Abstract
In the current paper, a special class of buffer allocation problem (BAP)—buffer and preventive maintenance periods allocation problems—is studied. Three objective functions including the maximization of production rate, the minimization of total buffer size, and the total number of defective units are examined in order to solve the proposed model. In addition, an integrated simulation and meta-heuristic algorithm method is used for the same purpose. The proposed model and solution approach are applied to a case study, i.e., water heater production line in Polar Saunier Duval Company. Furthermore, the model and solutions will be assessed. In this integrated approach, genetic algorithm (GA) and particle swarm optimization (PSO) algorithms are used as meta-heuristic algorithms. These algorithms are compared in terms of quality of solutions, computational time, and relative deviation of final solutions in 0.95 confidence level. Numerical results reveal that GA is superior to PSO in terms of the quality of obtained solutions and relative deviation from optimal solutions (RPD). Furthermore, PSO converges in shorter computational time. Finally, based on outputs of the integrated solution approach, some important points about the parameters of the production line are presented. Computational results show that production rate is the most influential objective function in optimal solutions. However, drawing from the characteristics of production line, the number of defective products has less impact on optimal solutions in comparison with other objective functions. It is also concluded that there is a direct relationship between the number of defective products and time periods between preventive maintenance operations; in other words, if the time periods between preventive maintenance operations increase, the number of defective products grow.
Similar content being viewed by others
References
Demir L, Tunali S, Eliiyi D, Lokketangen A (2013) Two approaches for solving the buffer allocation problem in unreliable production lines. Comput Oper Res 40(10):2556–2563
Battini D, Persona A, Regattieri A (2009) Buffer size design linked to reliability performance: a simulative study. Computers Industrial Engineering 56(4):1633–1641
Demir L, Tunali S, Løkketangen A (2011) A tabu search approach for buffer allocation in production lines with unreliable machines. Eng Optim 43(2):213–231
Demir L, Tunali S, Eliiyi DT (2012) An adaptive tabu search approach for buffer allocation problem in unreliable non-homogenous production lines. Comput Oper Res 39(7):1477–1486
Nahas N, Ait-Kadi D, Nourelfath M (2006) A new approach for buffer allocation in unreliable production lines. Int J Prod Econ 103(2):873–881
Batun S, Azizoglu M (2009) Single machine scheduling with preventive maintenances. Int J Prod Res 47(7):1753–1771
Nakagawa T (2005) Maintenance theory of reliability. Springerverlag London Limited, London
Vouros GA, Papadopoulos HT (1998) Buffer allocation in unreliable production lines using a knowledge-based system. Comput Oper Res 25(12):1055–1067
Helber S, Schimmelpfeng K, Stolletz R, Lagershausen S (2011) Using linear programming to analyze and optimize stochastic flow lines. Ann Oper Res 182(1):193–211
Srinivas C, Satyanarayana B, Ramji K, Ravela N (2011) Determination of buffer size in single and multi row flexible manufacturing systems through simulation. International Journal of Engineering Science and Technology 3(5):3889–3899
Can B, Heavey C (2012) A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models. Comput Oper Res 39(2):424–436
F. R. Cruz, G. Kendall, L. While, A. R. Duarte and N. L. Brito (2012) Throughput maximization of queueing networks with simultaneous minimization of service rates and buffers. Math Prob Eng 2012(692593)19 pages. doi:10.1155/2012/692593
Narashimhamu KL, Reddy VV, Rao CS (2014) Optimal buffer allocation in tandem closed Quening network with single server using PSO. International Confrence on Advances in Manufacturing and Materials Engineering 5(1):2084–2089
Abdul-Kader W, Ganjavi O, Baki F (2011) A nonlinear model for optimizing the performance of a multi-product production line. Int Trans Oper Res 18(5):561–577
Amiri M, Mohtashami A (2011) Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm. International Journal of Advanced Manufacturing 62(1):371–383
Bertazzi L (2011) Determining the optimal dimension of a work-in-process storage area. Int J Prod Econ 131(2):483–489
Zhou W, Lian Z (2011) A tandem network with a sharing buffer. Appl Math Model 35(9):4507–4515
Aksoy KH, Gupta SM (2011) Optimal management of remanufacturing systems with server vacations. Int J Adv Manuf Technol 54(9–12):1199–1218
Dolgui A, Eremeev A, Sigaev V (2011) HBBA: hybrid algorithm for buffer allocation in tandem production lines. J Intell Manuf 18(3):411–420
Can B, Heavey C (2011) Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems. Computers, Industrial Engineering 61(3):447–462
Seo D-W, Lee H (2011) Stationary waiting times in m-node tandem queues with production blocking. IEEE Trans Autom Control 56(4):958–961
Kim S, Lee H-J (2011) Allocation of buffer capacity to minimize average work-in-process. Production Planning, Control 12(7):706–716
Massim Y, Yalaoui F, Chatelet E, Yalaoui A, Zeblah A (2010) Efficient immune algorithm for optimal allocations in series-parallel continuous manufacturing systems. J Intell Manuf 12(1):20–29
McNamara T, Shaaban S, Hudson S (2011) Unpaced production lines with three simultaneous imbalance sources. Industrial Management, Data Systems 111(9):1356–1380
Qudeiri JA, Yamamoto H, Ramli R, Al-Momani KR (2007) Development of production simulator for buffer size decisions in complex production systems using genetic algorithms. Journal of Advanced Mechanical Design, Systems, and Manufacturing 1(3):418–429
Qudeiri JA, Yamamoto H, Ramli R, Jamali A (2008) Genetic algorithm for buffer size and work station capacity in serial–parallel production lines. Artificial Life and Robotics 12(1):102–106
Radhoui M, Rezg N, Chelbi A (2010) Joint quality control and preventive maintenance strategy for imperfect production processes. J Intell Manuf 21(2):205–212
Vergara HA, Kim DS (2009) A new method for the placement of buffers in serial production lines. Int J Prod Res 47(15–16):4437–4456
Lee H-T, Chen S-K, Shunder Chang S (2009) A meta-heuristic approach to buffer allocation in production line. Journal of CCIT 38(1):167–178
C. Roser, M. Nakano and M. Tanaka (2003) Buffer allocation model based on a single simulation. In: Proceedings 35th of the Winter Simulation Conference, 1238–1246
Zeleny M (1982) Multiple criteria decision making. McGraw-Hill, N.Y
Yu P (1973) A class of solutions for group decision problems. Manag Sci 19(18):936–946
K. Deb (1998) Genetic algorithm in search and optimization: the technique and applications. In: Proceeding of the International Workshop on Soft Computing and Intelligent Systems Machine Intelligence Unit
J. Kennedy and R. Eberhart (1995). Particle swarm optimization. In: IEEE Conference on Neural Networks: Perth
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zandieh, M., Joreir-Ahmadi, M.N. & Fadaei-Rafsanjani, A. Buffer allocation problem and preventive maintenance planning in non-homogenous unreliable production lines. Int J Adv Manuf Technol 91, 2581–2593 (2017). https://doi.org/10.1007/s00170-016-9744-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-016-9744-4