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Modelling of transport operations in supply chains in obedience to “just-in-time” conception

    Valery Lukinskiy Affiliation
    ; Vladislav Lukinskiy Affiliation
    ; Yuri Merkuryev Affiliation

Abstract

Transportation is a key logistics function, which determines the dynamic nature of material flows in logistics systems. At the same time, transportation is a source of uncertainty of logistics operations performance in the supply chain. Obviously, the development of a new approach for evaluation of the duration of delivery “Just-In-Time” (JIT) will improve the efficiency of supply chains in accordance with one of the major criteria, namely customer satisfaction. One of the basic approaches to make effective management decisions in transportation and other logistic operations is the JIT concept. In the majority of examined sources the JIT concept is described on the verbal level without any usage of calculation dependences. The paper is devoted to the formation of analytical and simulation models, which allow obtaining the probabilistic evaluation of the implementation of unimodal and multimodal international transportation JIT. The first model where the order of the operations implementation does not affect final result is formed on the basis of the probability theory: distribution laws composition, theorems of numerical characteristics of random variables, formula of complete probability. The second model accounts the impact of operations implementation order in transportation and their interconnection and is based on the simulation (the method of statistic experiments) and shown as a corresponding algorithm, which allows to consider different limitations (technical, organizational and so on). Considered analytical dependences give the possibility to obtain the necessary estimations of the transport operations implementation according to JIT: mean transportation time, delivery implementation probability by the set moment or the delivery time with the set probability. To carry out some comparative calculations and clarify the algorithm, two international routes have been chosen: the first one is a unimodal road transportation, the second one is a multimodal transportation (road and marine transport). All the data, which is necessary for calculation has been collected on the basis of official information (in particular, the data of tachograph, special questionnaires filled in by the drivers, the survey results of the managers). For unimodal transportations analytical dependences and modelling results give close results. For the combined multimodal transportations taking into account various limitations the preference must be given to the simulation. The modelled indexes take into consideration their intercommunication and definitely estimate the supply chains reliability, and this allows decreasing the uncertainty of the logistic system.

Keyword : logistics, probability, reliability, scheduling, simulation, transportation

How to Cite
Lukinskiy, V., Lukinskiy, V., & Merkuryev, Y. (2018). Modelling of transport operations in supply chains in obedience to “just-in-time” conception. Transport, 33(5), 1162-1172. https://doi.org/10.3846/transport.2018.7112
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Dec 18, 2018
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References

Axsäter, S. 2015. Inventory Control. Springer. 268 p. https://doi.org/10.1007/978-3-319-15729-0

Ballou, R. H. 2003. Business Logistics: Supply Chain Management. Prentice Hall. 816 p.

Bazaras, D.; Yatskiv, I.; Mačiulis, A.; Palšaitis, R. 2015. Analysis of common governance transport system development possibilities in the east-west transport corridor, Transport and Telecommunication Journal 16(1): 31–39. https://doi.org/10.1515/ttj-2015-0004

Bifulco, G. N.; Di Pace, R.; Viti, F. 2014. Evaluating the effects of information reliability on travellers’ route choice, European Transport Research Review 6(1): 61–70. https://doi.org/10.1007/s12544-013-0110-4

Bowersox, D. J.; Closs, D. J. 1996. Logistical Management: The Integrated Supply Chain Process. McGraw-Hill College. 752 p.

Chalumuri, R. S.; Yasuo, A. 2014. Modelling travel time distribution under various uncertainties on Hanshin expressway of Japan, European Transport Research Review 6(1): 85–92. https://doi.org/10.1007/s12544-013-0111-3

Christopher, M. 2016. Logistics and Supply Chain Management. FT Press. 328 p.

Chopra, S.; Meindl, P. 2015. Supply Chain Management: Strategy, Planning, and Operation. Pearson. 516 p.

Coyle, J. J.; Bardi, E. J.; Langley, C. J. 2002. Management of Business Logistics: A Supply Chain Perspective. South-Western College Pub. 707 p.

Ge, Y.-E.; Prentkovskis, O.; Tang, C.; Saleh, W.; Bell, M.; Junevičius, R. 2015. Solving traffic congestion from the demand side, Promet – Traffic & Transportation 27(6): 529–538. https://doi.org/10.7307/ptt.v27i6.1734

Heizer, J.; Render, B.; Munson, C. 2016. Operations Management: Sustainability and Supply Chain Management. Pearson. 912 p.

