Advertisement

Journal of Heuristics

, Volume 25, Issue 4–5, pp 753–792 | Cite as

Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem

  • Haneen AlgethamiEmail author
  • Anna Martínez-Gavara
  • Dario Landa-Silva
Article

Abstract

The workforce scheduling and routing problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operational cost. One of the main obstacles in designing a genetic algorithm for this problem is selecting the best set of operators that enable better GA performance. This paper presents a novel adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems. A mix of problem-specific and traditional crossovers are evaluated by using an online learning process to measure the operator’s effectiveness. Best operators are given high application rates and low rates are given to the worse. Application rates are dynamically adjusted according to the learning outcomes in a non-stationary environment. Experimental results show that the combined performances of all the operators were better than using only one operator used in isolation. This study provided an important opportunity to advance the understanding of the best method to use crossover operators for this highly-constrained optimisation problem effectively.

Keywords

Genetic algorithms Adaptive algorithms Genetic operators Routing Scheduling Workforce planning 

References

  1. Algethami, H., Landa-Silva, D., Martinez-Gavara, A.: Selecting genetic operators to maximise preference satisfaction in workforce scheduling and routing problem. In: Proceedings of the 6th International Conference on Operations Research and Enterprise Systems (ICORES), Porto, Portugal, pp. 416–423 (2017)Google Scholar
  2. Algethami, H., Landa-Silva, D.: Diversity-based adaptive genetic algorithm for a workforce scheduling and routing problem. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1771–1778 (2017)Google Scholar
  3. Algethami, H., Pinheiro, R.L., Landa-Silva, D.: A genetic algorithm for a workforce scheduling and routing problem. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 927–934 (2016)Google Scholar
  4. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2), 235–256 (2002)CrossRefzbMATHGoogle Scholar
  5. Belluz, J., Gaudesi, M., Squillero, G., Tonda, A.: Operator selection using improved dynamic multi-armed bandit. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO ’15, pp. 1311–1317. ACM, New York (2015)Google Scholar
  6. Castillo-Salazar, J.A., Landa-Silva, D., Qu, R.: A greedy heuristic for workforce scheduling and routing with time-dependent activities constraints. In: International Conference on Operations Research and Enterprise Systems (ICORES), pp. 367–375. Scipress, Lisbon, Portugal (2015)Google Scholar
  7. Castillo-Salazar, J.A., Landa-Silva, D., Qu, R.: Workforce scheduling and routing problems: literature survey and computational study. Ann. Oper. Res. 239(1), 39–67 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  8. Chu, P.C., Beasley, J.E.: A genetic algorithm for the multidimensional knapsack problem. J. Heuristics 42(1), 63–86 (1998)CrossRefzbMATHGoogle Scholar
  9. Consoli, P., Yao, X.: Diversity-driven selection of multiple crossover operators for the capacitated Arc routing problem. In: Blum, C., Ochoa, G. (eds.) Evolutionary Computation in Combinatorial Optimisation: 14th European Conference, EvoCOP 2014, Granada, Spain, April 23–25, Revised Selected Papers, pp. 97–108. Springer, Berlin (2014)Google Scholar
  10. Contreras-Bolton, C., Gatica, G., Barra, C.R., Parada, V.: A multi-operator genetic algorithm for the generalized minimum spanning tree problem. Expert Syst. Appl. 50, 1–8 (2016)CrossRefGoogle Scholar
  11. Cowling, P., Colledge, N., Dahal, K., Remde, S.: The trade-off between diversity and quality for multi-objective workforce scheduling. In: Proceedings of the 6th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP’06, pp. 13–24. Springer, Berlin (2006)Google Scholar
  12. Črepinšek, M., Liu, S.-H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: A survey. ACM Comput. Surv. 45(3), 35:1–35:33 (2013)zbMATHGoogle Scholar
  13. Eiben, A.E., Horvath, M., Kowalczyk, W., Schut, M.C.: Reinforcement learning for online control of evolutionary algorithms. In: Engineering Self-Organising Systems: 4th International Workshop, ESOA 2006, Hakodate, Japan, May 9, 2006, Revised and Invited Papers, pp. 151–160. Springer, Berlin (2007)Google Scholar
  14. Fialho, Á., Da Costa, L., Schoenauer, M., Sebag, M.: Analyzing bandit-based adaptive operator selection mechanisms. Ann. Math. Artif. Intell. 60(1), 25–64 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  15. García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J. Heuristics 15(6), 617 (2008)CrossRefzbMATHGoogle Scholar
  16. Hartmann, S.: A competitive genetic algorithm for resource-constrained project scheduling. Naval Res. Logist. NRL 45(7), 733–750 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  17. Laesanklang, W., Landa-Silva, D.: Decomposition techniques with mixed integer programming and heuristics for home healthcare planning. Ann. Oper. Res. 169, 1–35 (2016)zbMATHGoogle Scholar
