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A multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem

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Abstract

This paper proposes a constructive heuristic approach for the assembly line worker assignment and balancing problem (ALWABP). ALWABP arises when the operation time for every task differs according to the worker who executes the task. Since the operation times of tasks vary due to the workers, the problem requires a simultaneous solution to the double assignment problem. Tasks must be assigned to workers and workers to stations, concurrently. This problem is especially proposed in sheltered work centers for the disabled. However, it is not only important for the assembly lines with the disabled, but also for manually operated assembly lines with high labor turnover. In this paper, a multiple-rule based constructive randomized search (MRBCRS) algorithm is proposed in order to solve the ALWABP. Thirty nine task priority rules and four worker priority rules are defined. Performance of the proposed MRBCRS is compared with the relevant literature on benchmark data. Experimental results show that the proposed MRBCRS is very effective for benchmark problems. The results show that the algorithm improves upon the best-performing methods from the literature in terms of solution quality and time.

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References

  • Akyol, S. D., & Bayhan, G. M. (2011). ‘A particle swarm optimization algorithm for maximizing production rate and workload smoothness. In Third World Congress on Nature and Biologically Inspired Computing (NaBIC), IEEE, October, Spain (pp. 44–49).

  • Araujo, F. F. B., Costa, A. M., & Miralles, C. (2012). Two extensions for the ALWABP: Parallel stations and collaborative approach. International Journal of Production Economics, 140(1), 483–495. doi:10.1016/j.ijpe.2012.06.032.

    Article  Google Scholar 

  • Bansal, J. C., Singh, P. K., Saraswat, M., Verma, A., Jadon, S. S. & Abraham, A. (2011). ‘Inertia weight strategies in particle swarm optimization. In Third World Congress on Nature and Biologically Inspired Computing (NaBIC), IEEE, October, Spain (pp. 633–640).

  • Baykasoğlu, A. (2006). Multi-rule multi-objective simulated annealing algorithm for straight and U type assembly line balancing problems. Journal of Intelligent Manufacturing, 17(2), 217–232. doi:10.1007/s10845-005-6638-y.

    Article  Google Scholar 

  • Blum, C., & Miralles, C. (2011). On solving the assembly line worker assignment and balancing problem via beam search. Computers and Operations Research, 38(1), 328–39. doi:10.1016/j.cor.2010.05.008.

    Article  Google Scholar 

  • Borba, L., & Ritt, M. (2014). A heuristic and a branch-and-bound algorithm for the assembly line worker assignment and balancing problem. Computers and Operations Research, 45(1), 87–96. doi:10.1016/j.cor.2013.12.002.

    Article  Google Scholar 

  • Chaves, A. A., Miralles, C., & Lorena, L. A. N. (2007). Clustering search approach for the assembly line worker assignment and balancing problem. In Proceedings of the 37th International Conference on Computers and Industrial Engineering (pp. 1469–1478).

  • Chaves, A. A., Lorena, L. A. N., & Miralles, C. (2009). Hybrid metaheuristic for the assembly line worker assignment and balancing problem. Lecture Notes in Computer Science, 5818, 1–14.

    Article  Google Scholar 

  • Costa, A. M., & Miralles, C. (2009). Job rotation in assembly lines employing disabled workers. International Journal of Production Economics, 120(2), 625–632.

    Article  Google Scholar 

  • Guo, Z. X., Wong, W. K., Leung, S. Y. S., Fan, J. T., et al. (2008). A genetic-algorithm-based optimization model for solving the flexible assembly line balancing problem with work sharing and workstation revisiting. IEEE Transactions on Systems, Man and Cybernetics Part C—Applications and Reviews, 38(2), 218–228.

    Article  Google Scholar 

  • Guo, Z. X., Shi, L., Chen, L., & Liang, Y. (2015). A harmony search-based memetic optimization model for integrated production and transportation scheduling in MTO manufacturing. In OMEGA. doi:10.1016/j.omega.2015.10.012 (in press)

  • Hoffmann, T. R. (1990). Assembly line balancing: A set of challenging problems. International Journal of Production Research, 28, 1807–1815.

    Article  Google Scholar 

  • Karp, R. M. (1972). Reducibility among combinatorial problems. In R. E. Miller & J. W. Thatcher (Eds.), Complexity of computer applications (pp. 85–104). New York: Plenum Press.

