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A Hybrid Method for Optimizing Shopping Lists Oriented to Retail Store Costumers

  • Conference paper
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International Joint Conference SOCO’16-CISIS’16-ICEUTE’16 (SOCO 2016, CISIS 2016, ICEUTE 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 527))

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Abstract

In the present day, one of the most common activities of everyday life is going to a supermarket or similar retail spaces to buy groceries. Many consumers organizations like The European Consumer Organization [1], advise buyers to prepare a “grocery list” in order to be ready for this activity. The present work proposes a system that helps to develop this activity in several ways: Firstly, it enables the user to create lists with different levels of abstraction: from concrete products to generic ones (or families of products). Secondly, the lists are collaborative and can be shared with other users. Finally, it automatically determines the best store to buy a given product using the proposed optimization algorithm. Furthermore, the optimization algorithm assigns a part of the list to each user balancing the cost that every user has to pay and choosing the cheapest supermarket where they have to buy.

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References

  1. T.E.C. Organization: The European consumer organization (2016). http://www.beuc.eu/

  2. Ver Ploeg, M., Mancino, L., Todd, J.E., Clay, D.M., Benjamin, Scharadin: Where do Americans usually shop for food and how do they travel to get there? Initial Findings From the National Household Food Acquisition and Purchase Survey, U.S.D.o. Agriculture, Editor. Economic Research Service (2015)

    Google Scholar 

  3. Miller, M.A., et al.: Food purchasing habits of participants in the supplemental nutrition assistance program (SNAP) among shelby county, TN residents. J. Acad. Nutr. Diet. 115(9, Supplement), A76 (2015)

    Google Scholar 

  4. Behrens, J.H., et al.: Consumer purchase habits and views on food safety: a Brazilian study. Food Control 21(7), 963–969 (2010)

    Article  Google Scholar 

  5. Feo, T.A., Resende, M.G.C.: Greedy Randomized Adaptive Search Procedures. J. Global Optim. 6(2), 109–133 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  6. Festa, P., Resende, M.G.C.: An annotated bibliography of GRASP - part I: algorithms. Int. Trans. Oper. Res. 16(1), 1–24 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  7. Festa, P., Resende, M.G.C.: An annotated bibliography of GRASP-part II: applications. Int. Trans. Oper. Res. 16(2), 131–172 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  8. Resende, M.G.C.: Metaheuristic hybridization with greedy randomized adaptive search procedures. Tutorials Oper. Res. 295–319 (2008)

    Google Scholar 

  9. Hansen, P., Mladenović, N.: Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130(3), 449–467 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  10. Mladenovic, N.: A variable neighborhood algorithm - a new metaheuristics for combinatorial optimization. In: Abstract of Papers Presented at Optimization Days, Montreal Canada, p. 112 (1995)

    Google Scholar 

  11. Mladenovic, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  12. Nguyen, V.-P., Prins, C., Prodhon, C.: Solving the two-echelon location routing problem by a GRASP reinforced by a learning process and path relinking. Eur. J. Oper. Res. 216(1), 113–126 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  13. Villegas, J.G., et al.: GRASP/VND and multi-start evolutionary local search for the single truck and trailer routing problem with satellite depots. Eng. Appl. Artif. Intell. 23(5), 780–794 (2010)

    Article  Google Scholar 

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Acknowledgments

This work was partially supported by FEDER, the Spanish Ministry of Economy and Competitiveness (Project ECO2013-47129-C4-3-R) and the Regional Government of Castilla y León (Project BU329U14), Spain.

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Authors and Affiliations

  1. University of Burgos, Burgos, Spain

    Santiago Porras & Bruno Baruque

Authors
  1. Santiago Porras
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  2. Bruno Baruque
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Correspondence to Bruno Baruque .

Editor information

Editors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Manuel Graña

  2. University of the Basque Country, Vitoria, Spain

    José Manuel López-Guede

  3. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Oier Etxaniz

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Porras, S., Baruque, B. (2017). A Hybrid Method for Optimizing Shopping Lists Oriented to Retail Store Costumers. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-47364-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47363-5

  • Online ISBN: 978-3-319-47364-2

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