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Journal of Intelligent Manufacturing

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19-09-2020

A comparison of combat genetic and big bang–big crunch algorithms for solving the buffer allocation problem

Authors: Mehmet Ulaş Koyuncuoğlu, Leyla Demir

Published in: Journal of Intelligent Manufacturing | Issue 6/2021

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Abstract

The buffer allocation problem (BAP) aims to determine the optimal buffer configuration for a production line under the predefined constraints. The BAP is an NP-hard combinatorial optimization problem and the solution space exponentially grows as the problem size increases. Therefore, problem specific heuristic or meta-heuristic search algorithms are widely used to solve the BAP. In this study two population-based search algorithms; i.e. Combat Genetic Algorithm (CGA) and Big Bang-Big Crunch (BB-BC) algorithm, are proposed in solving the BAP to maximize the throughput of the line under the total buffer size constraint for unreliable production lines. Performances of the proposed algorithms are tested on existing benchmark problems taken from the literature. The experimental results showed that the proposed BB–BC algorithm yielded better results than the proposed CGA as well as other algorithms reported in the literature.

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Alatas, B. (2011). Uniform big bang-chaotic big crunch optimization. Communications in Nonlinear Science and Numerical Simulation, 16(9), 3696–3703. https://doi.org/10.1016/j.cnsns.2010.12.025.CrossRef

Altiparmak, F., Dengiz, B., & Bulgak, A. A. (2002). Optimization of buffer sizes in assembly systems using intelligent techniques. Winter Simulation Conference Proceedings. https://doi.org/10.1109/wsc.2002.1166373.CrossRef

Amiri, M., & Mohtashami, A. (2012). Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-011-3802-8.CrossRef

Biradar, S., Hote, Y. V., & Saxena, S. (2016). Reduced-order modeling of linear time invariant systems using big bang big crunch optimization and time moment matching method. Applied Mathematical Modelling, 40(15–16), 7225–7244. https://doi.org/10.1016/j.apm.2016.03.006.CrossRef

Bӓck. T. (1993). Optimal mutation rates in genetic search. In: Fifth international conference on genetic algorithms—ICGA (vol. 5, pp. 2–8). Morgan Kaufmann.

Camp, C. V., & Huq, F. (2013). CO₂ and cost optimization of reinforced concrete frames using a big bang–big crunch algorithm. Engineering Structures, 48, 363–372. https://doi.org/10.1016/j.engstruct.2012.09.004.CrossRef

Chuang, L. Y., Yang, C. H., Li, J. C., & Yang, C. H. (2012). A hybrid BPSO-CGA approach for gene selection and classification of microarray data. Journal of Computational Biology, 19(1), 68–82. https://doi.org/10.1089/cmb.2010.0064.CrossRef

Costa, A., Alfieri, A., Matta, A., & Fichera, S. (2015). A parallel tabu search for solving the primal buffer allocation problem in serial production systems. Computers & Operations Research, 64, 97–112. https://doi.org/10.1016/j.cor.2015.05.013.CrossRef

Cvetković, D., & Parmee, I. C. (2002). Preferences and their application in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation, 6(1), 42–57. https://doi.org/10.1109/4235.985691.CrossRef

Dallery, Y., David, R., & Xie, X. L. (1988). An efficient algorithm for analysis of transfer lines with unreliable machines and finite buffers. IIE Transactions (Institute of Industrial Engineers), 20(3), 281–283. https://doi.org/10.1080/07408178808966181.CrossRef

Demir, L., Tunal, S., & Eliiyi, D. T. (2012). An adaptive tabu search approach for buffer allocation problem in unreliable non-homogenous production lines. Computers & Operations Research. https://doi.org/10.1016/j.cor.2011.08.019.CrossRef

Demir, L., Tunali, S., & Eliiyi, D. T. (2014). The state of the art on buffer allocation problem: A comprehensive survey. Journal of Intelligent Manufacturing, 25(3), 371–392. https://doi.org/10.1007/s10845-012-0687-9.CrossRef

Demir, L., Tunali, S., & Løkketangen, A. (2011). A tabu search approach for buffer allocation in production lines with unreliable machines. Engineering Optimization, 43(2), 213–231. https://doi.org/10.1080/0305215X.2010.481022.CrossRef

Desai, S. R., & Prasad, R. (2013). A novel order diminution of LTI systems using big bang big crunch optimization and routh approximation. Applied Mathematical Modelling, 37(16–17), 8016–8028. https://doi.org/10.1016/j.apm.2013.02.052.CrossRef

Dolgui, A., Eremeev, A., Kolokolov, A., & Sigaev, V. (2002). A genetic algorithm for the allocation of buffer storage capacities in a production line with unreliable machines. Journal of Mathematical Modelling and Algorithms, 1(2), 89–104. https://doi.org/10.1023/A:1016560109076.CrossRef

