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Optimizing Routes for Medicine Distribution Using Team Ant Colony System

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Abstract

Distributing medicine using multiple deliverymen in big hospitals is considered complex and can be viewed as a Multiple Traveling Salesman Problem (MTSP). MTSP problems aim to minimize the total displacement of the salesmen, in which all intermediate nodes should be visited only once. The Team Ant Colony Optimization (TACO) can be used to solve this sort of problem. The goal is to find multiple routes with similar lengths to make the delivery process more efficient. Thus, we can map this objective in two different fitness functions: minimizing the longest route, aiming to be fair in the allocation of the workload to all deliverymen; and decreasing the total cost of routes, seeking to reduce the overall workload of the deliverymen. However, these objectives are conflicting. This work proposes the use of swarm optimizers to improve the performance of the TACO concerning these two objectives. The results using global optimizers for the parameters far outperformed the original TACO for the case study.

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References

  1. Barbosa, D.F., Silla Jr, C.N., Kashiwabara, A.Y.: Aplicaçao da otimizaçao por colônia de formigas ao problema de múltiplos caixeiros viajantes no atendimento de ordens de serviço nas empresas de distribuiçao de energia elétrica. Anais do XI Simpósio Brasileiro de Sistemas de Informaç ao, pp. 23–30 (2015)

    Google Scholar 

  2. Bastos Filho, C.J., de Lima Neto, F.B., Lins, A.J., Nascimento, A.I., Lima, M.P.: A novel search algorithm based on fish school behavior. In: 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008, pp. 2646–2651. IEEE (2008)

    Google Scholar 

  3. Bektas, T.: The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega 34(3), 209–219 (2006)

    Article  MathSciNet  Google Scholar 

  4. Carter, A.E., Ragsdale, C.T.: A new approach to solving the multiple traveling salesperson problem using genetic algorithms. Eur. J. Oper. Res. 175(1), 246–257 (2006)

    Article  MathSciNet  Google Scholar 

  5. Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)

    Article  Google Scholar 

  6. Dorigo, M., de Oca, M.A.M., Engelbrecht, A.: Particle swarm optimization. Scholarpedia 3(11), 1486 (2008)

    Article  Google Scholar 

  7. Durillo, J.J., Nebro, A.J.: jMetal: a java framework for multi-objective optimization. Adv. Eng. Softw. 42(10), 760–771 (2011)

    Article  Google Scholar 

  8. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)

    Article  Google Scholar 

  9. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  10. Kennedy, J., Eberhart, R.C.: The particle swarm: social adaptation in information-processing systems. In: New Ideas in Optimization, pp. 379–388. McGraw-Hill Ltd., London (1999)

    Google Scholar 

  11. Liu, W., Li, S., Zhao, F., Zheng, A.: An ant colony optimization algorithm for the multiple traveling salesmen problem. In: 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, pp. 1533–1537. IEEE (2009)

    Google Scholar 

  12. Martello, S., Toth, P.: Knapsack Problems: Algorithms and Computer Implementations. Wiley, New York (1990)

    MATH  Google Scholar 

  13. Somhom, S., Modares, A., Enkawa, T.: Competition-based neural network for the multiple travelling salesmen problem with minmax objective. Comput. Oper. Res. 26(4), 395–407 (1999)

    Article  MathSciNet  Google Scholar 

  14. Tang, L., Liu, J., Rong, A., Yang, Z.: A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan iron & steel complex. Eur. J. Oper. Res. 124(2), 267–282 (2000)

    Article  Google Scholar 

  15. Vallivaara, I.: A team ant colony optimization algorithm for the multiple travelling salesmen problem with minmax objective. In: Proceedings of the 27th IASTED International Conference on Modelling, Identification and Control, pp. 387–392. ACTA Press (2008)

    Google Scholar 

  16. Wang, X., Wang, S., Bi, D., Ding, L.: Hierarchical wireless multimedia sensor networks for collaborative hybrid semi-supervised classifier learning. Sensors 7(11), 2693–2722 (2007)

    Article  Google Scholar 

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Correspondence to Carmelo J. A. Bastos-Filho .

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Alencar, R.C., Santana, C.J., Bastos-Filho, C.J.A. (2020). Optimizing Routes for Medicine Distribution Using Team Ant Colony System. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_5

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