References
Abouei Ardakan, M., Zeinal Hamadani, A. & Alinaghian, M. (2015). Optimizing bi-objective redundancy allocation problem with a mixed redundancy strategy. ISA Transactions, 55, 116–128.
Alrabghi, A., & Tiwari, A. (2016). A novel approach for modeling complex maintenance systems using discrete event simulation. Reliability Engineering and System Safety, 154, 160–170.
Ameen, W., AlKahtani, M., Khan Mohammed, M., Abdulhameed, O., & El-Tamimi, A.M. (2018).Investigation of the effect of buffer storage capacity and repair rate on production line efficiency. Journal of King Saud University – Engineering Sciences, 30, 243–249.
Amiri, M., & Mohtashami, A. (2012). Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm. International Journal Advanced Manufacturing Technology, (62), 371–383.
Attar, A., Raissi, S., & Khalili-Damghani, K. (2017). A simulation-based optimization approach for free distributed repairable multi-state availability-redundancy allocation problems. Reliability Engineering and System Safety, 157, 177–191.
Chambari, A., Rahmati, S.H., Najafi A.A., & Karimi, A. (2012). A bi-objective model to optimize reliability and cost of system with a choice of redundancy strategies. Computers & Industrial Engineering, 63(1), 109–119.
Chang, Kuo-Hao., & Kuo P.Y. (2018). An efficient simulation optimization method for the generalized redundancy allocation problem. European Journal of Operational Research, 265, 1094–1101.
Chern, M. S. (1992). On the computational complexity of reliability redundancy allocation in a series system. Operations Research Letters, 11(5), 309–315.
Coello Coello C.A., Lamont G.B. & Van Veldhuizen D.A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems. Second Edition. Springer Science & Business Media.
Costa, A., Alfieri, 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.
Esfe,
M.H., Razi,
P., Hajmohammad,
M.H., Rostamian,
S.H., Sami Sarsam,
W., Akbar Abbasian Arani,
A., & Dahari,
M. (2017). Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN.
International Communications in Heat and Mass Transfer,
82, 154-160.
Esmaelian,
M., & Bakran, H. (2014). Preventive maintenance scheduling with integer programming and constraint programming. Industrial Management Journal, 6(3), 433-452.
Gershwin, S. B., & Schor, J. E. (2000). Efficient algorithms for buffer space allocation. Annals of Operations Research, (93), 117–144.
Heydari, M., Sullivan, KM. (2018). An Integrated Approach to Redundancy Allocation and Test Planning for Reliability Growth.Computers & Operations Research, 92, 182-193.
Huang, M., Guang, C., Pao, L., & Chou, Y. (2002). Buffer allocation in flow shop-type production system with general arrival and service patterns. Computer and Operation Research, 29(2), 103–121.
Huang, X., Coolen, F. P.A., Tahani, C.M. (2019). A heuristic survival signature based approach for reliability-redundancy allocation. Reliability Engineering and System Safety, 185, 511–517.
Ida, K., Gen, M. and Yokota, T. (1994). System Reliability Optimization with Several Failure Modes by Genetic Algorithm, Proceeding of the 16th International Conference on Computers and Industrial Engineering, Ashikaga of Japan, 82.
Jiansheng G., Zutong, W., Mingfa, Z., Ying, W. (2014). Uncertain multi-objective redundancy allocation problem of repairable systems based on artificial bee colony algorithm. Chinese Journal of Aeronautics, 27(6), 1477–1487.
Kayedpour, F., Amiri, A., Rafizadeh, M., & ShahryariNia, A. (2016). Multi-objective redundancy allocation problem for a system with repairable components considering instantaneous availability and strategy selection. Reliability Engineering & System Safety, 160, 132-151.
Kuo, W., & Prasad, R. (2000). An annotated overview of system reliability optimization. IEEE Transactions on Reliability, 49(2), 176–187.
