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Periodic Route Optimization for FMCG Distributors

  • Onur Çopur
  • Mert Yıldız
  • Simru Göven
  • Ali Övünç Güneri
  • Alper Berke Yavuz
  • Mahmut Ali Gökçe
  • Cansu YurtsevenEmail author
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Izmir based Information Technology (IT) solution provider develops supply chain management software tools for many customers from fast moving consumer goods sector (FMCG). Customers of the FMCG sector needs to be visited a certain number of times in a given period by sales representatives. The company must decide which customers must be visited in which sequence by each sales representative while obeying visit frequency and time windows requirements. These decisions have significant impact on total cost. For this reason, finding an optimal route for every sales representative for each day of the planning horizon is important. This practically challenging and technically important problem can be described as periodic multiple depot traveling salesman problem with time windows (PMDTSPTW). We propose a novel mathematical model for the optimal solution of this problem. The proposed model minimizes the total distance traveled by sales representatives by deciding which sales representative will visit which customer on which day while following the time windows to collect demand data. The proposed model is applicable to any company from FMCG sector. The solution approach in this study is implemented and tested with real life including coordinates and location data of customers and sales representatives from Turkey’s largest beer distributor. The proposed model is solved using IBM ILOG CPLEX Optimization Studio version 12.8. The results show significant improvement over the current situation. To ensure efficient usage of the proposed approach, a user-friendly decision support system (DSS) is constructed and implemented.

Keywords

Periodicity Travelling salesman problem Time windows Mathematical programming Decision support system 

Notes

Acknowledgement

This study is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) in the program of “2209-B Undergraduate Research Projects for Industrial Applications Fellowship Program”. We would like to thank Ege Can Erdoğan, Doğacan TANIŞ and Zafer Yapıcıel for their support and contributions to this study.

References

  1. 1.
    Alinaghian, M.: A Navel Heuristic Algorithm for the Periodic Vehicle Routing Problem (2014)Google Scholar
  2. 2.
    Chu, F., Labadie, N., Prins, C.: The Periodic Capacitated Arc Routing Problem linear programming model, metaheuristic and lower bounds. J. Syst. Sci. Syst. Eng. 13, 423–435 (2004).  https://doi.org/10.1007/s11518-006-0174-yCrossRefGoogle Scholar
  3. 3.
    Clarke, G., Wright, J.W.: Scheduling of vehicles from a central depot to a number of delivery points. Oper. Res. 12, 568–581 (1964)CrossRefGoogle Scholar
  4. 4.
    Coene, S., Arnout, A., Spieksma, F.: On a periodic vehicle routing problem. J. Oper. Res. Soc. 61, 1719–1728 (2010). https://doi.org/10.1057/jors.2009.154CrossRefGoogle Scholar
  5. 5.
    Cordeau, J.-F., Gendreau, M., Laporte, G.: A Tabu Search heuristic for periodic and multi-depot vehicle routing problems. Networks 30, 105–119 (1997).  https://doi.org/10.1002/(sici)1097-0037(199709)30:23.3.co;2-nCrossRefzbMATHGoogle Scholar
  6. 6.
    Mancini, S.: A real-life multi depot multi period vehicle routing problem with a heterogeneous fleet: formulation and adaptive large neighborhood search based matheuristic. Transport. Res. Part C (2015).  https://doi.org/10.1016/j.trc.2015.06.016CrossRefGoogle Scholar
  7. 7.
    Pacheco, J., Alvarez, A., García, I., Ángel-Bello, F.: Optimizing vehicle routes in a bakery company allowing flexibility in delivery dates. J. Oper. Res. Soc. 63, 569–581 (2012). https://doi.org/10.1057/jors.2011.51CrossRefGoogle Scholar
  8. 8.
    Paletta, G.: The period traveling salesman problem: A new heuristic algorithm. Comput. Oper. Res. 29, 1343–1352 (2002).  https://doi.org/10.1016/s0305-0548(01)00035-1MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Tan, K.C., Lee, L.H., Zhu, K.Q., Qu, K.: Heuristic methods for vehicle routing problem with time windows. Artif. Intell. Eng. 15, 281–295 (2001)CrossRefGoogle Scholar
  10. 10.
    Thangiah, S.R.: A hybrid genetic algorithms, simulated annealing and tabu search heuristic for vehicle routing problems with time windows. In: Chambers, L. (ed.) Practical Handbook of Genetic Algorithms Complex Structures, vol. 3, pp. 347–381. CRC Press (1999)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Onur Çopur
    • 1
  • Mert Yıldız
    • 1
  • Simru Göven
    • 1
  • Ali Övünç Güneri
    • 1
  • Alper Berke Yavuz
    • 1
  • Mahmut Ali Gökçe
    • 1
  • Cansu Yurtseven
    • 1
    Email author
  1. 1.Industrial EngineeringYaşar UniversityBornovaTurkey

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