Losses in Transportation—Importance and Methods of Handling

  • Marcin AnholcerEmail author
  • Tomasz Hinc
  • Arkadiusz Kawa
Part of the EcoProduction book series (ECOPROD)


A smart supply network must be immune against the situations like losing the goods during the transportation process. Such losses may raise the necessity of increasing the number of deliveries and thus the use of fuel and pollution. The importance of this problem has been proved by the results of a quantitative research performed among Polish companies. A solution to this problem is to design a smart supply network with appropriate DSS (decision support system), based on relevant mathematical models and algorithms, that allow to reduce the number of multiple deliveries.


Perishable products Losses in transport Exclusions in transport DSS Quantitative research 



The paper was written with financial support from the National Center of Science (Narodowe Centrum Nauki)—the grant no. DEC-2014/13/B/HS4/01552.


  1. 1.
    Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1993). Network flows. Theory, algorithms and applications. New Jersey: Prentice Hall, Englewood Cliffs.Google Scholar
  2. 2.
    Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: a review. European Journal of Operational Research, 196(1), 1–20.CrossRefGoogle Scholar
  3. 3.
    Anholcer, M., & Kawa, A. (2017). Identyfikacja strat i braków powstających w transporcie - wyniki badań przeprowadzonych metodą jakościową [Identification of losses and faults in transport: results from qualitative research]. Gospodarka Materiałowa i Logistyka, 12(2017), 22–29.Google Scholar
  4. 4.
    Anholcer, M. (2017). Optymalizacja przewozów produktów szybko tracących wartość – modele i algorytmy [Optimization of transport of perishable products – models and algorithms]. Poznań: Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu.Google Scholar
  5. 5.
    Balachandran, V., & Thompson, G.L. (1974). A note on Computational simplifications in solving generalized transportation problems, by Glover and Klingman. Discussion Papers 66, Northwestern University, Center for Mathematical Studies in Economics and Management Science, Cited 28 July 2015.
  6. 6.
    Balas, E. (1966). The dual method for the generalized transportation problem. Management Science Series A (Sciences), 12(7), 555–568.Google Scholar
  7. 7.
    Balas, E., & Ivanescu, P. L. (1964). On the generalized transportation problem. Management Science Series A (Sciences), 11(1), 188–202.Google Scholar
  8. 8.
    Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (2010). Linear programming and network flows (4th ed.). Hoboken, New Jersey: Wiley.Google Scholar
  9. 9.
    Benders, J. F. (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4, 238–252.CrossRefGoogle Scholar
  10. 10.
    Bertsekas, D. P., & Tseng, P. (1988). Relaxation methods for minimum cost ordinary and generalized network flow problems. Operations Research, 36(1), 93–114.CrossRefGoogle Scholar
  11. 11.
    Choi, T.-M., & Chiu, C.-H. (2012). Mean-downside-risk and mean-variance newsvendor models: implications for sustainable fashion retailing. International Journal of Production Economics, 135(2), 552–560.CrossRefGoogle Scholar
  12. 12.
    Cohen, E., & Megiddo, N. (1994). New algorithms for generalized network flows. Mathematical Programming, 64, 325–336.CrossRefGoogle Scholar
  13. 13.
    Dangalchev, C. A. (1996). Partially-linear transportation problems. European Journal of Operational Research, 91, 623–633.CrossRefGoogle Scholar
  14. 14.
    Elam, J., Glover, F., & Klingman, D. (1979). A strongly convergent primal simplex algorithm for generalized networks. Mathematics of Operations Research, 4(1), 39–59.CrossRefGoogle Scholar
  15. 15.
    Glover, F., Karney, D., Klingman, D., & Napier, A. (1974). A computation study on start procedures, basis change criteria, and solution algorithms for transportation problems. Management Science, 20(5), 793–813.CrossRefGoogle Scholar
