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An improved backtracking search algorithm for casting heat treatment charge plan problem

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Abstract

This study investigates the optimization of the charge plan in casting heat treatment. The optimization problem is formulated as a 0–1 integer programming model aiming at maximizing the utilization of the furnaces, minimizing the holding temperature differences and the overall delivery deadline of castings in a furnace. To approach the mathematical model, a two-steps solution methodology is designed. First, the feasible casting candidate sets are generated in consideration of the holding temperature and cooling mode constraints. Then, an improved backtracking search algorithm (IBSA) is proposed to obtain optimal charge plan for each feasible candidate set. The best one among the optimal charge plans obtained by IBSA is selected as the final charge plan. In IBSA, a mapping mechanism is applied to make original backtracking search algorithm (BSA) suitable to discrete problems. Improvements that consist of the modification of historical population updating mechanism, the hybrid of mutation and crossover strategy of difference evaluation algorithm, a greedy local search algorithm and the re-initialization operator are also made to enhance the exploitation and exploration ability of IBSA. The comparisons of simulation experiments demonstrate the effectiveness of the proposed model and the performance of the proposed algorithm.

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References

  • Ballestín, F., Mallor, F., & Mateo, P. M. (2011). Production scheduling in a market-driven foundry: A mathematical programming approach versus a project scheduling metaheuristic algorithm. Optimization and Engineering, 13(4), 663–687.

    Google Scholar 

  • BożejkoEmail, W., & Makuchowski, M. (2011). Solving the no-wait job-shop problem by using genetic algorithm with automatic adjustment. The International Journal of Advanced Manufacturing Technology, 57(5), 735–752.

    Article  Google Scholar 

  • Brest, J., Greiner, S., Bošković, B., Mernik, M., & Žumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, 10(6), 646–657.

    Article  Google Scholar 

  • Camargo, V. C. B., Mattiolli, L., & Toledo, F. M. B. (2012). A knapsack problem as a tool to solve the production planning problem in small foundries. Computers & Operations Research, 39(1), 86–92.

    Article  Google Scholar 

  • Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219(15), 8121–8144.

    Article  Google Scholar 

  • Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Distributed optimization by ant colonies. In Proceedings of the first European conference on artificial life (pp. 134–142).

  • Das, S., & Suganthan, P. N. (2011). Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1), 4–31.

    Article  Google Scholar 

  • de Araujo, S. A., Arenales, M. N., & Clark, A. R. (2008). Lot sizing and furnace scheduling in small foundries. Computers & Operations Research, 35(3), 916–932.

    Article  Google Scholar 

  • Duda, J. (2005). Lot-sizing in a foundry using genetic algorithm and repair functions. In EvoCOP 2005: Evolutionary computation in combinatorial optimization (pp. 101–111).

  • Duda, J., Stawowy, A., & Basiura, R. (2014). Mathematical programming for lot sizing and production scheduling in foundries. Archives of Foundry Engineering, 14(3), 17–20.

    Article  Google Scholar 

  • Eberhart, R. C., & Kennedy, J. A. (1995). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science (pp. 39–43).

  • Guner, G. H., Tunali, S., & Jans, R. (2010). A review of applications of genetic algorithms in lot sizing. Journal of Intelligent Manufacturing, 21(4), 575–590.

    Article  Google Scholar 

  • Hans, E., & van de Velde, S. (2011). The lot sizing and scheduling of sand casting operations. International Journal of Production Research, 49(9), 2481–2499.

    Article  Google Scholar 

  • Holland, J. H. (1992). Genetic algorithms. Scientific American, 267, 66–72.

    Article  Google Scholar 

  • Huang, J., Gao, L., & Li, X. (2015). An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes. Applied Soft Computing, 36, 349–356.

    Article  Google Scholar 

  • Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.

    Article  Google Scholar 

  • Kılıç, U. (2015). Backtracking search algorithm-based optimal power flow with valve point effect and prohibited zones. Electrical Engineering, 97(2), 101–110.

    Article  Google Scholar 

  • Korytkowski, P., Rymaszewski, S., & Wiśniewski, T. (2013). Ant colony optimization for job shop scheduling using multi-attribute dispatching rules. The International Journal of Advanced Manufacturing Technology, 67(1), 231–241.

    Article  Google Scholar 

  • Li, X., & Yin, M. (2013). An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure. Advances in Engineering Software, 55, 10–31.

    Article  Google Scholar 

  • Li, X., Guo, S., Liu, Y., Du, B., & Wang, L. (2017). A production planning model for make-to-order foundry flow shop with capacity constraint. Mathematical Problems in Engineering, 2017, 1–15.

