Integration of a Real-Time Stochastic Routing Optimization Software with an Enterprise Resource Planner

  • Pedro J. S. CardosoEmail author
  • Gabriela Schütz
  • Jorge Semião
  • Jânio Monteiro
  • João Rodrigues
  • Andriy Mazayev
  • Emanuel Ey
  • Micael Viegas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 582)


In order to manage their activities in a centralized manner, an Enterprise Resource Planning (ERP) software is a fundamental tool to many companies. As a generic software, many times it’s necessary to add new functionalities to the ERP in order to improve and to adapt/suite it to the companies’ processes. The Intelligent Fresh Food Fleet Router (i3FR) project aims to meet the needs expressed by several companies, namely the usefulness of a tool that makes “intelligent” management of the food distribution logistics. This “intelligence” presupposes interconnection capacity of various platforms (e.g., fleet management, GPS, and logistics), and active communication between them in order to optimize and enable integrated decisions.

This paper presents a multi-layered architecture to integrate existing ERPs with a route optimization and a temperature data acquisition module. The optimization module is prepared to deal with dynamic scenarios, as new demands may appear during the optimization process and the routes will admit several states (e.g., open, locked and closed), according with the ERP manager instructions. The data aquisition module implements the retrieve of some vehicles parameters (e.g., chambers’ temperatures and vehicle’s global positioning system data), used to validate the routes and provide information to the company’s manager.

A distribution company was selected as case-study, having up to 5000 daily deliveries and a fleet of 120 vehicles. The integration of the developed modules with the company’s ERP allowed the maintainance of most of the existing procedures, avoiding routines disruption.


Enterprise resource planning Vehicle routing problem Geographical information Application programming interface Data acquisition 



This work was partly supported by project i3FR: Intelligent Fresh Food Fleet Router – QREN I&DT, n. 34130, POPH, FEDER, the Portuguese Foundation for Science and Technology (FCT), project LARSyS PEstOE/EEI/LA0009/2013. We also thanks to project leader X4DEV, Business Solutions,


  1. 1.
    Abousaeidi, M., Fauzi, R., Muhamad, R.: Application of geographic information system (gis) in routing for delivery of fresh vegetables. In: 2011 IEEE Colloquium on Humanities, Science and Engineering (CHUSER), pp. 551–555. IEEE (2011)Google Scholar
  2. 2.
    Ambrosino, D., Sciomachen, A.: A food distribution network problem: a case study. IMA J. Manage. Math. 18(1), 33–53 (2007)CrossRefzbMATHGoogle Scholar
  3. 3.
    Ey, E., Schütz, G., Cardoso, P.J.S., Mazayev, A.: Solutions in under 10 seconds for vehicle routing problems with time windows using commodity computers. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9019, pp. 418–432. Springer, Heidelberg (2015)Google Scholar
  4. 4.
    Carić, T., Galić, A., Fosin, J., Gold, H., Reinholz, A.: A modelling and optimization framework for real-world vehicle routing problems. In: Caric, T., Gold, H. (eds.) Vehicle Routing Problem, pp. 15–34. InTech (2008)Google Scholar
  5. 5.
    Chen, H.K., Hsueh, C.F., Chang, M.S.: Production scheduling and vehicle routing with time windows for perishable food products. Comput. Oper. Res. 36(7), 2311–2319 (2009)CrossRefMathSciNetzbMATHGoogle Scholar
  6. 6.
    Faulin, J.: Applying MIXALG procedure in a routing problem to optimize food product delivery. Omega 31(5), 387–395 (2003)CrossRefGoogle Scholar
  7. 7.
    Glover, F., Laguna, M.: Tabu Search. Springer, New York (1999)Google Scholar
  8. 8.
    Hsu, C.I., Hung, S.F., Li, H.C.: Vehicle routing problem with time-windows for perishable food delivery. J. Food Eng. 80(2), 465–475 (2007)CrossRefGoogle Scholar
  9. 9.
    JSON: Javascript object notation, June 2015.
  10. 10., June 2015.
  11. 11.
    Magalhães Mendes, J.: A comparative study of crossover operators for genetic algorithms to solve the job shop scheduling problem. WSEAS Trans. Comput. 12(4), 164–173 (2013)Google Scholar
  12. 12.
    MongoDB, Inc.: MongoDB, June 2015.
  13. 13., June 2015.
  14. 14., June 2015.
  15. 15., June 2015.
  16. 16.
    OSRM: OSRM – Open Source Routing Machine, June 2015.
  17. 17.
    Osvald, A., Stirn, L.Z.: A vehicle routing algorithm for the distribution of fresh vegetables and similar perishable food. J. Food Eng. 85(2), 285–295 (2008).
  18. 18.
    Redmond, E., Wilson, J.R.: Seven databases in seven weeks: a guide to modern databases and the NoSQL movement. Pragmatic Bookshelf (2012)Google Scholar
  19. 19.
    Richardson, L., Ruby, S.: RESTful Web Services. O’Reilly Media Inc., Sebastopol (2008)Google Scholar
  20. 20.
    Routyn: Routyn., June 2015
  21. 21.
    Russell, R.A.: Hybrid heuristics for the vehicle routing problem with time windows. Transp. Sci. 29(2), 156–166 (1995)CrossRefzbMATHGoogle Scholar
  22. 22.
    SAGE, ERP X3: SAGE ERP X3., June 2015
  23. 23.
    Schrimpf, G., Schneider, J., Stamm-Wilbrandt, H., Dueck, G.: Record breaking optimization results using the ruin and recreate principle. J. Comput. Phys. 159(2), 139–171 (2000)CrossRefMathSciNetzbMATHGoogle Scholar
  24. 24.
    Solomon, M.M.: Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res. 35(2), 254–265 (1987)CrossRefMathSciNetzbMATHGoogle Scholar
  25. 25.
    Tan, K., Lee, L., Ou, K.: Artificial intelligence heuristics in solving vehicle routing problems with time window constraints. Eng. Appl. Artif. Intell. 14(6), 825–837 (2001)CrossRefGoogle Scholar
  26. 26.
    Tarantilis, C., Kiranoudis, C.: Distribution of fresh meat. J. Food Eng. 51(1), 85–91 (2002).
  27. 27.
    Thangiah, S.R.: A hybrid genetic algorithms, simulated annealing and tabu search heuristic for vehicle routing problems with time windows. Pract. Handb. Genet. Algorithms 3, 347–381 (1999)Google Scholar
  28. 28.
    Thangiah, S.R., Osman, I.H., Sun, T.: Hybrid genetic algorithm, simulated annealing and tabu search methods for vehicle routing problems with time windows. Technical report SRU CpSc-TR-94-27 69, Computer Science Department, Slippery Rock University (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Pedro J. S. Cardoso
    • 1
    • 2
    Email author
  • Gabriela Schütz
    • 1
    • 3
  • Jorge Semião
    • 1
    • 5
  • Jânio Monteiro
    • 1
    • 5
  • João Rodrigues
    • 1
    • 2
  • Andriy Mazayev
    • 4
  • Emanuel Ey
    • 1
  • Micael Viegas
    • 1
  1. 1.Instituto Superior de EngenhariaUniversity of the AlgarveFaroPortugal
  2. 2.LARSys, University of the AlgarveFaroPortugal
  3. 3.CEOT, University of the AlgarveFaroPortugal
  4. 4.Depart. de Eng. Eletrónica e InformáticaUniversity of the AlgarveFaroPortugal
  5. 5.INESC-IDLisbonPortugal

Personalised recommendations