  16. 16.
    Glover, F., & Klingman, D. (1973). A note on computational simplifications in solving generalized transportation problems. Transportation Science, 7(4), 351–361.CrossRefGoogle Scholar
  17. 17.
    Glover, F., Klingman, D., & Napier, A. (1972). Basic dual feasible solutions for a class of generalized networks. Operations Research, 20(1), 126–136.CrossRefGoogle Scholar
  18. 18.
    Goldberg, A.V., Plotkin, S.A., Tardos, E. (1988). Combinatorial algorithms for the generalized circulation problem. In SFCS’88 Proceedings of the 29th Annual Symposium on Foundations of Computer Science (pp. 432–443).Google Scholar
  19. 19.
    Goldfarb, D., & Lin, Y. (2002). Combinatorial interior point methods for generalized network flow problems. Mathematical Programming Series A, 93, 227–246.CrossRefGoogle Scholar
  20. 20.
    Gottlieb, E. S. (2002). Solving generalized transportation problems via pure transportation problems. Naval Research Logistics, 49(7), 666–685.CrossRefGoogle Scholar
  21. 21.
    Gupta, R. (1978). Solving the generalized transportation problem with constraints. Zeitschrift für angewandte Mathematik und Mechanik, 58(10), 451–458.CrossRefGoogle Scholar
  22. 22.
    Jewell, W. S. (1962). Optimal flow through networks with gains. Operations Research, 10(4), 476–499.CrossRefGoogle Scholar
  23. 23.
    Johnson, E. L. (1966). Networks and basic solutions. Operations Research, 14(4), 619–623.CrossRefGoogle Scholar
  24. 24.
    Langley, R.W. (1973). Continuous and integer generalized network flow problems. Ph.D. thesis, Georgia Institute of Technology.Google Scholar
  25. 25.
    Laudon, K. C., & Laudon, J. P. (2006). Management information systems. Managing the digital firm (9th ed.). Upper Saddle River, NJ: Pearson-Prentice Hall.Google Scholar
  26. 26.
    Lourie, J. R. (1964). Topology and computation of the generalized transportation problem. Management Science Series A (Sciences), 11(1), 177–187.Google Scholar
  27. 27.
    Masoumi, A. H., Yu, M., & Nagurney, A. (2012). A supply chain generalized network oligopoly model for pharmaceuticals under brand differentiation and perishability. Transportation Research E, 48(4), 762–780.CrossRefGoogle Scholar
  28. 28.
    Nagurney, A., & Masoumi, A. H. (2012). Supply chain network design of a sustainable blood banking system. In T. Boone, V. Jayaraman, & R. Ganeshan (Eds.), Sustainable supply chains: Models, methods and public policy implications (pp. 49–72). London: Springer.CrossRefGoogle Scholar
  29. 29.
    Nagurney, A., Masoumi, A. H., & Yu, M. (2012). Supply chain network operations management of a blood banking system with cost and risk minimization. Computational Management Science, 9(2), 205–231.CrossRefGoogle Scholar
  30. 30.
    Nagurney, A., & Nagurney, L. S. (2012). Medical nuclear supply chain design: A tractable network model and computational approach. International Journal of Production Economics, 140(2), 865–874.CrossRefGoogle Scholar
  31. 31.
    Nagurney, A., & Yu, M. (2011). Fashion supply chain management through cost and time minimization from a network perspective. In T.-M. Choi (Ed.), Fashion supply chain management: Industry and business analysis (pp. 1–20). Hershey, Pennsylvania: IGI Global.Google Scholar
  32. 32.
    Nagurney, A., & Yu, M. (2012). Sustainable fashion supply chain management under oligopolistic competition and brand differentiation. International Journal of Production Economics, 135(2), 532–540.CrossRefGoogle Scholar
  33. 33.
    Nagurney, A., Yu, M., Masoumi, A. H., & Nagurney, L. (2013). Networks against time. Supply chain analytics for perishable products. New York: Springer.CrossRefGoogle Scholar
  34. 34.
    Nahmias, S. (1982). Perishable inventory theory: A review. Operations Research, 30(4), 680–708.CrossRefGoogle Scholar
  35. 35.