Lavoie, P., Kenne, J., & Gharbi, A. (2009). Optimization of production control policies in failure- prone homogenous transfer lines.
IIE Transactions, 41(3), 209–222.
Liu, X.,
Lei, M.,
Zeng, Q., &
Li, A. (2019). Integrated Optimization of Mixed-Model Assembly Line Balancing and Buffer Allocation Based on Operation Time Complexity.
Procedia CIRP, 81, 1040-1045.
Manitz, M. (2008). Queuing model based analysis of assembly lines with finite buffers and general services times. Computers & Operations Research, 35(8), 2520–2536.
Mariano, C.H., & Pece, C.A.Z. (2015). Simulation Optimization Approach to Solve a Complex Multi-objective Redundancy Allocation Problem. In: Mujica Mota M., De La Mota I., Guimarans Serrano D. (eds) Applied Simulation and Optimization. Springer, Cham.
Montgomery,
D.C. (2012). Design and Analysis of Experiments, 8
th Edition, Wiley.
Okasha, N.M., & Dan, M. (2009). Lifetime-oriented multi-objective optimization of structural maintenance considering system reliability, redundancy and life-cycle cost using GA. Structural Safety, 31, 460–74.
Pourkarim Guilani, P., Azimi, P., Niaki, S.T.A., Akhavan Niaki, S.A. (2016). Redundancy allocation problem of a system with increasing failure rates of components based on Weibull distribution: A simulation-based optimization approach. Reliability Engineering and System Safety, 152, 187–196.
Pasandideh, S.H.R., Akhavan Niaki, S.T., & Asadi, K. (2015). Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Information Science, 292, 57–74.
Rigdon, S.E., Basu, A.P. (2000). Statistical Methods for the Reliability of Repairable Systems. Wiley, New York.
Shahrokhi, M. (2018). Developing an Approach to Calculate Fuzzy Reliability Based on Fuzzy Failure Rate. Industrial Management Journal, 10(2), 183-200.
Stenström, C., Parida, A., & Kumar, U. (2016). Measuring and monitoring operational availability of rail infrastructure. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 230(5), 1457–1468.
Tavakkoli-Moghaddam, R., Safari, J., Sassani, F. (2008). Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm. Reliability Engineering & System Safety, 93 (4), 550-556.
Tsadiras, A.K., Papadopoulos, C.T., & O’Kelly, M.E.J. (2013). An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines. Computers & Industrial Engineering, (66), 1150–1162.
Weiss, S., Arne Schwarz, J., & Stolletz R.(2018). The Buffer Allocation Problem in production lines: Formulations, solution methods, and instances. IISE Transactions. DOI: 10.1080/24725854.2018.1442031.
Yahyatabar, A., & Najafi, A.A. (2017). A quadratic reproduction based Invasive Weed Optimization algorithm to minimize periodic preventive maintenance cost for series-parallel systems. Computers & Industrial Engineering, 110, 436-461.
Yelkenci Kose, S., & Kilincci, O. (2015). Hybrid approach for buffer allocation in open serial production lines, Computers & Operations Research, 60(c), 67-78
Yokota, T., Gen, M. and Ida, K. (1995). System Reliability of Optimization Problems with Several Failure Modes by Genetic Algorithm. Japanese Journal of Fuzzy Theory and systems, 7, 117-135.
Yue, H., Xing, K., Hu H., Wu W., Su H.(2018). Resource failure and buffer space allocation control for automated manufacturing systems. Information Sciences, 450, 392–408.
Zheng, Z., Zhou, W., Zheng, Y., & Wu, Y. (2016). Optimal maintenance policy for a system with preventive repair and two types of failures, Computers & Industrial Engineering, 98, 102-112.
Zoulfaghari, H., Zeinal Hamadani, A., & Abouei Ardakan, M. (2014). Bi-objective redundancy allocation problem for a system with mixed repairable and non-repairable components. ISA Transactions, 53, 17–24.
)