The model of selecting multimodal technologies for the transport of perishable products

Paweł LELEŃ; Mariusz WASIAK

doi:10.5604/01.3001.0013.5573

Authors

Paweł LELEŃ Warsaw University of Technology, Faculty of Transport, Warsaw, Poland Author
Mariusz WASIAK Warsaw University of Technology, Faculty of Transport, Warsaw, Poland Author

DOI:

https://doi.org/10.5604/01.3001.0013.5573

Keywords:

perishable cargo, multimodal transport, transport technologies selection, mathematical model

Abstract

The main goal of this paper is to provide an original model of selecting multimodal technologies for the transport of perishable goods. The model in particular refers to the transportability of cargoes. The features of cargoes that have the most impact on transportability were specified. Formal representations of the key elements of the model were presented and characterized, including: perishable cargoes, form of transported goods (solid, liquid, etc.), means of handling (including loading devices and transport means), transport routes, categories of human labor, multimodal technologies and transportation tasks. A formal representation of decision variables, as well as constrains and a criterion function were provided. The model bases on two main solution assessment criteria: cost criterion and cargo safety criterion. A cargo safety criterion in the model is composed of 18 partial criterion functions. Each of these functions directly affects one safety aspect of the transported cargo. The exemplary partial criteria of cargo safety included in the model are: acceptable transport time, minimum or maximum temperature in the cargo’s direct surroundings, resistance to mechanical damage. In order to present a practical application of the presented mathematical model the paper shows also an example of selecting one of the multimodal technologies for the transport of perishable goods from the set of pre-defined types of multimodal transport technologies. The developed method uses different elements of the mathematical model provided in the paper, depending on the considered problem (including characteristics of cargo and their transport forms). For a significant group of perishable cargoes, it is not required to consider all defined criteria associated with cargo safety. The developed model allows for the accurate selection of transport technology for perishable cargoes for most transportation tasks. It should help to increase the efficiency of selection of multimodal transport technology for perishable products. The selected technology will then be characterized by the lowest transport cost and will ensure the safety of transported cargoes, as well as will meet other requirements determined by the transport task. As part of further work, it is possible to develop proposed method by considering additional characteristics of perishable cargoes.

References

ACCORSI, R., GALLO, A., MANZINI, R., 2017. A climate driven decision-support model for the distribution of perishable products. Journal of Cleaner Production, 165, 917-929.

ANTONOWICZ M., 2011. Regulation and Logistics in rail freight transport. Archives of Transport, 23, Iss. 3, 275-284.

BAK, O. 2018. Supply chain risk management research agenda – from a literature review to a call for future research directions. Business Process Management Journal 28.

BARFORD, M. B., SALLING, K. M., LELEUR, S., 2011. Composite decision sup-

port by combining cost-benefit and multicriteria decision analysis. Decision Support Systems, 51, Iss. 1, 167-175.

BOGATAJ, D., BOGATAJ, M., HUDOKLIN, D., 2017. Mitigating risks of perishable products in the cyber-physical systems based on the extended MRP model. International Journal of Production Economics, 193, 51-62.

BOGDANOWICZ, S., 2008. Mierniki oceny podatności w aspekcie realizacji procesu transportowego. Prace Naukowe Politechniki Warszawskiej. Transport, 64, 19-29,

BOGDANOWICZ, S., 2012. Podatność. Teoria i zastosowanie w transporcie. Warszawa: Oficyna Wydawnicza Politechniki Warszawskiej.

COLIN T., ZHIGUO L., 2014. Quantitative evaluation of mechanical damage to fresh fruits. Trends in Food Science and Technology, 35, 138-150.

GRILLO, H.,.ALEMANYA, M.M.E., ORTIZA, A., FUERTES-MIQUEL, V.S., 2017. Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Applied Mathematical Modelling, 49, 255-278.

GUS, 2012. Statistical Yearbook of Poland 2012. Warsaw.

GUS, 2014. Statistical Yearbook of Poland 2014. Warsaw.

GUS, 2016. Statistical Yearbook of Poland 2016. Warsaw.

GUS, 2017. Statistical Yearbook of Poland 2017. Warsaw.

HORUBAŁA, A., 1975. Przechowalnictwo żywności. Warszawa: PWN.

JACYNA M., 2001, Modelowanie wielokryterialne w zastosowaniu do oceny systemów transportowych, Prace Naukowe Politechniki Warszawskiej. Transport, 47, 3-139.