    Chapter  Google Scholar 

  • Miralles, C., Garcia-Sabater, J. P., Andres, C., & Cardos, M. (2007). Advantages of assembly lines in sheltered work centres for disabled. A case study. International Journal of Production Economics, 110(1), 187–197. doi:10.1016/j.ijpe.2007.02.023.

    Article  Google Scholar 

  • Miralles, C., Garcia-Sabater, J. P., Andres, C., & Cardos, M. (2008). Branch and bound procedures for solving the assembly line worker assignment and balancing problem: Application to sheltered work centres for disabled. Discrete Applied Mathematics, 156(3), 352–367. doi:10.1016/j.dam.2005.12.012.

  • Moreira, M. C. O., & Costa, A. M. (2009). A minimalist yet efficient tabu search for balancing assembly lines with disabled workers. In Anais do XLI Simposio Brasileiro de Pesquisa Operacional, Porto Seguro, Brazil (pp. 660–671).

  • Moreira, M. C. O., Ritt, M., Costa, A. M., & Chaves, A. A. (2012). Simple heuristics for the assembly line worker assignment and balancing problem. Journal of Heuristics, 18(3), 505–524. doi:10.1007/s10732-012-9195-5.

    Article  Google Scholar 

  • Moreira, M. C. O., Miralles, C., & Costa, A. M. (2015). Model and heuristics for the assembly line worker integration and balancing problem. Computers and Operations Research, 54(1), 64–73. doi:10.1016/j.cor.2014.08.021.

    Article  Google Scholar 

  • Mutlu, O., Polat, O., & Supciller, A. A. (2013). An iterative genetic algorithm for the assembly line worker assignment and balancing problem of type-II. Computers and Operations Research, 40(1), 418–26. doi:10.1016/j.cor.2012.07.010.

    Article  Google Scholar 

  • Polat, O., Kalayci, C. B., Mutlu, O., & Gupta, S. M. (2015). A two-phase variable neighbourhood search algorithm for assembly line worker assignment and balancing problem type-II: an industrial case study. International Journal of Production Research, 54(3), 722–741. doi:10.1080/00207543.2015.1055344.

    Article  Google Scholar 

  • Ritt, M., Costa, A. M., & Miralles, C. (2015). The assembly line worker assignment and balancing problem with stochastic worker availability. International Journal of Production Research, 54(3), 907–922. doi:10.1080/00207543.2015.1108534.

    Article  Google Scholar 

  • Scholl, A., & Voß, S. (1996). Simple assembly line balancing–heuristic approaches. Journal of Heuristics, 2(3), 217–244. doi:10.1007/BF00127358.

    Article  Google Scholar 

  • Simaria, A. S., & Pedro, M. V. (2004). A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II. Computers and Industrial Engineering, 47(4), 391–407.

    Article  Google Scholar 

  • Sungur, B., & Yavuz, Y. (2014). Assembly line balancing with hierarchical worker assignment. Journal of Manufacturing Systems, 37, 290–298. doi:10.1016/j.jmsy.2014.08.004.

    Article  Google Scholar 

  • Vila, M., & Pereira, J. (2014). A branch-and-bound algorithm for assembly line worker assignment and balancing problems. Computers and Operations Research, 44(1), 105–114. doi:10.1016/j.cor.2013.10.016.

    Article  Google Scholar 

  • Zacharia, P Th, & Nearchou, A. C. (2016). A population-based algorithm for the bi-objective assembly line worker assignment and balancing problem. Engineering Applications of Artificial Intelligence, 49, 1–9.

    Article  Google Scholar 

  • Zaman, T., Paul, S. K., & Azeem, A. (2012). Sustainable operator assignment in an assembly line using genetic algorithm. International Journal of Production Research, 50(18), 5077–5084.

    Article  Google Scholar 

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Correspondence to Adil Baykasoğlu.

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Akyol, S.D., Baykasoğlu, A. A multiple-rule based constructive randomized search algorithm for solving assembly line worker assignment and balancing problem. J Intell Manuf 30, 557–573 (2019). https://doi.org/10.1007/s10845-016-1262-6

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  • DOI: https://doi.org/10.1007/s10845-016-1262-6

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