A modelling approach to the transport support for the harvesting and transportation complex under uncertain conditions

Medvediev, Ievgen; Eliseyev, Peter; Lebid, Iryna; Sakno, Olha

doi:10.1088/1757-899x/977/1/012003

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Надійна організація логістики постачання формується з використанням існуючих елементів інфраструктури транспортної мережі з урахуванням ризиків і збоїв [2,3]. Ресурси мають використовуватися більш ефективно, а логістична система має будуватися з одночасним використанням різних джерел постачання та з додержанням таких принципів доставки, як «гнучкість» і «саме вчасно» [4,5,6], оскільки це допомагає зменшити ризики відмови та проблеми в логістиці постачання товарів.…”

Section: постановка проблемиunclassified

Model of Functioning Logistics for Supply of Specialised Vehicles in Containers From North American Companies to Ukraine

Pavlenko,

Muzylov,

Medvediev

2024

MEC

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The article analyses the current state and prospects for developing logistics for supplying various types of cargo in international traffic to be transported in containers. It allowed us to formulate the purpose of the study in this development. This topic is relevant since the efficient organisation of containerised supply logistics can improve the functioning of transport systems. An effective way to reduce the cost of supplying a cargo unit is efficient resource use (railway stations, ports, warehouses, wagons, vehicles, containers). The study paid considerable attention to developing the relevant infrastructure, solving specific problems of efficient use of all types of resources, using digital technologies, and building reliable models of logistics supply systems. The article builds a structural model based on the principles of logistics interaction of possible participants in the supply process, representing the interaction of subsystems that significantly affect the quality of the relevant operations. We formed five schemes of interaction between logistics participants for supplying specialised vehicles in containers with the participation of automobile, railway, and sea transport and the relevant infrastructure. These schemes identify the main ports of departure in the United States (New York and Houston) and ports of destination in Europe (Klaipeda (Lithuania), Constanta (Romania)), and take into account the possibility of using road and rail transport resources in European countries and Ukraine. We developed a mathematical model of logistics functioning for the supply of specialised vehicles in containers from North American enterprises to Ukraine, which accounts for the specifics of sequential operations and the peculiarities of determining the performance indicators of each supply chain operation. The prospect of using these models is to build a simulation model of logistics for the supply of specialised vehicles in containers from North American companies to Ukraine, conduct simulations using a software environment, and determine the rational option under the established conditions. Keywords: model, logistics, supply, vehicles, international transportation.

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Section: постановка проблемиunclassified

Model of Functioning Logistics for Supply of Specialised Vehicles in Containers From North American Companies to Ukraine

Pavlenko,

Muzylov,

Medvediev

2024

MEC

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“…1, there are eight nodes, node 1 is the starting point, and node 8 is the end point; there are 11 paths, all of which can be finished by highway, railway, and waterway transport. Logistics often produce various uncertainties in the transportation process, influenced by external factors (Medvediev et al, 2020). For example, the transportation time cannot be fully maintained within the set time and will be affected by the transportation road conditions and weather; goods may also be temporarily increased or decreased in the intermediate nodes in the transportation process.…”

Section: Multimodal Transport Path Optimization Model Under Transport...mentioning

confidence: 99%

Rail Consignment Path Planning Based on Multimodal Transport: Considering the Time Uncertainty Condition

2023

EPPM-Journal

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Multimodal transport has emerged and applied to logistics work as single-mode transport was unable to meet the growing demand for logistics. Due to the difference in cost and time consumption between different modes of transportation, the path planning of multimodal transport is different from that of single-mode transport. This paper firstly established a multimodal railroad consignment path optimization model under the condition of transportation time uncertainty and then optimized the path and transportation mode with the particle swarm optimization (PSO) algorithm according to the path optimization model. In addition, the PSO algorithm was optimized by a genetic algorithm to avoid the optimization process of the PSO algorithm from falling into the locally optimal solution. Finally, simulation experiments were carried out on the improved PSO algorithm, and it was compared with the traditional PSO and genetic algorithms. The results showed that the improved PSO algorithm converged to stability after about 200 iterations, the traditional PSO algorithm converged to stability after about 240 iterations, and the genetic algorithm converged to stability after about 400 iterations; the total transport cost and time of railway transportation were USD 114,300 and 52.5 h; the total transport cost and time of the path obtained by the genetic algorithm were USD 60,400 and 35.6 h; the total transport cost and time of the path obtained by the traditional PSO algorithm were USD 37700 and 24.3 h; the total transport cost and time of the path obtained by the improved PSO algorithm were USD 3,1900 and 23.4 h.

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“…The operational procedures used for harvesting are also impacted by the weather. Through interviews with wheat harvest specialists, the effects of the harvesting process on operational and logistic services were investigated (Medvediev et al, 2017). There are no studies that optimize the harvesting process using a cost-oriented strategy depending on whether it is raining or not, according to the literature review.…”

Section: Introductionmentioning

confidence: 99%

Precipitation forecast with logistics regression methods for harvest optimization

SAMASTI¹,

Küçükdeniz

2023

International Journal of Agriculture Environment and Food Sciences

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This paper proposes a model that forecasts the weather and then, based on that forecast, uses an income-oriented linear programming method to optimize the harvesting process. Data representing a total yearly output capacity of 472,878 tons from 214 different field locations were used to test the model for sugar beet production. Prior to optimization, long-term one-year weather rainfall forecasting was done using 10 years of actual weather data for the field locations. Weather precipitation was forecasted using logistic regression with an accuracy of 84.16%. The outcome of the weather precipitation prediction model was a parameter in the optimization model. The weather forecast for precipitation led to the 120-day harvest planning being optimized. Comparative analysis was done on the outcomes of the developed model and the current scenario. Comparing the current situation to the proposed one, revenue would have increased by 16.7%. Given that it incorporates weather forecasts into the harvest optimization process, the methodology presented in this paper is more practical than other harvest optimization models.

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A modelling approach to the transport support for the harvesting and transportation complex under uncertain conditions

Cited by 5 publications

References 2 publications

Model of Functioning Logistics for Supply of Specialised Vehicles in Containers From North American Companies to Ukraine

Model of Functioning Logistics for Supply of Specialised Vehicles in Containers From North American Companies to Ukraine

Rail Consignment Path Planning Based on Multimodal Transport: Considering the Time Uncertainty Condition

Precipitation forecast with logistics regression methods for harvest optimization

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