering method and the genetic method were able to select the optimal node city, thus solving the actual site selection problem of multimodal transportation networks. Using the FCM model, the 86 city nodes were categorized into four types, leading to the establishment of the most proficient multimodal transportation network in the Bohai Rim region. Using a genetic algorithm for optimization, a stable state is reached after 25 iterations. In the validation experiment on path optimization, the cost was reduced by 47.12% compared to the minimum single objective time, and transportation carbon emissions saw a reduction of 28.23%. Similarly, compared to the lowest target for transportation carbon emissions, the cost was reduced by 39.48% and the time was reduced by 38.12%. Compared to the lowest target for transportation carbon emissions, the time was reduced by 32.02% and the carbon emissions were reduced by 19.23%. Notably, the transportation multi‐objective path optimization model showed significant improvement compared to the single‐target model. The research method has been proven to be superior, and can offer the most optimal transportation route guidance for participants in multimodal transportation. Furthermore, it can effectively tackle the issue of node selection convergence and multi‐objective optimization, while also serving as a valuable source of data to support the theoretical advancement of multimodal transportation network path optimization.

Route Optimization for Hazardous Chemicals Transportation under Time-Varying Conditions

Since accidents of hazardous chemicals transportation will cause serious loss to the surrounding environment and lives and properties, this paper studies the transportation route optimization problem of hazardous chemicals under dynamic time-varying conditions. Combined with the goal of green sustainable development, a multiobjective nonlinear optimization model is constructed to minimize the transportation risk, transportation cost, and carbon emissions generated in the transportation. The model is solved by the improved Fast Non-Dominated Sorting Genetic Algorithm with Elite Strategy (NSGA-II) algorithm. The effectiveness of the model and the algorithm are tested on the Sioux Falls network. The experimental results show that under time-varying conditions, a vehicle’s departure at different times will generate different transportation costs and risks. Therefore, enterprises need to rationally arrange the departure time of vehicles according to the time windows of customer nodes and road conditions. In additio, from the relationship between the optimization objectives, in order to achieve green, sustainable and low-risk transportation, enterprises should first reduce their transportation costs.

Optimization of logistics intermodal scheduling model based on counting and efficiency in the context of low carbon economy

Abstract It is of great significance to study how to reasonably allocate materials and optimize distribution routes in the context of a low-carbon economy to improve the efficiency of logistics work. This paper constructs a low-carbon economy logistics intermodal dispatch optimization model on the basis of the counting and efficiency model. It carries out the optimization of transport routes under the consideration of logistics transportation externality cost. Its total generalized transportation cost is minimized as the goal of optimization modeling, establishing the cargo transportation operation flow, and outputting the logistics flow distribution and path optimization scheme under the whole integrated transportation network. The multimodal transportation optimization model is solved by using triangular fuzzy theory to select the optimal scheduling scheme, and simulation analysis is carried out using an earthquake-stricken area as an example. The results show that the comprehensive satisfaction degree of the disaster victims of the rescue plan obtained by the proposed intermodal logistics scheduling optimization model is 0.9022. At the same time, the value of GAP obtained by this paper’s method for optimization in a multi-scenario deterministic environment is smaller than that of the traditional method by 0.002~0.068, and the running time of this paper’s method is less than that of the conventional algorithm by 81.9~1400.5 s. The study verifies the validity of the proposed intermodal logistics scheduling model and is useful for improving logistics dispatching and path optimization. The study confirms the effectiveness of the model proposed in this paper, which has some applications and promotion potential for improving logistics deployment and other auxiliary decision-making.