    Google Scholar 

  • Lin, Q., Gao, L., Li, X., & Zhang, C. (2015). A hybrid backtracking search algorithm for permutation flow-shop scheduling problem. Computers & Industrial Engineering, 85, 437–446.

    Article  Google Scholar 

  • Mirjalili, S. (2015). The ant lion optimizer. Advances in Engineering Software, 83, 80–98.

    Article  Google Scholar 

  • Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.

    Article  Google Scholar 

  • Modiri-Delshad, M., & Rahim, N. A. (2016). Multi-objective backtracking search algorithm for economic emission dispatch problem. Applied Soft Computing, 40, 479–494.

    Article  Google Scholar 

  • Neri, F., & Tirronen, V. (2010). Recent advances in differential evolution: A survey and experimental analysis. Artificial Intelligence Review, 33(1), 61–106.

    Article  Google Scholar 

  • Pan, W. (2012). A new fruit fly optimization algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69–74.

    Article  Google Scholar 

  • Qian, B., Wang, L., Hu, R., Wang, W., Huang, D., & Wang, X. (2008). A hybrid differential evolution method for permutation flow-shop scheduling. The International Journal of Advanced Manufacturing Technology, 38(7), 757–777.

    Article  Google Scholar 

  • Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315.

    Article  Google Scholar 

  • Santos-Meza, E. D., Santos, M., & Arenales, M. N. (2002). A lot-sizing problem in an automated foundry. European Journal of Operational Research, 139(3), 490–500.

    Article  Google Scholar 

  • Shafiullah, M., Abido, M. A., & Coelho, L. S. (2016). Optimal power flow of two-terminal HVDC systems using backtracking search algorithm. International Journal of Electrical Power & Energy Systems, 78, 326–335.

    Article  Google Scholar 

  • Storn, R., & Price, K. (1997). Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.

    Article  Google Scholar 

  • Tang, L., Meng, Y., & Liu, J. (2011). An improved Lagrangean relaxation algorithm for the dynamic batching decision problem. International Journal of Production Research, 49(9), 2501–2517.

  • Tao, F., Cheng, Y., Zhang, L., & Nee, A. Y. C. (2015). Advanced manufacturing systems: Socialization characteristics and trends. Journal of Intelligent Manufacturing. doi:10.1007/s10845-015-1042-8.

  • Teixeira, R. F., Fernandes, F. C. F., & Pereira, N. A. (2010). Binary integer programming formulations for scheduling in market-driven foundries. Computers & Industrial Engineering, 59(3), 425–435.

    Article  Google Scholar 

  • Triki, H., Mellouli, A., & Masmoudi, F. (2017). A multi-objective genetic algorithm for assembly line resource assignment and balancing problem of type 2 (ALRABP-2). Journal of Intelligent Manufacturing, 28(2), 371–385.

    Article  Google Scholar 

  • Yang, X., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330–343.

    Article  Google Scholar 

  • Yıldız, A. R. (2009a). A novel hybrid immune algorithm for global optimization in design and manufacturing. Robotics and Computer-Integrated Manufacturing, 25(2), 261–270.

    Article  Google Scholar 

  • Yıldız, A. R. (2009b). A novel particle swarm optimization approach for product design and manufacturing. The International Journal of Advanced Manufacturing Technology, 40, 617–628.

    Article  Google Scholar 

  • Yildiz, A. R. (2012). A comparative study of population-based optimization algorithms for turning operations. Information Sciences, 210, 81–88.

    Article  Google Scholar 

  • Yildiz, A. R. (2013). Comparison of evolutionary-based optimization algorithms for structural design optimization. Engineering Applications of Artificial Intelligence, 26(1), 327–333.

    Article  Google Scholar 

  • Yildiz, A. R., & Solanki, K. N. (2012). Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach. The International Journal of Advanced Manufacturing Technology, 59(1), 367–376.

    Article  Google Scholar 

  • Zacharia, P. T., & Nearchou, A. C. (2012). Multi-objective fuzzy assembly line balancing using genetic algorithms. Journal of Intelligent Manufacturing, 23(3), 615–627.

    Article  Google Scholar 

  • Zhou, J., Ji, X., Liao, D., & Yin, Y. (2013). Research and application of enterprise resource planning system for foundry enterprises. China Foundry, 10(1), 8–17.

    Google Scholar 

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Acknowledgements

This work is supported by the National Science and Technology Key Projects of Numerical Control under Grant No. 2012ZX04012-011.

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Correspondence to Xiaoyuan Ji.

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Zhou, J., Ye, H., Ji, X. et al. An improved backtracking search algorithm for casting heat treatment charge plan problem. J Intell Manuf 30, 1335–1350 (2019). https://doi.org/10.1007/s10845-017-1328-0

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