Abstract
This paper presents a multiobjective formulation of the buffer allocation problem in unreliable production lines. Majority of the solution methods for buffer allocation problems assume that the process times, time between failures, and repair times are deterministic or exponentially distributed. This paper relaxes these restrictions by proposing a simulation-based methodology which can consider general function distributions for all parameters of production lines. Factorial design has been used to build a meta-model for estimating production rate based on a detailed, discrete event simulation model. We use genetic algorithm combined to line search method to solve the multiobjective model and determining the optimal (or near optimal) size of each buffer storage.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
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
Vidalis M, Papadopoulos C, Heavey C (2005) On the workload and ‘phaseload’ allocation problems of short reliable production lines with finite buffers. Comput Ind Eng 48(4):825–837
Nahas N, Nourelfath M, Ait-Kadi D (2008) Ant colonies for structure optimization in a failure prone series–parallel production system. J Qual Maint Eng 14(1):7–33
Shi C, Gershwin S (2009) An efficient buffer design algorithm for production line profit maximization. Int J Prod Econ 122(2):725–740
Massim Y, Yalaouib F, Amodeob L, Chateletc E, Zeblaha A (2010) Efficient combined immune-decomposition algorithm for optimal buffer allocation in production lines for throughput and profit maximization. Comput Oper Res 37(4):611–620
Dallery Y, Gershwin S (1992) Manufacturing flow line systems: a review of models and analytical results. Queuing Syst Theory Appl 12:3–94
Buzacott J (1967) Automatic transfer lines with buffer stocks. Int J Prod Res 5(3):183–200
Chow W (1987) Buffer capacity analysis for sequential production lines with variable processing times. Int J Prod Res 25(8):1183–1196
Gershwin S (1987) An efficient decomposition method for the approximate evaluation of tandem queues with finite storage space and blocking. Oper Res 35(2):291–305
Dallery Y, David R, Xie X (1988) An efficient algorithm for analysis of transfer lines with unreliable machines and finite buffers. IIE Trans 20(3):280–283
Huang M, Guang C, Pao L, Chou Y (2002) Buffer allocation in flow-shop-type production system with general arrival and service patterns. Comput Oper Res 29(2):103–121
Diamantidis A, Papadopoulos C (2004) A dynamic programming algorithm for the buffer allocation problem in homogeneous asymptotically reliable serial production lines. Math Probl Eng 3:209–223
Lim J, Meerkov S, Top F (1990) Homogeneous, asymptotically reliable serial production line: theory and a case study. IEEE Trans Autom Control 35(5):524–534
Smith J, Cruz F (2005) The buffer allocation problem for general finite buffer queuing networks. IIE Trans 37(4):343–365
Manitz M (2008) Queuing model based analysis of assembly lines with finite buffers and general services times. Comput Oper Res 35(8):2520–2536
Diamantidis C, Papadopoulos T (2009) Exact analysis of a two-workstation one-buffer flow line with parallel unreliable machines. Eur J Oper Res 197(2):572–580
Radhoui M, Rezg N, Chelbi A (2009) Integrated model of preventive maintenance, quality control and buffer sizing for unreliable and imperfect production systems. Int J Prod Res 47(2):389–402
Sabuncuoglu E, Gocgun Y (2006) Analysis of serial production lines: characterisation study and a new heuristic procedure for optimal buffer allocation. Int J Prod Res 44(13):2499–2523
Vergara H, Kim D (2009) A new method for the placement of buffers in serial production lines. Int J Prod Res 47(16):4437–4456
Romulo I, Pernando P, Valdes J (2004) Optimal buffer inventory and preventive maintenance for an imperfect production process. Int J Prod Res 42(5):959–974
Colledani M, Tolio T (2011) Integrated analysis of quality and production logistics performance in manufacturing lines. Int J Prod Res 49(2):485–518
Abdul-Kader W, Gharbi A (2002) Capacity estimation of a multi-product unreliable production line. Int J Prod Res 40(18):4815–4834
Fonseca CM, Fleming PJ (1993) Genetic algorithms for multi-objective optimization: formulation, discussion and generalization. Proceeding of the fifth international conference on genetic algorithm, San Mateo, CA 416–423
Poidlena JR, Hendtlass T (1998) An accelerated genetic algorithm. Appl Intell 8:103–111
Deb K (1999) Evolutionary algorithms for multi-criterion optimization in engineering design. Kanpur Genetic Algorithm Laboratory KanGAL 1999–2001
Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform 16:193–203
Luh GC, Chueh CH, Liu WW (2003) MOIA: multi-objective immune algorithm. Eng Optim 35:143–164
Yildiz A (2009) A new design optimization framework based on immune algorithm and Taguchi’s method. Comput Ind 60:613–620
Yildiz A (2009) An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. J Mater Process Technol 209:2773–2780
Yildiz A (2009) A novel particle swarm optimization approach for product design and manufacturing. Int J Adv Manuf Technol 40:617–628
Yildiz A (2008) Hybrid Taguchi-Harmony search algorithm for solving engineering optimization problems. Int J Ind Eng Theory Appl Pract 15(3):286–293
