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A Literature Review of Mathematical Programming Applications in the Fresh Agri-Food Supply Chain

  • Helio Yochihiro FuchigamiEmail author
  • Maico Roris Severino
  • Lie Yamanaka
  • Meire Ramalho de Oliveira
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 281)

Abstract

This work presents a literature review of mixed integer programming (MIP) applications in the fresh agri-food supply chain (ASC). The agricultural products attract great attention because it is an issue related to public health, subject to strict regulations and inspections. Due to the strong need to increase the efficiency of the supply chain, the planning models have become of increasing importance to producers and companies. Among the concerns of ASC problems, perishability is very critical for horticultural products, whose shelf life is significantly lower than of traditional crops, like grains and other vegetables. This research comes up first with a tertiary study of ASC publications, i.e., a review of reviews to investigate the core themes that have been studied. Thus, a secondary study on the primary literature was conducted. The use of mathematical models for applications in crop planning can be found since the early 1950 years, even in a tenuous way, but it became more widespread during the decade of 1980, with growing interest in the 1990s. In particular, the perishability of fresh products and risk management are two themes which have gained importance and visibility among recent research and publications, highlighting the relevance of the present study.

Keywords

Agri-food supply chain Fresh food delivery Mixed integer programming 

Notes

Acknowledgements

This work was supported by Newton Fund (British Council), CNPq (National Council of Technological and Scientific Development, Brazil) and FAPEG (Goias State Research Foundation, Brazil).