Huber, S.; Klauenberg, J.; Thaller, C. 2015. Consideration of transport logistics hubs in freight transport demand models, European Transport Research Review 7: 32. https://doi.org/10.1007/s12544-015-0181-5

Jonsson, P. 2008. Logistics and Supply Chain Management. McGraw-Hill Education. 510 p.

Kersten, W.; Blecker, T. 2006. Managing Risks in Supply Chains: How to Build Reliable Collaboration in Logistics. Erich Schmidt Verlag. 300 p.

Krajewski, L. J.; Malhotra, M. K.; Ritzman, L. P. 2015. Operations Management: Processes and Supply Chains, Student Value Edition. Pearson. 672 p.

Krüger, N. A.; Vierth, I. 2015. Precautionary and operational costs of freight train delays: a case study of a Swedish grocery company, European Transport Research Review 7: 6. https://doi.org/10.1007/s12544-015-0155-7

Langevin, A.; Riopel, D. 2005. Logistics Systems: Design and Optimization. Springer. 388 p. https://doi.org/10.1007/b106452

Lazauskas, J.; Bureika, G.; Valiūnas, V.; Pečeliūnas, R.; Matijošius, J.; Nagurnas, S. 2012. The research on competitiveness of road transport enterprises: Lithuanian case, Transport and Telecommunication Journal 13(2): 138–147. https://doi.org/10.2478/v10244-012-0011-y

Lukinskiy, V. S.; Lukinskiy, V. V.; Churilov, R. 2014. Problems of the supply chain reliability evaluation, Transport and Telecommunication Journal 15(2): 120–129. https://doi.org/10.2478/ttj-2014-0011

Lukinskiy, V. S.; Malevich, Yu.; Plastunyak, I.; Pletnyova, N.; Lukinskiy, V. V. 2007. Modeli i metody teorii logistiki. Sankt-Peterburg: Piter. 448 s. (in Russian).

Mazurkiewicz, J.; Walkowiak, T. 2013. Discrete transportation system’s availability problem in case of critical situation sets, Transport and Telecommunication Journal 14(4): 272–281. https://doi.org/10.2478/ttj-2013-0023

Palšaitis, R.; Petraška, A. 2012. Heavyweight and oversized cargo transportation risk management, Transport and Telecommunication Journal 13(1): 51–56. https://doi.org/10.2478/v10244-012-0005-9

Palšaitis, R.; Ponomariovas, A. 2012. Assessment of rail freight transport service quality, Transport and Telecommunication Journal 13(3): 188–192. https://doi.org/10.2478/v10244-012-0015-7

Petraška, A.; Čižiūnienė, K.; Jarašūnienė, A.; Maruschak, P.; Prentkovskis, O. 2017. Algorithm for the assessment of heavyweight and oversize cargo transportation routes, Journal of Business Economics and Management 18(6): 1098–1114. https://doi.org/10.3846/16111699.2017.1334229

Petraška, A.; Čižiūnienė, K.; Prentkovskis, O.; Jarašūnienė, A. 2018. Methodology of selection of heavy and oversized freight transportation system, Transport and Telecommunication Journal 19(1): 45–58. https://doi.org/10.2478/ttj-2018-0005

Stadtler, H.; Kilger, C. 2008. Supply Chain Management and Advanced Planning: Concepts, Models, Software and Case Studies. Springer. 512 p. https://doi.org/10.1007/b106298

Stock, J.; Lambert, D. 2000. Strategic Logistics Management. McGraw-Hill/Irwin. 896 p.

Vasilis Vasiliauskas, A.; Jakubauskas, G. 2007. Principle and benefits of third party logistics approach when managing logistics supply chain, Transport 22(2): 68–72.

Ventcel’, E. S.; Ovcharov, L. A. 1983. Prikladnye zadachi teorii veroyatnostej. Moskva: Radio i svyaz’. 416 s. (in Russian).

Wisner, J. D.; Leong, G. K.; Tan, K.-C. 2004. Supply Chain Management: a Balanced Approach. South-Western College Pub. 528 p.

Yatskiv, I.; Pticina, I.; Savrasovs, M. 2012. Urban public transport system’s reliability estimation using microscopic simulation Transport and Telecommunication Journal 13(3): 219–228. https://doi.org/10.2478/v10244-012-0018-4