Managerial decisions using information platforms for organizing the provision of logistics and transport services

The article examines managerial decision types that can be made with the help of functional capabilities of information platforms for organizing the provision of logistics and transport services. The classification features of managerial decisions in the transport and logistics sphere have been singled out, including methods of justification and making decisions, areas of coverage and application, duration, hierarchy level, methods of regulation and resolution, decisions made by using IT-technologies and information and logistics platforms, tools. The types of managerial decisions have been characterized (including decisions based on subjective conclusions and judgments; rational; economically justified; individual; collective; collegial; general; partial; decisions in the organizational-administrative; organizational-coordinating; informational-analytical; and regulatory spheres; strategic decisions; operational decisions, decisions made by top, middle and lower management managers; contour; structured; algorithmic; typical; non-typical), including those made by using information and logistics platforms, tools and services («DELLA Truck Transport»; «Lardi Trans»; «MaponPro», electronic document management system «M.E.Doc.»; «BAS Accounting Prof» and «BAS Accounting Corp» software; information service of electronic reporting and document management «Liga: REPORT». The existing types of information and logistics platforms («DELLA Truck Transport», «Lardi Trans», «MaponPro») have been studied, and the possibilities and advantages of their use for decision-making in the organization of the provision of logistics and transport services have been determined. It has been established that the use of such information services is an effective tool in the process of making and implementing a wide range of managerial decisions regarding the optimization of logistics and transport processes and routes, improving the quality of services and safety during transportation, and reducing costs when organizing the provision of logistics and transport services.

Investigation of Simulated Annealing and Ant Colony Optimization to Solve Delivery Routing Problem in Surabaya, Indonesia

This research emphasizes the importance of efficient goods delivery services because it is essential in maintaining the sustainable supply chain's continuity and applying optimization techniques, specifically the Traveling Salesman Problem (TSP), in determining optimal transportation routes. It compares the effectiveness of the Simulated Annealing (SA) and Ant Colony Optimization (ACO) in finding the shortest routes to minimize operational costs. This study compares the performance of the SA and ACO algorithms. It shows that both provide improved routes compared to the existing routes, with the SA achieving shorter distances, 17.4% smaller than the existing route.

Modelling of optimal transportation route selection based on artificial bee colony algorithm

Modelling of optimal transportation route selection based on artificial bee colony algorithm

Transportation Route Optimization of Municipal Solid Waste based on Improved Ant Colony Algorithm in Internet of Vehicles

Transportation Route Optimization of Municipal Solid Waste based on Improved Ant Colony Algorithm in Internet of Vehicles

MODEL OF TRANSPORT SAFETY ASSESSMENT IN MULTIMODAL TRANSPORTATION SYSTEMS

In this paper methods, criteria and models of safety assessment of multimodal transportation systems are investigated. Today, new types of transportation systems are formed – multimodal transportation systems, which may be defined as the result of the synthesis of several transport systems, where different modes of transport are effectively interact. The need for such systems was formed due to the peculiarities of each mode of transport, its technological and technical characteristics, which restrain the ability to compete and promote transport interaction. In this context, the problem of transport safety assess of multimodal freight transportations becomes highly important. An analysis of modern scientific researches on topic of transport safety is conducted. Unfortunately, the considered approaches to transport safety have a set of disadvantages if the complex systems are considered in form of, for example, combined, multimodal or intermodal transportations: the problem of local optimum; the problem of safety management in case of transport company is not the actual infrastructure owner; the problem of unifying estimates for several modes of transport, and so on. The system’s safety depends on its integrity and sustainable development, which is directly dependent on the objective conditions of its formation, development and operation. At the same time, the state of safety is directly dependent on subjective factors, the purpose of which is to ensure security, i.e., to preserve the integrity and maintain sustainable development and optimal functioning of the system. Thus, there is a conditional field of protection of transport system objects, which provides counteraction to a set of existing or perceived threats. In this context, modelling the transport safety assessment of multimodal freight is relevant and requires appropriate scientific developments. In the research, methods of mathematical modelling of transport systems were used in order to develop methodological approach that could be potentially used in improving the multimodal freight transportation safety. A methodical approach to safety assessment on the example of multimodal transportation, which takes into account the drawbacks of existing studies, is proposed. The practical application of developed model was demonstrated on the existing system of multimodal transportation. The developed safety assessment method may be potentially used both to assess the transportation route safety level and as key factor in optimal transportation route designing.