A stochastic model predictive control to heterogeneous rail freight car fleet sizing problem

Milenković, Miloš; Bojović, Nebojša J.; Švadlenka, Libor; Melichar, Vlastimil

doi:10.1016/j.tre.2015.07.009

Cited by 27 publications

(9 citation statements)

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“… Casadevall and Pirofski, 2020 , Deb and Jain, 2013 , Deb et al, 2000 , DehghaniAshkezari and Yaghoubi, 2019 , Dhiman et al, forthcoming , Dolgui et al, 2018 , Hosseini-Motlagh et al, 2020a , Ma et al, 2019 , Milenković et al, 2015 , Mitropoulos et al, 2006 , Samani et al, 2020 , Soner et al, 2020 , Wang et al, 2019 , World Health Organization, 2020 , Zhang et al, 2020a , Zhang et al, 2020b , Zhou and Zheng, 2020 .…”

Section: Uncited Referencesmentioning

confidence: 99%

Convalescent plasma bank facility location-allocation problem for COVID-19

Manupati

Schoenherr

Wagner³

et al. 2021

Transportation Research Part E: Logistics and Transportation Re

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Section: Uncited Referencesmentioning

confidence: 99%

Convalescent plasma bank facility location-allocation problem for COVID-19

Manupati

Schoenherr

Wagner³

et al. 2021

Transportation Research Part E: Logistics and Transportation Re

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“…Автори статті [2] представляють класифікацію проблем щодо парку рухомого скраду у розрізі морських та автомобільних перевезень. У дослідженнях [3][4][5][6][7] вирішуються задачі визначення розміру парку залізничних вагонів, стаття [8] присвячена питанню визначення розміру парку для каршерінгових систем.…”

Section: аналіз останніх досліджень та публікаційunclassified

Information Technology of Decision Support to Design the Transportation Orders' Servicing

Davidich

Samchuk

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et al. 2021

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The main purpose of most transport companies is to provide the quality services to customers with minimal costs. At the same time, determination of the number of vehicles and their utilization rate when satisfying transportation orders is the important task, the proper solution of which leads to the full and timely servicing and contributes to an increase of a transport company's competitiveness in the present-day market. Due to the analysis results of the state-of-the-art literature and Internet sources, it has been revealed that the problem of finding the rational fleet size and the rate of its utilization to complete the transportation orders were not fully solved. From the criteria analysis it has been proposed to substantiate the vehicle fleet size according to the car utilization rate to be assigned as the "vehicle working time-to-total working time" ratio. Considering the probabilistic nature of the transportation process, a simulation model to complete the orders by a truck fleet has been developed in the AnyLogic environment. An experimental plan has been developed to reproduce the real transportation order conditions and consisted of 27 series, each of which was of 100 experiments. The variation range of input factors, which was the transportation distance, vehicles' number and orders' hourly intensity were [10;30], [1;3] and [0.6;1], respectively. From the experimental results processing by the regression analysis methods, it has been found that the dependence of changes in the car utilization rate, transportation distance, vehicle' number and orders' intensity was of linear form. The obtained dependence has been estimated via the determination coefficient, which was 0.95, and indicated the high quality of the model proposed. The resulting model allows calculating the required number of vehicles from their operating conditions. In the case study the 2 vehicles were recommended to service the transportation orders. Further research efforts can be taking into account a larger number of influencing factors, increasing their variation range and obtaining dependencies to describe the presented criterion change to acceptable accuracy.

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“…The implementation of the developed mathematical model has resulted in solving the problem of optimization [9,25]. Milenkovič, Bojovič, Švadlenka and Melichar [16] represent one of the most successful and most popular advanced control methods in the analysis of stochastic variables.…”

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confidence: 99%

Data Mining Workspace as an Optimization Prediction Technique for Solving Transport Problems

et al. 2017

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Summary. This article addresses the study related to forecasting with an actual highspeed decision making under careful modelling of time series data. The study uses datamining modelling for algorithmic optimization of transport goals. Our finding brings to the future adequate techniques for the fitting of a prediction model. This model is going to be used for analyses of the future transaction costs in the frontiers of the Czech Republic. Time series prediction methods for the performance of prediction models in the package of Statistics are Exponential, ARIMA and Neural Network approaches. The primary target for a predictive scenario in the data mining workspace is to provide modelling data faster and with more versatility than the other management techniques. РЕШЕНИЕ ЗАДАЧИ ПРОГНОЗИРОВАНИЯ В ТРАНСПОРТНОЙ ОТРАСЛИ С ПОМОЩЬЮ МЕТОДОВ DATA MININGАннотация. В данной статье рассматривается задача прогнозирования временных рядов, которая заключается в построении модели для предсказания будущих событий. В исследовании используются методы интеллектуального анализа данных. Модель прогнозирования позволяет адекватно оценивать исследуемый процесс. Целью исследования является изучение динамики расходов при реализации экспортной продукции. Прогнозирование осуществляется с помощью ARIMA-модели, на основе метода экспоненциального сглаживания и по технологии логической нейронной сети. Построение базового и быстрого сценария прогнозирования является важным и ответственным этапом в научной деятельности.

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A stochastic model predictive control to heterogeneous rail freight car fleet sizing problem

Cited by 27 publications

References 44 publications

Convalescent plasma bank facility location-allocation problem for COVID-19

Convalescent plasma bank facility location-allocation problem for COVID-19

Information Technology of Decision Support to Design the Transportation Orders' Servicing

Data Mining Workspace as an Optimization Prediction Technique for Solving Transport Problems

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