  18. Laesanklang, W., Lankaites-Pinheiro, R., Algethami, H., Landa-Silva, D.: Extended decomposition for mixed integer programming to solve a workforce scheduling and routing problem. In: de Werra, D., Parlier, G.H., Vitoriano, B. (eds.) Operations Research and Enterprise Systems: 4th International Conference, ICORES 2015: Revised Selected Papers, Volume 577 of Communications in Computer and Information Science, pp. 191–211. Springer, Lisbon (2015)Google Scholar
  19. Lassaigne, R., De Rougemont, M.: Logic and Complexity. Springer, New York (2012)zbMATHGoogle Scholar
  20. Li, K., Fialho, A., Kwong, S., Zhang, Q.: Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 18(1), 114–130 (2014)CrossRefGoogle Scholar
  21. Mankowska, D., Meisel, F., Bierwirth, C.: The home health care routing and scheduling problem with interdependent services. Health Care Manag. Sci. 17(1), 15–30 (2014)CrossRefGoogle Scholar
  22. Maturana, J., Saubion, F.: A compass to guide genetic algorithms. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) Proceedings of the Parallel Problem Solving from Nature—PPSN X: 10th International Conference, Dortmund, Germany, September 13–17, 2008, pp. 256–265. Springer, Berlin (2008)CrossRefGoogle Scholar
  23. Misir, M., Smet, P., Verbeeck, K., Vanden Berghe, G.: Security personnel routing and rostering: a hyper-heuristic approach. In: Proceedings of the 3rd International Conference on Applied Operational Research, vol. 3, Tadbir, pp. 193–205 (2011)Google Scholar
  24. Morrison, R.W., De Jong, K.A.: Measurement of population diversity. In: Artificial Evolution: 5th International Conference, Evolution Artificielle, EA 2001 Le Creusot, France, October 29–31, 2001 Selected Papers, pp. 31–41. Springer, Berlin (2002)Google Scholar
  25. Mutingi, M., Mbohwa, C.: Health-care staff scheduling in a fuzzy environment: a fuzzy genetic algorithm approach. In: Conference Proceedings (DFC Quality and Operations Management), International Conference on Industrial Engineering and Operations Management, pp. 303–312 (2014)Google Scholar
  26. Onieva, E., Osaba, E., Angulo, I., Moreno, A., Bahillo, A., Perallos, A.: Improvement of drug delivery routes through the adoption of multi-operator evolutionary algorithms and intelligent vans capable of reporting real-time incidents. IEEE Trans. Autom. Sci. Eng. 14(2), 1009–1019 (2017)CrossRefGoogle Scholar
  27. Osaba, E., Onieva, E., Carballedo, R., Diaz, F., Perallos, A.: An adaptive multi-crossover population algorithm for solving routing problems. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2013), pp. 113–124. Springer (2014)Google Scholar
  28. Osaba, E., Onieva, E., Carballedo, R., Diaz, F., Perallos, A., Zhang, X.: A multi-crossover and adaptive island based population algorithm for solving routing problems. J. Zhejiang Univ. Sci. C 14(11), 815–821 (2013)CrossRefGoogle Scholar
  29. Panait, L., Luke, S.: Cooperative multi-agent learning: the state of the art. Auton. Agents Multi-agent Syst. 11(3), 387–434 (2005)CrossRefGoogle Scholar
  30. Pinheiro, R.L., Landa-Silva, D., Atkin, J.: A variable neighbourhood search for the workforce scheduling and routing problem. In: Advances in Nature and Biologically Inspired Computing, pp. 247–259 . Springer, Pietermaritzburg, South Africa (2016)Google Scholar
  31. Puljić, K., Manger, R.: Comparison of eight evolutionary crossover operators for the vehicle routing problem. Math. Commun. 18(2), 359–375 (2013)MathSciNetzbMATHGoogle Scholar
  32. Rasmussen, M.S., Justesen, T., Dohn, A., Larsen, J.: The home care crew scheduling problem: preference-based visit clustering and temporal dependencies. Eur. J. Oper. Res. 219(3), 598–610 (2012)CrossRefzbMATHGoogle Scholar
  33. Spears, W.M.: Adapting crossover in evolutionary algorithms. In: Proceedings of the Fourth Annual Conference on Evolutionary Programming, pp. 367–384. MIT Press (1995)Google Scholar
  34. Thierens, D.: An adaptive pursuit strategy for allocating operator probabilities. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, GECCO ’05, pp. 1539–1546. ACM, New York (2005)Google Scholar
  35. Tuson, A.L.: Adapting operator probabilities in genetic algorithms. Technical report, Master’s thesis, Evolutionary Computation Group, Department of Artificial Intelligence, Edinburgh University (1995)Google Scholar
  36. Whitacre, J.M., Pham, T.Q., Sarker, R.A.: Use of statistical outlier detection method in adaptive evolutionary algorithms. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO ’06, pp. 1345–1352. ACM, New York (2006)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Collage of Computers and Information TechnologyTaif UniversityTa’ifSaudi Arabia
  2. 2.Estadstica y Investigacin OperativaUniversidad de ValenciaValenciaSpain
  3. 3.ASAP Research Group, School of Computer ScienceThe University of NottinghamNottinghamUK

Personalised recommendations