JACYNA M., SEMENOV I.N., TROJANOWSKI P., 2015. The research directions of increase effectiveness of the functioning of the RSA with regard to specialized transport. Archives of Transport, 35, Iss. 3, 27-39

JACYNA, M, WASIAK, M., 2015. Multicriteria Decision Support in Designing Transport Systems in: MIKULSKI J. (eds) Tools of Transport Telematics. TST 2015. Communications in Computer and Information Science, vol. 531.

JACYNA, M., IZDEBSKI, M., SZCZEPAŃSKI, E., & GOŁDA, P., 2018. The task assignment of vehicles for a production company. Symmetry-Basel, 11(10), 1–19.

JACYNA-GOŁDA, I., 2015. Decision-making model for supporting supply chain efficiency evaluation. Archives of Transport, 33, Iss. 1, 17-31.

JACYNA-GOŁDA, I, IZDEBSKI, M., SZCZEPAŃSKI, E., GOŁDA, P., 2018. The assessment of supply chain effectiveness. Ar-chives of Transport, 45(1), 43-52.

JAMES, S. J., JAMES, C., EVANS, J. A., 2006. Modelling of food transportation system – a review. International Journal of Refrigeration, 29, 947-957.

JANKOWSKI, A., & KOWALSKI, M. (2018). Alternative fuel in the combustion process of combustion engines. Journal of KONBiN, 48(1), 55-81.

KAIPIA R., DUKOVSKA‐POPOVSKA I., LOIKKANEN L., 2013. Creating sustainable fresh food supply chains through waste reduction. International Journal of Physical Distribution & Logistics Management, 43, Iss: 3, 262-276.

LELEŃ, P. WASIAK, M., 2017. Współczynniki podatności transportowej ładunków szybko psujących się. Prace Naukowe Politech-niki Warszawskiej. Transport, 117, 161-176.

LELEŃ, P., 2015. Trendy zmian w organizacji transportu w obrocie produktami żywnościowymi na przykładzie warzyw i owoców. Prace Naukowe Politechniki Warszawskiej. Trans-port, 107, 69-84.

LELEŃ, P., WASIAK M., 2018. Optimization of multimodal transport technologies selection for packed non climacteric vegetables and fruits. In: SIERPIŃSKI G. (eds). Advanced Solutions of Transport Systems for Growing Mobility. TSTP 2017. Advances in Intelligent Systems and Computing (AISC), 631, 203-215, Cham: Springer.

LIU, L., LIU, X., LIU, G., 2018. The risk management of perishable supply chain based on

coloured Petri Net modelling. Information Processing In Agriculture.

MADEYSKI, M., 1960. Podatność przewozowa ładunków. Problemy Transportu Samochodowego, 2, 3-12.

MADEYSKI, M., LISSOWSKA, E., 1981. Badania analityczne transportu samochodowego. Warszawa: Wydawnictwo Komunikacji i Łączności.

MAŃKA, I., MAŃKA, A., 2016. Cost analysis and optimization in the logistic supply chain using the SimProLOGIC program. Scientific Journal of Silesian University of Technology. Series Transport, 93, 91-97.

NAKANDALA, D., LAU, H., ZHANG, JJ., 2016. Cost-optimization modelling for fresh food quality and transportation. Industrial Management & Data Systems, 116, Iss. 3, 564-583.

PIEKARSKA, J., KONDRATOWICZ, J., 2011. Wykorzystanie technologii chłodniczej w transporcie żywności. Chłodnictwo, 4, 44-47.

TIJSKENS, LLM., POLDERDIJK, JJ., 1996. A generic model for keeping quality of vegetable produce during storage and distribution. Agricultural Systems, 51, Iss. 4, 431-452.

TRZASKALIK, T., 2014. Wielokryterialne wspomaganie decyzji: przegląd metod i zastosowań. Zeszyty Naukowe Politechniki Śląskiej. Organizacja i Zarządzenie, 74, 239-263.

TYLUTKI, A., 1998. Podatność transportowa ładunków na przewozy kombinowane. Problemy Ekonomiki Transportu, 2, 31-46.

Van der VORST, JGAJ, TROMP, SO, van der ZEE, DJ, 2009. Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics. International Journal of Production Research, 47, Iss. 23, 6611-6631.

WASIAK, M., 2016. Vehicle selection model with respect to Economic Order Quantity. Archives of Transport, 40, Iss. 4, 77-85.

WASIAK, M., JACYNA, M., LEWCZUK, K., SZCZEPAŃSKI, E., 2017. The method for evaluation of efficiency of the concept of centrally managed distribution in cities. Transport, 32(4), 348-357.