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A Real Case Study of Mutualisation Problems for Non-medical Products Distribution

  • Abderrahman AbbassiEmail author
  • Said Kharraja
  • Ahmed Elhilali Alaoui
  • Denis Parra
Conference paper
Part of the Lecture Notes in Intelligent Transportation and Infrastructure book series (LNITI)

Abstract

One of the important problems encountered by healthcare decision makers is distribution of non-medical products from storing centers to a set of healthcare establishments. The problem can described with different graphs and modeled with various mathematical formulations. In this study, we deal with a problem of non-medical products distribution known as a Mutualisation problem. Among three possible scenarios given by decision makers, we determine the best scenario for distributing these products. For simplifying, we report the principal assumptions and the setting problems of each case. We use a simulated annealing algorithm for solving theme and we give a comparative study of the obtained results for a real benchmark.

Keywords

Healthcare management Case study Mutualisation Location-Routing Simulated annealing 

Notes

Acknowledgements

We wish to thank the consulting company CERCLH, who allowed us to use their real benchmark instances. The use of such data has contributed significantly to the development of a credible real case study. Thanks are due to the team work for the very constructive comments and discussions.

References

  1. 1.
    Govindan, K., Jafarian, A., Khodaverdi, R., Devika, K.: Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. Int. J. Prod. Econ. 152, 9–28 (2014)Google Scholar
  2. 2.
    Ahn, J., De Weck, O., Geng, Y., Klabjan, D.: Column generation based heuristics for a generalized location routing problem with profits arising in space exploration. Eur. J. Oper. Res. 223(1), 47–59 (2012)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Carlsson, D., Rönnqvist, M.: Supply chain management in forestry—case studies at Södra Cell AB. Eur. J. Oper. Res. 163(3), 589–616 (2005)zbMATHGoogle Scholar
  4. 4.
    Gunpinar, S., Centeno, G.: An integer programming approach to the bloodmobile routing problem. Transp. Res. Part E: Logistics Trans. Rev. 86, 94–115 (2016)Google Scholar
  5. 5.
    Niakan, F., Rahimi, M.: A multi-objective healthcare inventory routing problem; a fuzzy possibilistic approach. Transp. Res. Part E: Logistics Trans. Rev. 80, 74–94 (2015)Google Scholar
  6. 6.
    Detti, P., Papalini, F., de Lara, G.Z.M.: A multi-depot dial-a-ride problem with heterogeneous vehicles and compatibility constraints in healthcare. Omega 70, 1–14 (2017)Google Scholar
  7. 7.
    Crainic, T.G., Perboli, G., Mancini, S., Tadei, R.: Two-echelon vehicle routing problem: a satellite location analysis. Procedia-Social and Behav. Sci. 2(3), 5944–5955 (2010)Google Scholar
  8. 8.
    Bala, K., Brcanov, D., Gvozdenović, N.: Two-echelon location routing synchronized with production schedules and time windows. CEJOR 25(3), 525–543 (2017)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Derbel, H., Jarboui, B., Hanafi, S., Chabchoub, H.: Genetic algorithm with iterated local search for solving a location-routing problem. Expert Syst. Appl. 39(3), 2865–2871 (2012)zbMATHGoogle Scholar
  10. 10.
    Nguyen, V.P., Prins, C., Prodhon, C.: A multi-start iterated local search with tabu list and path relinking for the two-echelon location-routing problem. Eng. Appl. Artif. Intell. 25(1), 56–71 (2012)Google Scholar
  11. 11.
    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)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Prodhon, C.: A hybrid evolutionary algorithm for the periodic location-routing problem. Eur. J. Oper. Res. 210(2), 204–212 (2011)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Cuda, R., Guastaroba, G., Speranza, M.G.: A survey on two-echelon routing problems. Comput. Oper. Res. 55, 185–199 (2015)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Prodhon, C., Prins, C.: A survey of recent research on location-routing problems. Eur. J. Oper. Res. 238(1), 1–17 (2014)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Drexl, M., Schneider, M.: A survey of variants and extensions of the location-routing problem. Eur. J. Oper. Res. 241(2), 283–308 (2015)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Samanlioglu, F.: A multi-objective mathematical model for the industrial hazardous waste location-routing problem. Eur. J. Oper. Res. 226(2), 332–340 (2013)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Niakan, F., Rahimi, M.: A multi-objective healthcare inventory routing problem; a fuzzy possibilistic approach. Transp. Res. Part E: Logistics Trans. Rev. 80, 74–94 (2015)Google Scholar
  18. 18.
    Solimanpur, M., Kamran, M.A.: Solving facilities location problem in the presence of alternative processing routes using a genetic algorithm. Comput. Ind. Eng. 59(4), 830–839 (2010)Google Scholar
  19. 19.
    Derbel, H., Jarboui, B., Hanafi, S., Chabchoub, H.: Genetic algorithm with iterated local search for solving a location-routing problem. Expert Syst. Appl. 39(3), 2865–2871 (2012)zbMATHGoogle Scholar
  20. 20.
    Govindan, K., Jafarian, A., Khodaverdi, R., Devika, K.: Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. Int. J. Prod. Econ. 152, 9–28 (2014)Google Scholar
  21. 21.
    Dalfard, V.M., Kaveh, M., Nosratian, N.E.: Two meta-heuristic algorithms for two-echelon location-routing problem with vehicle fleet capacity and maximum route length constraints. Neural Comput. Appl. 23(7–8), 2341–2349 (2013)Google Scholar
  22. 22.
    Caballero, R., González, M., Guerrero, F.M., Molina, J., Paralera, C.: Solving a multiobjective location routing problem with a metaheuristic based on tabu search. Application to a real case in Andalusia. Eur. J. Oper. Res. 177(3), 1751–1763 (2007)zbMATHGoogle Scholar
  23. 23.
    Wang, H., Du, L., Ma, S.: Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake. Transp. Res. Part E: Logistics Trans. Rev. 69, 160–179 (2014)Google Scholar
  24. 24.
    Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953)Google Scholar
  25. 25.
    Nagy, G., Salhi, S.: Heuristic algorithms for single and multiple depot vehicle routing problems with pickups and deliveries. Eur. J. Oper. Res. 162(1), 126–141 (2005)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Abderrahman Abbassi
    • 1
    Email author
  • Said Kharraja
    • 2
  • Ahmed Elhilali Alaoui
    • 1
  • Denis Parra
    • 3
  1. 1.Modeling and Scientific Calculus Laboratory, Faculty of Sciences and Technology of FezFezMorocco
  2. 2.Laboratory of Signal and Industrial Process AnalysisUniversity of Saint-EtienneRoanneFrance
  3. 3.Centre de Recherche et Compétences En Logistique Hospitalière CERCLHRoanneFrance

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