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Агрегация и анализ сведений логистических компаний для построения сложного маршрута перевозки грузаAggregation and analysis of information from logistics companies to build a complex cargo transportation route
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At present, e-commerce drives the logistics industry to develop greatly, but at the same time, there is a huge demand in this field, such as lower cost and higher efficiency. Facing the needs of logistics management development, it needs the blessing of intelligent technology, which involves countless fields at present. Intelligent logistics management has become a hot spot at present. What needs to be solved in this respect is how to shorten the transportation distance and save costs. To solve this problem, this paper proposes to introduce the shortest path algorithm. This paper compares the Dijkstra algorithm with the A ∗ algorithm under the background of logistics management and finds that the latter is more suitable for this field with huge amount of information. In order to improve the performance of the A ∗ algorithm, this paper introduces ant colony algorithm, which can better avoid obstacles. Combining these two algorithms, a ∗ -ant colony algorithm is obtained. The algorithm absorbs the advantages of the two algorithms, while maintaining high efficiency and good stability. These characteristics are very satisfying in the field of logistics management. Through the performance test and simulation experiment, it is concluded that the algorithm has excellent optimization ability and can reduce the cost for this field.
To solve the problem of long logistics delivery time in supply chain, a Mixed Integer Non-linear Program (MINLP) model is built by using Mixed Integer nonlinear programming theory. Firstly, the General algebraic modeling system (GAMS) is used to build the model to fully integrate each parameter of logistics transportation, the total distribution time of the supply chain network, the coverage radius of the logistics base, the number of users, the total capacity of the logistics base, the mode of railway and road transportation, the nonlinear programming model is built and solved by DICOPT solver in GAMS. The cost of logistics can be decreased, transportation time can be reduced, and the logistics system's operating efficiency can be increased in the long term with the help of this algorithm. The proper operation of the logistics system is critical in encouraging the supply chain circulation of various industries and has a direct impact on the society's economic development. The optimal logistics distribution plan with 5 logistics bases covered users of 18 and railway capacity of 2. With the same railway capacity and the same total budget, the larger the number of covered users, the greater the total distribution time increases, but the larger the total budget, the growth of the total distribution time slows down significantly. Experiments show that MINLP model can solve the problem of logistics-based layout optimization in nonlinear logistics management.
This paper investigates the real-time customized bus (CB) route optimization problem, which aims to maximize the service rate for clients and profits for operators. The on-road bus has a flexible route, which can be updated based on the real-time data and route optimization solutions. A two-phase framework is established. In phase 1, the vehicle-related data including existing route and schedule, client-related data involving pick-up/drop-off location, and time windows are collected once receiving a new CB request. The second phase optimizes the bus route by establishing three nonlinear programming models under the given data from phase 1. A concept of profit difference is introduced to decide the served demand. To improve computation efficiency, a real-time search algorithm is proposed that the neighboring buses are tested one by one. Finally, a numerical study based on Sioux Falls network reveals the effectiveness of the proposed methodology. The results indicate that the real-time route optimization can be achieved within the computation time of 0.17–0.38 seconds.
This article is devoted to the technologies of web scraping (web crawling) for Node.js, used in the task of aggregating information and estimating the parameters of cargo routes by extracting data from open sources. The challenge of web scraping occurs in many different contexts, both scientific and industrial. The tasks of web scraping have both wide practical applications and a significant educational aspect. However, the existing material on web scraping is scattered and unstructured. In this paper, using the example of solving the scientific and technical problem of aggregating information and evaluating the parameters of cargo routes by extracting data from open sources, an overview of the technologies for web scraping on Node.js is presented, the classification of sites by complexity is described, the systematization of the features of sites that are an obstacle to scrapinf is given, and possible ways to bypass them. Thus, the didactic goal of this article is achieved to systematize the material on parsing websites.
Logistics centers are important facilities which served possible transportation methods together and and carried out activities in harmony with each other such as transportation, storage, handling, distribution, consolidation, customs clearance, sorting, import, export and transit transactions, insurance and banking, infrastructure services, consultancy and production, etc. Due to the continuous demand for freight, freight transportation must be realized uninterrupted, in the shortest way, and optimal in terms of fuel consumption and transportation time. In this study, the problem of transportation of freight trains to logistics centers by the shortest route is discussed. When the literature on logistics villages is scanned; The contributions of logistics centers to the logistics sector have been examined, their strengths and weaknesses have been tried to be revealed, and studies on suggestions for effective and efficient operation have been observed. But in this study; with an engineering applications approach to the subject, it is aimed to obtain the most appropriate result for the route selection among the logistics centers by including twelve logistics centers operating throughout the country as well as seven logistics centers that are in the construction and tender stages, in the scope of the study. In generally the application area of the shortest path problems is to determine the shortest distance between the points in a network, the Floyd-Warshall Algorithm is preferred in this study in terms of determining the shortest path between any two nodes in the network. For this purpose, a graph consisting of a total of 33 nodes was created for the national railway network and the Floyd-Warshall algorithm was coded in the Python programming language to calculate the shortest route and the shortest route were determined between a total of nineteen logistics centers.
