Influence of Transportation Mode to the Deterioration Rate: Case Study of Food Transport by Ship
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
Influence of Transportation Mode to the Deterioration Rate: Case Study of Food Transport by Ship

Authors: Danijela Tuljak-Suban, Valter Suban

Abstract:

Food as perishable goods represents a specific and sensitive part in the supply chain theory, since changing physical or chemical characteristics considerably influence the approach to stock management. The most delicate phase of this process is transportation, where it becomes difficult to ensure the stable conditions which limit deterioration, since the value of the deterioration rate could be easily influenced by the mode of transportation. The fuzzy definition of variables allows one to take these variations into account. Furthermore, an appropriate choice of the defuzzification method permits one to adapt results to real conditions as far as possible. In this article those methods which take into account the relationship between the deterioration rate of perishable goods and transportation by ship will be applied with the aim of (a) minimizing the total cost function, defined as the sum of the ordering cost, holding cost, disposing cost and transportation costs, and (b) improving the supply chain sustainability by reducing environmental impact and waste disposal costs.

Keywords: Perishable goods, fuzzy reasoning, transport by ship, supply chain sustainability.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1098916

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2586

References:


[1] M. M. Aung, Y. S. Chang, "Temperature management for the quality assurance of a perishable food supply chain", Food Control, vol. 40, pp. 198-207, 6// 2014.
[2] Barua, L.S. Mudunuri, and O. Kosheleva, “Why Trapezoidal and Triangular Membership Functions Work So Well: Towards a Theoretical Explanation”, Journal of Uncertain Systems, Vol.8, 2014.
[3] M. Bogataj, L. Bogataj, R. Vodopivec, "Stability of perishable goods in cold logistic chains", International Journal of Production Economics, vol. 93–94, pp. 345-356, 2005.
[4] H.-C. Chang, J.-S. Yao, L.-Y. Ouyang, “Fuzzy mixture inventory model involving fuzzy random variable lead time demand and fuzzy total demand”, European Journal of Operational Research, 169(1), pp. 65- 80, 2006.
[5] H.-C. Chang, “An application of fuzzy sets theory to the EOQ model with imperfect quality items”, Computers & Operations Research, Volume 31, Issue 12, pp. 2079-2092, 2004.
[6] Cordón, O., Herrera, F., Márquez, F. A., Peregrín, A. A Study on the Evolutionary Adaptive Defuzzification Methods in Fuzzy Modeling. International Journal of Hybrid Intelligent Systems, 1(1), 36-48, 2004.
[7] M. Eisele, K. Hentschel, T. Kunemund, "Hardware realization of fast defuzzification by adaptive integration", Microelectronics for Neural Networks and Fuzzy Systems, 1994., Proceedings of the Fourth International Conference on, 1994, pp. 318-323.
[8] W. B. Fitzgerald, O. J. A. Howitt, I. J. Smith, A. Hume, "Energy use of integral refrigerated containers in maritime transportation", Energy Policy, vol. 39, pp. 1885-1896, 2011.
[9] P.M. Ghare, G.H. Schrader, “A model for exponentially decaying inventory system”, International Journal of Production Research, 21, pp. 449-460, 1963.
[10] K. Govindan, A. Jafarian, R. Khodaverdi, and K. Devika, "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food", International Journal of Production Economics, vol. 152, pp. 9-28, 2014.
[11] Kaufmann, M. M. Gupta, “Introduction to Fuzzy Arithmetic: Theory and Applications”. New York, Nostrand Reinhold, 1991.
[12] W. V. Leekwijck, E. E. Kerre, "Defuzzification: criteria and classification", Fuzzy Sets and Systems, vol. 108, pp. 159-178, 1999.
[13] Y. Lemma, D. Kitaw, G. Gatew, "Loss in Perishable Food Supply Chain: An Optimization Approach Literature Review", International Journal of Scientific & Engineering Research, Volume 5, pp. 302-311, 2014.