    Nielsen, S. S., & Zenios, S. A. (1993). Proximal minimizations with D-functions and the massively parallel solution of linear network programs. Computational Optimization and Applications, 1, 375–398.CrossRefGoogle Scholar
  36. 36.
    Patriksson, M. (2008). A survey on the continuous nonlinear resource allocation problem. European Journal of Operational Research, 185, 1–46.CrossRefGoogle Scholar
  37. 37.
    Qi, L. (1987). The A-Forest Iteration Method for the stochastic generalized transportation problem. Matematics of Operations Research, 12(1), 1–21.CrossRefGoogle Scholar
  38. 38.
    Restrepo, M., & Williamson, D. P. (2009). A simple GAP-canceling algorithm for the generalized maximum flow problem. Mathematical Programming Series A, 118, 47–74.CrossRefGoogle Scholar
  39. 39.
    Rong, A., Akkerman, R., & Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Economics, 131(1), 421–429.CrossRefGoogle Scholar
  40. 40.
    Rowse, J. (1981). Solving the generalized transportation problem. Regional Science and Urban Economics, 11, 57–68.CrossRefGoogle Scholar
  41. 41.
    Sikora, W. (2003). Algorytm generujących ścieżek dla zagadnienia rozdziału [Generating paths algorithm for generalized transportation problem]. In A. Całczyński (Ed.), Metody i zastosowania badań operacyjnych ’2002, Prace naukowe Politechniki Radomskiej (pp. 283–304).Google Scholar
  42. 42.
    Sikora, W. (2004). Algorytm indeksowy dla zagadnienia rozdziału z kryterium dochodu [Index algorithm for generalized transportation problem with income criterion]. In E Panek (Ed.), Matematyka w Ekonomii. Zeszyty Naukowe Akademii Ekonomicznej w Poznaniu (Vol. 41, pp. 361–375).Google Scholar
  43. 43.
    Sikora, W. (2008). Algorytm drzewa poprawy dla zagadnienia rozdziału z ograniczoną pojemnością˛ pól [Improvement tree algorithm for capacitated generalized transportation problem]. In W. Sikora (Ed.), Z prac Katedry Badań Operacyjnych. Zeszyty Naukowe Akademii Ekonomicznej w Poznaniu (Vol. 104, pp. 130–146).Google Scholar
  44. 44.
    Sikora, W. (2010). Dwuetapowe zagadnienie rozdziału z kryterium dochodu [Two-stage generalized transportation problem with income criterion]. In W. Sikora (Ed.), Z prac Katedry Badań Operacyjnych, Zeszyty Naukowe Akademii Ekonomicznej w Poznaniu (Vol. 138, pp. 60–76).Google Scholar
  45. 45.
    Sikora, W. (2012). Optymalizacja produkcji roślinnej jako nieliniowe zagadnienie rozdziału [Optimization of plant production as a nonlinear generalized transportation problem]. Metody Ilościowe w Badaniach Ekonomicznych, XII(1), 184–193.Google Scholar
  46. 46.
    Thompson, G. L., & Sethi, A. P. (1986). Solution of constrained generalized transportation problems using the pivot and probe algorithm. Computers & Operations Research, 13(1), 1–9.CrossRefGoogle Scholar
  47. 47.
    Waters, D. (2007). Supply chain risk management. Vulnerability and resilience in logistics. London, Philadelphia: Kogan Page Limited.Google Scholar
  48. 48.
    Wayne, K. D. (2002). A polynomial combinatorial algorithm for generalized minimum cost flow. Mathematics of Operations Research, 27(3), 445–459.CrossRefGoogle Scholar
  49. 49.
    Yu, M., & Nagurney, A. (2013). Competitive food supply chain networks with application to fresh produce. European Journal of Operational Research, 224(2), 273–282.CrossRefGoogle Scholar
  50. 50.
    Zanoni, S., & Zavanella, L. (2012). Chilled or frozen? Decision strategies for sustainable food supply chains. International Journal of Production Economics, 140(2), 731–736.CrossRefGoogle Scholar
  51. 51.
    Zenios, S. A. (1994). Data parallel computing for network-structured optimization problems. Computational Optimization and Applications, 3, 199–242.CrossRefGoogle Scholar
  52. 52.
    Zenios, S. A., & Censor, Y. (1991). Massively parallel row-action algorithms for some nonlinear transportation problems. SIAM Journal on Optimization, 1(3), 373–400.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Poznań University of Economics and BusinessPoznańPoland

Personalised recommendations