References

  1. 1.
    Abedinnia, H., Glock, C.H., Schneider, M., Grosse, E.H.: Machine scheduling problems in production: a tertiary study. Comput. Ind. Eng. (in press) (2017)Google Scholar
  2. 2.
    Ahumada, O., Villalobos, J.R.: Applications of planning models in the agri-food supply chain: a review. Eur. J. Oper. Res. 195, 1–20 (2009)CrossRefGoogle Scholar
  3. 3.
    Ahumada, O., Villalobos, J.R.: A tactical model for planning the production and distribution of fresh produce. Ann. Oper. Res. 190, 339–358 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Ahumada, O., Villalobos, J.R.: Operational model for planning the harvest and distribution of perishable agricultural products. Int. J. Prod. Econ. 133, 677–687 (2011)CrossRefGoogle Scholar
  5. 5.
    Apaiah, R.K., Hendrix, E.M.T.: Design of a supply chain network for pea-based novel protein foods. J. Food Eng. 70, 383–391 (2005)CrossRefGoogle Scholar
  6. 6.
    Aramyan, C., Ondersteijn, O., van Kooten, O., Lansink, A.O.: Performance indicators in agri-food production chains. In: Quantifying the Agri-Food Supply Chain, pp. 49–66. Springer, Netherlands, Chapter 5 (2006)Google Scholar
  7. 7.
    Bixby, R., Fenelon, M., Gu, Z., Rothber, E., Wunderling, R.: MIP: theory and practice—closing the gap. In: Powell, M., Scholtes, S. (eds.) System Modeling and Optimization: Methods, Theory and Applications. Kluwer Academic Publishers, Hingham (2000)Google Scholar
  8. 8.
    Blanco, V., Carpente, L., Hinojosa, Y., Puerta, J.: Planning for agricultural forage harvesters and trucks: model, heuristics, and case study. Netw. Spat. Econ. 10(3), 321–343 (2010)CrossRefGoogle Scholar
  9. 9.
    Blanco, A.M., Masini, G., Petracci, N., Bandoni, J.A.: Operations management of a packaging plant in the fruit industry. J. Food Eng. 70, 299–307 (2005)CrossRefGoogle Scholar
  10. 10.
    Broekmeulen, R.A.C.M.: Operations management of distribution centers for vegetables and fruits. Int. Trans. Oper. Res. 5(6), 501–508 (1998)CrossRefGoogle Scholar
  11. 11.
    Carpente, L., Casas-Méndez, B., Jácome, C., Puerto, J.: A model and two heuristic approaches for a forage harvester planning problem: a case study. Top 10, 122–139 (2010)CrossRefGoogle Scholar
  12. 12.
    Ferrer, J.C., MacCawley, A., Maturana, S., Toloza, S., Vera, J.: An optimization approach for scheduling wine grape harvest operations. Int. J. Prod. Econ. 112(2), 985–999 (2008)Google Scholar
  13. 13.
    Ganeshkumar, C., Pachayappan, M., Madanmohan, G.: Agri-food supply chain management: literature review. Intell. Inform. Manag. 9, 68–96 (2017)Google Scholar
  14. 14.
    Glen, J.J.: Mathematical-models in farm-planning – a survey. Oper. Res. 35(5), 641–666 (1987)CrossRefGoogle Scholar
  15. 15.
    Handayati, Y., Simatupang, T.M., Perdana, T.: Agri-food supply chain coordination: the state-of-the-art and recent developments. Logistics Res 8(5), 1–15 (2015)Google Scholar
  16. 16.
    Higgins, A.: Scheduling of road vehicles in sugarcane transport: a case study at an Australian sugar mill. Eur. J. Oper. Res. 170, 987–1000 (2006)CrossRefGoogle Scholar
  17. 17.
    Kader, A.A.: Postharvest Technology of Horticultural Crops. ANR, Oakland (2002)Google Scholar
  18. 18.
    Kusumastuti, R.D., Donk, D.P., Teunter, R.: Crop-related harvesting and processing planning: a review. Int. J. Prod. Econ. 174, 76–92 (2016)CrossRefGoogle Scholar
  19. 19.
    Lowe, T.J., Preckel, P.V.: Decision technologies for agribusiness problems: a brief review of selected literature and a call for research. Manuf. Serv. Oper. Manag. 6(3), 201–208 (2004)CrossRefGoogle Scholar
  20. 20.
    Lucas, M.T., Chhajed, D.: Applications of location analysis in agriculture: a survey. J. Oper. Res. Soc. 55(6), 561–578 (2004)CrossRefGoogle Scholar
  21. 21.
    Maia, L.O.A., Lago, R.A., Qassim, R.Y.: Selection of postharvest technology routes by mixed-integer linear programming. Int. J. Prod. Econ. 49, 85–90 (1997)CrossRefGoogle Scholar
  22. 22.
    Mason, N., Flores, H., Villalobos, J.R., Ahumada, O.: Planning the planting, harvest, and distribution of fresh horticultural products. In: Plà-Aragonés, L.M.: Handbook of Operations Research in Agriculture and the Agri-Food Industry, pp. 19–54. Springer Science, New York, Chapter 3 (2015)Google Scholar
  23. 23.
    Paam, P., Berretta, R., Heydar, M., Middleton, R.H., García-Flores, R., Juliano, P.: Planning models to optimize the agri-fresh food supply chain for loss minimization: a review. In: Reference Module in Food Science, pp. 19–54. Elsevier (2016)Google Scholar
  24. 24.
    Quadra, P.R.B., Rodriguez, J.M., Terol, M.C.R., Cruz, D.E.: A three level multi-period multi-location and multi-crop sustainable supply chain model. In: IEEE International Conference on Industrial Engineering and Engineering Management (2009)Google Scholar
  25. 25.
    Rantala, J.: Optimizing the supply chain strategy of a multi-unit Finnish nursery company. Silva Fennica 38(2), 203–215 (2004)CrossRefGoogle Scholar
  26. 26.
    Rong, A., Akkerman, R., Grunow, M.: An optimization approach for managing fresh food quality throughout the supply chain. Int. J. Prod. Econ. 131, 421–429 (2011)CrossRefGoogle Scholar
  27. 27.
    Salin, V.: Information technology in agri-food supply chains. Int. Food Agribus. Manag. Rev. 1(3), 329–334 (1998)CrossRefGoogle Scholar
  28. 28.
    Septiani, W., Marimin, M., Herdiyeni, Y., Haditjaroko, L.: Method and approach mapping for agri-food supply chain risk management: a literature review. Int. J. Supply Chain Manag. 5(2), 51–64 (2016)Google Scholar
  29. 29.
    Siddh, M.M., Soni, G., Jain, R., Sharma, M.K., Yadav, V.: Agri-fresh food supply chain quality (AFSCQ): a literature review. Ind. Manage. Data Syst. 117(9), 2015–2044 (2017)Google Scholar
  30. 30.
    Soto-Silva, W.E., González-Araya, M.C., Oliva-Fernández, M.A., Plà-Aragonés, L.M.: Optimizing fresh food logistics for processing: application for a large Chilean apple supply chain. Comput. Electron. Agric. 136, 42–57 (2017)CrossRefGoogle Scholar
  31. 31.
    Stray, B.J., Vuuren, J.H., Bezuidenhout, C.N.: An optimisation-based seasonal sugarcane harvest scheduling decision support system for commercial growers in South Africa. Comput. Electron. Agric. 18, 21–31 (2012)CrossRefGoogle Scholar
  32. 32.
    Tavella, E., Hjortsø, C.N.: Enhancing the design and management of a local organic food supply chain with soft systems methodology. Int. Food Agribus. Manag. Rev. 15(2), 47–68 (2012)Google Scholar
  33. 33.
    Tsolakis, N.K., Keramydas, C.A., Toka, A.K., Aidonis, D.A., Iakovou, E.T.: Agrifood supply chain management: a comprehensive hierarchical decision-making framework and a critical taxonomy. Biosys. Eng. 120, 47–64 (2014)CrossRefGoogle Scholar
  34. 34.
    Tsolakis, N.K., Keramydas, C.A., Toka, A.K., Aidonis, D.A., Iakovou, E.T.: Supply chain management for the agri-food sector: a critical taxonomy. In: 2nd International Conference on Supply Chains (2012)Google Scholar
  35. 35.
    Yandra, A., Marimin, M., Jamaran, I., Tamura, H.: An integration of multi-objective genetic algorithm and fuzzy logic for optimization of agroindustrial supply chain design. In: Annual Meeting of the ISSS (2007)Google Scholar
  36. 36.
    Zhang, W., Wilhelm, W.E.: OR/MS decision support models for the specialty crops industry: a literature review. Ann. Oper. Res. 190(1), 131–148 (2011)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Zhao, X., Lv, Q.: Optimal design of agri-food chain network: an improved particle swarm optimization approach. In: International Conference on Management and Service Science (2011)Google Scholar
  38. 38.
    Zuo, M., Kuo, W., McRoberts, K.L.: Application of mathematical programming to a large-scale agricultural production and distribution system. J. Oper. Res. Soc. 43(8), 639–648 (1991)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Federal University of Goias (UFG)Aparecida de GoianiaBrazil

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