[14] L. Lin, H.-M. Lee., “Fuzzy assessment for sampling survey defuzzification by signed distance method”, Expert System Applications, Volume 37, pp. 7852-7857, 2010. DOI=10.1016/j.eswa.2010.04.052 http://dx.doi.org/10.1016/j.eswa.2010.04.052
[15] B Liu, “Uncertainty Theory: An introduction to its Axiomatic Foundations”. Berlin, Springer-Verlag, 2004.
[16] N. Mogharreban, L. F. Di Lalla, "Comparison of Defuzzification Techniques for Analysis of Non-interval Data", Fuzzy Information Processing Society, 2006. NAFIPS 2006. Annual meeting of the North American, pp. 257-260, 2006. doi: 10.1109/NAFIPS.2006.365418
[17] L.-Y. Ouyang, K.-S. Wu, M.-C. Cheng, “An Inventory Model for deteriorating items with exponential declining demand and partial backlogging”, Yugoslav Journal of Operations Research, 15, Number 2, pp. 277-288, 2005.
[18] W. Pedrycz, “Semantics and Perception of Fuzzy Sets and Fuzzy Mappings”, Computational Intelligence: A Compendium Studies in Computational Intelligence, Volume 115, 597-639, 2008. DOI: 10.1007/978-3-540-78293-3_14
[19] T. A. Runkler, M. Glesner, "Defuzzification with improved static and dynamic behavior: Extended center of area," EUFIT’93—First European Congress on Fuzzy and Intelligent Technologies, vol. 2, pp. 845-851, 1993.
[20] C. Singh, S. R. Singh, "An integrated supply chain model for the perishable items with fuzzy production rate and fuzzy demand rate", The Yugoslav Journal of Operations Research ISSN: 0354-0243 EISSN: 2334-6043, vol. 21, 2011.
[21] L. Stefanini, "A Differential Evolution Algorithm for Fuzzy Extension of Functions", Analysis and Design of Intelligent Systems using Soft Computing Techniques, vol. 41, Springer Berlin Heidelberg, 2007, pp. 377-386.
[22] D. A. Teodorović and G. Pavković, "The fuzzy set theory approach to the vehicle routing problem when demand at nodes is uncertain", Fuzzy Sets and Systems, vol. 82, pp. 307-317, 9/23/ 1996.
[23] P. L. Theodore, I. S. Saguy, and S. T. Petros, "Kinetics of Food Deterioration and Shelf-Life Prediction", Handbook of Food Engineering Practice, CRC Press, 1997.
[24] D. Tuljak Suban, “Optimization of the ordering cycle of perishable goods by choosing the appropriate fuzzy deterioration rate and travel length”, Actual problems of logistics, Gliwice: Wydawnictwo politechniki Śląskiej, 2012, pp. 129-152.
[25] UNCTAD, Review of Maritime Transport 2013, UNCTAD/RMT/2013, UN publications, Geneve, Switzerland, 2013.
[26] X. Wang, W. Tang, “Optimal production run length in deteriorating production processes with fuzzy elapsed time”, Computers & Industrial Engineering, Volume 56, Issue 4, pp. 1627-1632, 2009.
[27] G. Wilmsmeier, R. J. Sanchez, »The relevance of international transport costson food prices: Endogenous and exogenous effects«, Research in Transportation Economics , Volume 25, Issue 1, pp. 56-66, 2009. http://dx.doi.org/10.1016/j.retrec.2009.08.004
[28] J.-S. Yao, L.-Y. Ouyang, H.-C. Chang, “Models for a fuzzy inventory of two replaceable merchandises without backorder based on the signed distance of fuzzy sets”, European Journal of Operational Research, Volume 150, Issue 3, pp. 601-616, 2003.
[29] J.-S. Yao, J. Chiang, “Inventory without backorder with fuzzy total cost and fuzzy storing cost defuzzified by centroid and signed distance”, European Journal of Operational Research, Volume 148, Issue 2, pp. 401-409, 2003.
[30] J.-S. Yao, J.-S. Su, “Fuzzy total demand and maximum inventory with backorder based on signed distance method”, International Journal of Innovative Computing, Information and Control, Volume 4, Number 9, pp. 2249-2261, 2008.
[31] Yared Lemma, D. K., Gatew, G. Loss in Perishable Food Supply Chain: An Optimization Approach Literature Review. International Journal of Scientific & Engineering Research, Volume 5(5), 302-311, 2014.
[32] Y. Zhu, X. Ji, “Expected values of functions of fuzzy variables”, Journal of Intelligent and Fuzzy Systems, Volume 17, pp. 471-478, 2006.