Dolgui, A., Eremeev, A. V., & Sigaev, V. S. (2007). HBBA: Hybrid algorithm for buffer allocation in tandem production lines. Journal of Intelligent Manufacturing, 18(3), 411–420. https://doi.org/10.1007/s10845-007-0030-z.CrossRef

Eksin, I., & Erol, O. K. (2001). Evolutionary algorithm with modifications in the reproduction phase. IEE Proceedings - Software, 148(2), 75–80.CrossRef

Erol, O. K., & Eksin, I. (2006). A new optimization method: Big bang-big crunch. Advances in Engineering Software, 37(2), 106–111. https://doi.org/10.1016/j.advengsoft.2005.04.005.CrossRef

Fernández-Cabán, P. L., & Masters, F. J. (2018). Hybridizing particle swarm and big bang–big crunch optimization methods to explore then exploit the design domain of large planar frame structures. Computers & Structures, 202, 1–14. https://doi.org/10.1016/j.compstruc.2018.02.014.CrossRef

Gen, M., & Lin, L. (2014). Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey. Journal of Intelligent Manufacturing, 25(5), 849–866. https://doi.org/10.1007/s10845-013-0804-4.CrossRef

Genç, H. M., Eksin, I., & Erol, O. K. (2013). Big bang–big crunch optimization algorithm with local directional moves. Turkish Journal of Electrical Engineering and Computer Sciences, 21(5), 1359–1375. https://doi.org/10.3906/elk-1106-46.CrossRef

Gershwin, S. B. (1987). An efficient decomposition method for the approximate evaluation of production lines with finite storage space and blocking. Operations Research, 35(2), 291–305. https://doi.org/10.1007/BFb0006317.CrossRef

Gershwin, S. B., & Schor, J. E. (2000). Efficient algorithms for buffer space allocation. Annals of Operations Research, 93(1–4), 117–144. https://doi.org/10.1023/A:1018988226612.CrossRef

Guner Goren, H., Tunali, S., & Jans, R. (2010). A review of applications of genetic algorithms in lot sizing. Journal of Intelligent Manufacturing, 21(4), 575–590. https://doi.org/10.1007/s10845-008-0205-2.CrossRef

Hamdi, M., & Zaied, M. (2019). Resource allocation based on hybrid genetic algorithm and particle swarm optimization for D2D multicast communications. Applied Soft Computing Journal, 83, 105605. https://doi.org/10.1016/j.asoc.2019.105605.CrossRef

Hasançebi, O., & Kazemzadeh Azad, S. (2012). An exponential big bang–big crunch algorithm for discrete design optimization of steel frames. Computers & Structures, 110–111, 167–179. https://doi.org/10.1016/j.compstruc.2012.07.014.CrossRef

Hillier, F. S., So, K. C., & Boling, R. W. (1993). Toward characterizing the optimal allocation of storage space in production line systems with variable processing times. Management Science, 39, 126–133.CrossRef

Ho, Y. C., Eyler, M. A., & Chien, T. T. (1979). A gradient technique for general buffer storage design in a production line. International Journal of Production Research, 17(6), 557–580.CrossRef

Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: The University of Michigan Press.

Kaveh, A., & Talatahari, S. (2009). Size optimization of space trusses using big bang–big crunch algorithm. Computers & Structures, 87(17–18), 1129–1140. https://doi.org/10.1016/j.compstruc.2009.04.011.CrossRef

Kaveh, A., & Talatahari, S. (2010). Optimal design of Schwedler and ribbed domes via hybrid big bang–big crunch algorithm. Journal of Constructional Steel Research, 66(3), 412–419. https://doi.org/10.1016/j.jcsr.2009.10.013.CrossRef

Koenigsberg, E. (1959). Production lines and internal storage: A review. Management Science, 5(4), 410–433. https://doi.org/10.1287/mnsc.5.4.410.CrossRef

Li, L., Qian, Y., Yang, Y. M., & Du, K. (2015). A fast algorithm for buffer allocation problem. International Journal of Production Research. https://doi.org/10.1080/00207543.2015.1092612.CrossRef

Liberopoulos, G. (2019). Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies. Flexible Services and Manufacturing Journal. https://doi.org/10.1007/s10696-019-09341-y.CrossRef

Lopes, T. C., Sikora, C. G. S., Michels, A. S., & Magatão, L. (2020). An iterative decomposition for asynchronous mixed-model assembly lines: Combining balancing, sequencing, and buffer allocation. International Journal of Production Research, 58(2), 615–630. https://doi.org/10.1080/00207543.2019.1598597.CrossRef

Motlagh, M. M., Azimi, P., Amiri, M., & Madraki, G. (2019). An efficient simulation optimization methodology to solve a multi-objective problem in unreliable unbalanced production lines. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2019.112836.CrossRef

Nahas, N., Ait-Kadi, D., & Nourelfath, M. (2006). A new approach for buffer allocation in unreliable production lines. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2006.02.011.CrossRef