E-Commerce Business is widely spread all around the world, and explored by almost half the population of the world, because of its facility that it provides doorstep delivery. You just need to place the order on e-commerce website and within Two to three days max the order gets delivered at your doorstep. But it’s a challenge for e-commerce logistic department (ECL) to manage this order and delivery chain. They need to have a predefined strong network of order and delivery chain. While delivering the small parcels e-commerce department considers delivery assistant/ Boy to deliver these parcels. For a Single Delivery assistant it’s very hard and headache job to deliver packages accordingly. Here the problem arrives of last mile distribution, completing target deliveries before deadline. This project aims to solve the last-mile distribution of e-commerce logistics (ECL) for the survival of third-party logistics enterprise. Delivery assistant have software which navigates them towards the delivery address. But this creates headache and waste of time while delivering parcels one by one separately without having an optimized route which connects all the target parcels to be delivered in a day. Route Optimization software needs to develop to solve these problems. This project aims to develop an optimized solution on above problems based on multiple source multiple destination approach using Dijkstra’s algorithm and scripted using python libraries. On this basis, the improved Dijkstra was proved effective through example analysis on the said test data sets. The analysis results also reflect how the number of vehicles affects the maximum profit of the logistics enterprise and the coverage of the ECL logistics network.
Driving style, traffic and weather conditions have a significant impact on vehicle fuel consumption and in particular, the road freight traffic significantly contributes to the CO
2
increase in atmosphere. This paper proposes an Eco-Route Planner devoted to determine and communicate to the drivers of Heavy-Duty Vehicles (HDVs) the eco-route that guarantees the minimum fuel consumption by respecting the travel time established by the freight companies. The proposed eco-route is the optimal route from origin to destination and includes the optimized speed and gear profiles. To this aim, the Cloud Computing System architecture is composed of two main components: the Data Management System that collects, fuses and integrates the raw external sources data and the Cloud Optimizer that builds the route network, selects the eco-route and determines the optimal speed and gear profiles. Finally, a real case study is discussed by showing the benefit of the proposed Eco-Route planner.
In most European countries, websites are available with route planners and real time information about incidents and expected delay on the highways. These websites enable car drivers to plan their route from start to destination. On their way, they can use route planners on their smart phone or other devices. Recent route planners take care of incidents on the roads. However, none of these planners takes care of the future. In this paper, we present a route planner integrating information from experts in the traffic centers and a route planner based on a dynamic version of the Dijkstra algorithm.
This paper aims to handle the problem of vehicle routing optimisation in agricultural products logistics distribution. The vehicle routing optimisation problem is converted to a graph model calculation problem and then the node set of the graph contain depots and customers. The vehicle routing optimisation is to seek an optimal one from all possible paths which consumes least fuels. The main innovation of this paper is to introduce the ant colony algorithm in the vehicle route optimisation problem. In vehicle routing, each ant starts from the depot and goes through several customers and then goes back to the starting point. Furthermore, customers are determined with the pheromone information and multiple pheromone information matrixes are built up. Finally, experimental results demonstrate that our proposed algorithm can significantly reduce fuel cost in agricultural products logistics distribution.
In the research of the park gathering industry manufacturing mode, the route of existing systems to provide logistics and transportation services often cannot meet the actual scheduling needs. To find a suitable distribution method is necessary. In this paper, based on the background of the industrial park logistics scheduling, the Dijkstra algorithm is investigated and proposed an improved algorithm based on the original algorithm. Experimental results show that the improved Dijkstra algorithm not only retains the advantages of the original algorithm, but also can quickly and accurately find out the optimal delivery route based on the latest traffic conditions, significantly improve the logistics park path search efficiency and optimize the quality of the scheduling. Realized reduce consumption, improve efficiency and reduce the purposes of logistics costs throughout the park.
Based on practical project, this study proposes key steps of optimization and decision-making in the fourth-party logistics operation. First, we identified supplier evaluation indicators by using the fuzzy comprehensive evaluation method. A directed graph model with multi-dimensions was established based on four elements: price, time, logistics capacity and evaluation information of 3PL suppliers. After simplifying and transforming some restrictions, we solved the problem with Dijkstra algorithm.
Агрегация сведений и оценка параметров грузовых маршрутов в условиях информационного дефицита // ИБРР-2021: мат. конф. 2021
- М В Абрамов
- М С Есин
Абрамов М.В., Есин М.С. Агрегация сведений и оценка параметров грузовых маршрутов в условиях информационного дефицита // ИБРР-2021: мат. конф. 2021. С. 328-329.
Dynamic programming method for optimization problem of multi-modal transportation
- D Zavalishchin
- A Popkov
Zavalishchin D., Popkov A. Dynamic programming method for optimization problem of multi-modal transportation. CEUR-WS Proc. Proc. MMIT, 2016, vol. 1825, pp. 118-122.
Унификация принятия логистических решений с использованием теории графов // Экономика, право и образование в условиях риска и неопределенности: тенденции и перспективы развития: матер. Междунар. науч.-практич. конф
- М В Ашихмина
- В В Городничев
Ашихмина М.В., Городничев В.В. Унификация принятия логистических решений с использованием теории
графов // Экономика, право и образование в условиях риска и неопределенности: тенденции и перспективы развития: матер. Междунар. науч.-практич. конф. 2016
Aggregation of information and estimation of parameters of cargo routes in conditions of information deficit
- M V Abramov
- M S Esin
Abramov, M.V., Esin, M.S. (2021) 'Aggregation of information and estimation of parameters of cargo routes in
conditions of information deficit', Proc. ISRR-2021, pp. 328-329 (in Russ.).
Unification of logistic decision making using graph theory
- M V Ashikhmina
- V V Gorodnichev
Ashikhmina, M.V., Gorodnichev, V.V. (2016) 'Unification of logistic decision making using graph theory', Proc.
Intern. Sci. and Pract. Conf. Economics, Law and Education Under Risk and Uncertainty: Trends and Prospects,
pp. 7-10 (in Russ.).