Yildiz A (2009) Hybrid immune-simulated annealing algorithm for optimal design and manufacturing. Int J Mater Prod Technol 34(3):217–226
Yildiz A (2009) A novel hybrid immune algorithm for global optimization in design and manufacturing. Robot Comput-Integr Manuf 25:261–270
Yildiz A, Ozturk N, Kaya N, Ozturk F (2007) Hybrid multi-objective shape design optimization using Taguchi’s method and genetic algorithm. Struct Multidiscip Optim 34(4):277–365
Yildiz A, Saitou K (2011) Topology synthesis of multicomponent structural assemblies in continuum domains. J Mech Des 133(1)
Lavoie P, Kenne J, Gharbi A (2009) Optimization of production control policies in failure-prone homogenous transfer lines. IEEE Trans 41:209–222
Kleijnen JPC, Sargent RG (2000) A methodology for fitting and validating meta-models in simulation. Eur J Oper Res 120:14–29
Kleijnen JPC (1987) Statistical tools for simulation practitioners. Marcel Dekker, New York
Noguera J, Watson E (2006) Response surface analysis of a multi-product batch processing facility using a simulation meta-model. Int J Prod Econ 102:333–343
Barton RR (1992) Meta-models for simulation input–output relations. In: Swain JJ, Goldsman D, Crain RC, Wilson JR (eds) Proceedings of the 1992 Winter Simulation Conference: 289–299
Durieux S, Pierreval H (2003) Regression meta-modeling for the design of automated manufacturing system composed of parallel machines sharing a material handling resource. Int J Prod Econ 1–10
Dengiz B, Akbay KS (2000) Computer simulation of a PCB production line: meta-modeling approach. Int J Prod Econ 63:195–205
Safizadeh MH (1990) Optimization in simulation: current issues and the future outlook. Nav Res Logist 37:807–825
Madu CN, Kuei CH (1994) Regression meta-modeling in computer simulation—the state of the art. Simul Pract Theory 2:27–41
Kleijnen JPC (1979) Regression meta-models for generalizing simulation results. IEEE SMC-9 2:93–96
Kleijnen JPC (1981) Regression analysis for simulation practitioners. J Oper Res Soc 32:35–43
Montgomery D (2001) Design and analysis of experiments. Wiley, New York
Costa C, Wolf M, Maria R, Rubens M (2005) Factorial design technique applied to genetic algorithm parameters in a batch cooling crystallization optimization. Comput Chem Eng 29(10):2229–2241
Barros N, Scarminio I, Bruns R (2001) Como fazer experimentos: pesquisa e desenvolvimento na ciˆencia e naind’ustria. Editora da Unicamp Camp 20:83–184
Box G, Hunter W, Hunter J (1978) Statistic for experimenters—an introduction to design data analysis and model building. Wiley, New York
Rodrigues J, Toledo E, Maciel F (2002) A tuned approach of the predictive adaptative GPC controller applied to a fedbatch bioreactor using complete factorial design. Comput Chem Eng 26(10):1493–1500
Kleijnen JPC (1995) Verification and validation of simulation models. Eur J Oper Res 82:145–162
Panis RP, Myers RH, Houck EC (1994) Combining regression diagnostics with simulation meta-models. Eur J Oper Res 73:85–94
Yu P (1973) A class of solutions for group decision problems. Manag Sci 19(8):936–946
Zeleny M (1982) Multiple criteria decision making. McGraw-Hill, New York
Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Michigan
Fuhner T, Jung T (2004) Use of genetic algorithms for the development and optimization of crystal growth processes. J Cryst Growth 266(1–3):229–238
Deb K (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 58–87
Deb K (1999) An introduction to genetic algorithms. In Sadhana-Academy Proceedings in Engineering Sciences 24 Part 4–5: 293–315
Wang HF, Wu KY (2004) Hybrid genetic algorithm for optimization problems with permutation property. Comput Oper Res 31(14):2453–2471
Gen M, Cheng R (1996) Optimal design of system reliability using interval programming and genetic algorithms. Comput Ind Eng 31(1–2):237–240
Turabieh H, Sheta A, Vasant P (2007) Hybrid optimization genetic algorithm (HOGA) with interactive evolution to solve constraint optimization problems for production systems. Int J Comput Sci 1(4):395–406
Elegbede C, Adjallah K (2003) Availability allocation to repairable systems with genetic algorithms: a multi-objective formulation. Reliab Eng Syst Saf 82(3):319–330
Gupta RK, Bhunia AK, Roy D (2009) A GA based penalty function technique for solving constrained redundancy allocation problem of series system with interval valued reliability of components. J Comput Appl Math 232:275–284
Kumar R, Izui K, Yoshimura M, Nishiwaki Sh (2009) Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization. Reliab Eng Syst Saf 94:891–904
Yokota T, Gen M, Li Y (1996) Genetic algorithm for non-linear mixed integer programming problems and its applications. Comput Ind Eng 30(4):905–917
Yang J, Hwang M, Sung T, Jin Y (2000) Application of genetic algorithm for reliability allocation in nuclear power plants. Reliab Eng Syst Saf 65(3):229–238
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Amiri, M., Mohtashami, A. Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm. Int J Adv Manuf Technol 62, 371–383 (2012). https://doi.org/10.1007/s00170-011-3802-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-011-3802-8