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International Conference on Innovative Techniques and Applications of Artificial Intelligence

SGAI 2017: Artificial Intelligence XXXIV pp 300–312Cite as

Fuzzy Logic Based Personalized Task Recommendation System for Field Services

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10630))

Abstract

Within service providing industries, field service resources often follow a schedule that is produced centrally by a scheduling system. The main objective of such systems is to fully utilize the resources by increasing the number of completed tasks while reducing operational costs. Existing off the shelf scheduling systems started to incorporate the resources’ preferences and experience which although being implicit knowledge, are recognized as important drivers for service delivery efficiency. One of the scheduling systems that currently operates at BT allocates tasks interactively with a subset of empowered engineers. These engineers can select the tasks they think relevant for them to address along the working period. In this paper, we propose a fuzzy logic based personalized recommendation system that recommends tasks to the engineers based on their history of completed tasks. By analyzing the past data, we observe that the engineers indeed have distinguishable preferences that can be identified and exploited using the proposed system. We introduce a new evaluation measure for evaluating the proposed recommendations. Experiments show that the recommended tasks have up to 100% similarity to the previous tasks chosen by the engineers. Personalized recommendation systems for field service engineers have the potential to help understand how the field engineers react as the workstack evolves and new tasks come in, and to ultimately improve the robustness of service delivery.

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References

  1. Kern, M., Shakya, S., Owusu, G.: Integrated resource planning for diverse workforces. In: 2009 International Conference on Computers & Industrial Engineering CIE, pp. 1169–1173. IEEE (2009)

    Google Scholar 

  2. Mohamed, A., Hagras, H., Shakya, S., Liret, A., Dorne, R., Owusu, G.: Hierarchical type-2 fuzzy logic based real time dynamic operational planning system. In: Bramer, M., Petridis, M. (eds.) Research and Development in Intelligent Systems XXXI, pp. 255–267. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12069-0_19

    Google Scholar 

  3. Voudouris, C., Owusu, G., Dorne, R., Lesaint, D.: Service Chain Management: Technology Innovation For The Service Business. Springer Science & Business Media, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75504-3

    Google Scholar 

  4. Haugen, D.L., Hill, A.V.: Scheduling to improve field service quality. Decis. Sci. 30(3), 783–804 (1999)

    Article  Google Scholar 

  5. Petrakis, I., Hass, C., Bichler, M.: On the impact of real-time information on field service scheduling. Decis. Support Syst. 53(2), 282–293 (2012)

    Article  Google Scholar 

  6. Collins, J.E., Sisley, E.M.: Automated assignment and scheduling of service personnel. IEEE Expert 9(2), 33–39 (1994)

    Article  Google Scholar 

  7. Alsheddy, A., Tsang, E.P.: Empowerment scheduling for a field workforce. J. Sched. 14(6), 639–654 (2011)

    Article  MathSciNet  Google Scholar 

  8. Bobadilla, J., Ortega, F., Hernando, A., Gutirrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013). http://www.sciencedirect.com/science/article/pii/S0950705113001044

    Article  Google Scholar 

  9. Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)

    Article  Google Scholar 

  10. Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_12

    Chapter  Google Scholar 

  12. Sharma, L., Gera, A.: A survey of recommendation system: research challenges. Int. J. Eng. Trends Technol. (IJETT) 4(5), 1989–1992 (2013)

    Google Scholar 

  13. Trewin, S.: Knowledge-based recommender systems. Encycl. Libr. Inf. Sci. 69(32), 180–200 (2000)

    Google Scholar 

  14. Felfernig, A., Friedrich, G., Jannach, D., Zanker, M.: Developing constraint based recommenders. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 187–215. Springer, Heidelberg (2011). https://doi.org/10.1007/978-0-387-85820-3_6

    Chapter  Google Scholar 

  15. Wu, D., Zhang, G., Lu, J.: A fuzzy preference tree-based recommender system for personalized business-to-business e-services. IEEE Trans. Fuzzy Syst. 23(1), 29–43 (2015)

    Article  Google Scholar 

  16. Zenebe, A., Norcio, A.F.: Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems. Fuzzy Sets Syst. 160(1), 76–94 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  17. Yager, R.R.: Fuzzy logic methods in recommender systems. Fuzzy Sets Syst. 136(2), 133–149 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  18. Martinez, L., Barranco, M.J., Perez, L.G., Espinilla, M.: A knowledge based recommender system with multi granular linguistic information. Int. J. Comput. Intell. Syst. 1(3), 225–236 (2008)

    Article  MATH  Google Scholar 

  19. Ojokoh, B., Omisore, M., Samuel, O., Ogunniyi, T.: A fuzzy logic based personalized recommender system. Int. J. Comput. Sci. Inf. Technol. Secur. 2(5), 1008–1015 (2012)

    Google Scholar 

  20. Parra, D., Amatriain, X.: Walk the talk. In: Konstan, Joseph A., Conejo, R., Marzo, José L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 255–268. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22362-4_22. http://dl.acm.org/citation.cfm?id=2021855.2021878

    Chapter  Google Scholar 

  21. Zhang, Z., Lin, H., Liu, K., Wu, D., Zhang, G., Lu, J.: A hybrid fuzzy-based personalized recommender system for telecom products/services. Inf. Sci. 235, 117–129 (2013)

    Article  Google Scholar 

  22. Herrera-Viedma, E., Porcel, C., Lopez-Herrera, A.G., Alonso, S.: A fuzzy linguistic recommender system to advice research resources in university digital libraries. In: Bustince, H., Herrera, F., Montero, J. (eds.) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, vol. 220, pp. 567–585. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  23. Del Olmo, F.H., Gaudioso, E.: Evaluation of recommender systems: a new approach. Expert Syst. Appl. 35(3), 790–804 (2008)

    Article  Google Scholar 

  24. Bilgin, A., Hagras, H., Van Helvert, J., Alghazzawi, D.: A linear general type-2 fuzzy-logic-based computing with words approach for realizing an ambient intelligent platform for cooking recipe recommendation. IEEE Trans. Fuzzy Syst. 24(2), 306–329 (2016)

    Article  Google Scholar 

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Acknowledgments

This study is partially supported by the Marie Curie Initial Training Network (ITN) ESSENCE, grant agreement no. 607062.

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Authors and Affiliations

  1. Business Modelling and Operational Transformation Practice, British Telecom, Ipswich, UK

    Ahmed Mohamed & Gilbert Owusu

  2. ILLC, University of Amsterdam, Amsterdam, Netherlands

    Aysenur Bilgin

  3. BMOT Research, BT France, Paris, France

    Anne Liret

Authors
  1. Ahmed Mohamed
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  2. Aysenur Bilgin
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  3. Anne Liret
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  4. Gilbert Owusu
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Corresponding author

Correspondence to Anne Liret .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, United Kingdom

    Max Bramer

  2. Middlesex University , London, United Kingdom

    Miltos Petridis

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Mohamed, A., Bilgin, A., Liret, A., Owusu, G. (2017). Fuzzy Logic Based Personalized Task Recommendation System for Field Services. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXIV. SGAI 2017. Lecture Notes in Computer Science(), vol 10630. Springer, Cham. https://doi.org/10.1007/978-3-319-71078-5_26

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  • DOI: https://doi.org/10.1007/978-3-319-71078-5_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71077-8

  • Online ISBN: 978-3-319-71078-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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