Nahas, N., & Nourelfath, M. (2018). Joint optimization of maintenance, buffers and machines in manufacturing lines. Engineering Optimization. https://doi.org/10.1080/0305215X.2017.1299716.CrossRef

Nahas, N., Nourelfath, M., & Ait-Kadi, D. (2009). Selecting machines and buffers in unreliable series-parallel production lines. International Journal of Production Research. https://doi.org/10.1080/00207540701806883.CrossRef

Narasimhamu, K. L., Venugopal Reddy, V., & Rao, C. S. P. (2014). Optimal buffer allocation in tandem closed queuing network with single server using PSO. Procedia Materials Science, 5, 2084–2089. https://doi.org/10.1016/j.mspro.2014.07.543.CrossRef

Niyomubyeyi, O., Sicuaio, T. E., Díaz González, J. I., Pilesjö, P., & Mansourian, A. (2020). A comparative study of four metaheuristic algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for evacuation planning. Algorithms, 13(1), 16. https://doi.org/10.3390/a13010016.CrossRef

Pedrielli, G., Matta, A., Alfieri, A., & Zhang, M. (2018). Design and control of manufacturing systems: A discrete event optimisation methodology. International Journal of Production Research. https://doi.org/10.1080/00207543.2017.1412532.CrossRef

Romero-Silva, R., & Shaaban, S. (2019). Influence of unbalanced operation time means and uneven buffer allocation on unreliable merging assembly line efficiency. International Journal of Production Research, 57(6), 1645–1666. https://doi.org/10.1080/00207543.2018.1495344.CrossRef

Sedighizadeh, M., Ahmadi, S., & Sarvi, M. (2013). An efficient hybrid big bang–big crunch algorithm for multi-objective reconfiguration of balanced and unbalanced distribution systems in fuzzy framework. Electric Power Components and Systems, 41(1), 75–99. https://doi.org/10.1080/15325008.2012.732658.CrossRef

Smith, J. M. G. (2018). Simultaneous buffer and service rate allocation in open finite queueing networks. IISE Transactions, 50(3), 203–216. https://doi.org/10.1080/24725854.2017.1300359.CrossRef

Talbi, E. G. (2009). Metaheuristics: From design to implementation. Metaheuristics: From Design to Implementation. https://doi.org/10.1002/9780470496916.CrossRef

Tang, H., Zhou, J., Xue, S., & Xie, L. (2010). Big bang–big crunch optimization for parameter estimation in structural systems. Mechanical Systems and Signal Processing, 24(8), 2888–2897. https://doi.org/10.1016/j.ymssp.2010.03.012.CrossRef

Tasan, S. O., & Tunali, S. (2008). A review of the current applications of genetic algorithms in assembly line balancing. Journal of Intelligent Manufacturing, 19(1), 49–69. https://doi.org/10.1007/s10845-007-0045-5.CrossRef

Thierens, D. (2002). Adaptive mutation rate control schemes in genetic algorithms. In Proceedings of the 2002 congress on evolutionary computation, CEC 2002 (Vol. 1, pp. 980–985). https://doi.org/10.1109/CEC.2002.1007058.

Weiss, S., Matta, A., & Stolletz, R. (2018). Optimization of buffer allocations in flow lines with limited supply. IISE Transactions, 50(3), 191–202. https://doi.org/10.1080/24725854.2017.1328751.CrossRef

Weiss, S., Schwarz, J. A., & Stolletz, R. (2019). The buffer allocation problem in production lines: Formulations, solution methods, and instances. IISE Transactions. https://doi.org/10.1080/24725854.2018.1442031.CrossRef

Xi, S., Chen, Q., MacGregor Smith, J., Mao, N., Yu, A., & Zhang, H. (2019). A new method for solving buffer allocation problem in large unbalanced production lines. International Journal of Production Research. https://doi.org/10.1080/00207543.2019.1685709.CrossRef

Xuemei, L., Huan, S., Rui, Z., Yongqi, J., & Aiping, L. (2017). Collaborative optimization of transfer line balancing and buffer allocation based on polychromatic set. Procedia CIRP, 63, 213–218. https://doi.org/10.1016/j.procir.2017.02.045.CrossRef

Yelkenci Kose, S., & Kilincci, O. (2018). A multi-objective hybrid evolutionary approach for buffer allocation in open serial production lines. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1435-6.CrossRef

Zandieh, M., Joreir-Ahmadi, M. N., & Fadaei-Rafsanjani, A. (2017). Buffer allocation problem and preventive maintenance planning in non-homogenous unreliable production lines. International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-016-9744-4.CrossRef

Title: A comparison of combat genetic and big bang–big crunch algorithms for solving the buffer allocation problem
Authors: Mehmet Ulaş Koyuncuoğlu
Leyla Demir
Publication date: 19-09-2020
Publisher: Springer US
Published in: Journal of Intelligent Manufacturing / Issue 6/2021
Print ISSN: 0956-5515
Electronic ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-020-01647-1

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