PREDICTING DELIVERY VOLUMES FOR PETROL STATIONS UNDER CONDITIONS OF STOCHASTIC DEMAND

1 NAUMOV Vitalii
Co-authors:
2 LORENC Augustyn 3 SOLARZ Agnieszka
Institutions:
1 Cracow University of Technology, Institute of Road and Railway Engineering and Transport, Cracow, Poland, EU, vnaumov@pk.edu.pl
2 Cracow University of Technology, Institute of Rail Vehicles, Cracow, Poland, EU, alorenc@pk.edu.pl
3 Cracow University of Technology, Institute of Road and Railway Engineering and Transport, Cracow, Poland, EU, agnieszkasolarz93@gmail.com
Conference:
CLC 2018 - Carpathian Logistics Congress, Wellness Hotel Step, Prague, Czech Republic, EU, December 3 - 5, 2018
Proceedings:
Proceedings CLC 2018 - Carpathian Logistics Congress
Pages:
137-142
ISBN:
978-80-87294-88-8
ISSN:
2694-9318
Published:
18th April 2019
Proceedings of the conference were published in Web of Science.
Metrics:
501 views / 794 downloads
Abstract

Technological processes of freight transport are characterized by stochasticity of indicators describing its efficiency. The mentioned randomness is usually could be explained by the stochasticity of demand for transport services. Consideration of the demand parameters stochasticity is especially important in planning deliveries of petrol products, as far as additional costs caused by delays in the deliveries, in this case, could be extremely high. Thus, the accurate prediction model could contribute significantly to the reduction of costs for the distribution of petrol products.The paper proposes an approach to forecasting the delivery volumes for petrol stations. The authors have developed the prediction model based on time series. The paper illustrates results obtained with the use of the developed model for different cases of the season duration as the predicting model parameter. The obtained results show that the optimal season duration differs for various types of petrol products.

Keywords: petrol demand forecast, delivery planning, Winter’s model, loss function

© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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