ORIGINAL_ARTICLE
Cold standby redundancy optimization for nonrepairable series-parallel systems: Erlang time to failure distribution
In modeling a cold standby redundancy allocation problem (RAP) with imperfect switching mechanism, deriving a closed form version of a system reliability is too difficult. A convenient lower bound on system reliability is proposed and this approximation is widely used as a part of objective function for a system reliability maximization problem in the literature. Considering this lower bound does not necessarily lead to an optimal solution. In this study by assuming that working time of switching mechanism is exponentially distributed, exact value of system reliability is derived analytically through applying Markov process and solving a relevant set of differential-difference equations. The Runge-Kutta numerical scheme is also employed to verify the accuracy of the results. It is assumed that components time to failure follow an Erlang distribution which is appropriate for most engineering design problems by giving the possibility of modeling different increasing hazard functions. A new mathematical model is presented and its performance is evaluated through solving a well-known example in the literature. Results demonstrate that a higher level of system reliability is achievable through implementing the proposed model.
http://www.jise.ir/article_39491_8d691dba7b3a70dd6f174f2431666036.pdf
2017-06-20T11:23:20
2020-08-06T11:23:20
1
16
Cold standby
Redundancy allocation problem
System reliability
Markov process
Differential-difference equations
Meisam
Sadeghi
meisam.sadeghi.m@gmail.com
true
1
Department of Industrial Engineering, K. N. Toosi University of Technology
Department of Industrial Engineering, K. N. Toosi University of Technology
Department of Industrial Engineering, K. N. Toosi University of Technology
AUTHOR
Emad
Roghanian
e_roghanian@kntu.ac.ir
true
2
Department of Industrial Engineering, K. N. Toosi University of Technology
Department of Industrial Engineering, K. N. Toosi University of Technology
Department of Industrial Engineering, K. N. Toosi University of Technology
LEAD_AUTHOR
Hamid
Shahriari
hshahriari@kntu.ac.ir
true
3
Department of Industrial Engineering, K. N. Toosi University of Technology
Department of Industrial Engineering, K. N. Toosi University of Technology
Department of Industrial Engineering, K. N. Toosi University of Technology
AUTHOR
Ardakan, M. A., & Hamadani, A. Z. (2014). Reliability optimization of series–parallel systems with mixed redundancy strategy in subsystems. Reliability Engineering & System Safety, 130, 132-139.
1
Bellman, R., & Dreyfus, S. (1958). Dynamic programming and the reliability of multicomponent devices. Operations Research, 6(2), 200-206.
2
Bulfin, R. L., & Liu, C. Y. (1985). Optimal allocation of redundant components for large systems. IEEE Transactions on Reliability, 34(3), 241-247.
3
Chambari, A., Najafi, A. A., Rahmati, S. H. A., & Karimi, A. (2013). An efficient simulated annealing algorithm for the redundancy allocation problem with a choice of redundancy strategies. Reliability Engineering & System Safety, 119, 158-164.
4
Chambari, A., Rahmati, S. H. A., & Najafi, A. 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.
5
Coit, D. W. (2001). Cold-standby redundancy optimization for nonrepairable systems. Iie Transactions, 33(6), 471-478.
6
Coit, D. W. (2003). Maximization of system reliability with a choice of redundancy strategies. IIE transactions, 35(6), 535-543.
7
Coit, D. W., & Smith, A. E. (1996). Reliability optimization of series-parallel systems using a genetic algorithm. IEEE Transactions on reliability, 45(2), 254-260.
8
Djerdjour, M., & Rekab, K. (2001). A branch and bound algorithm for designing reliable systems at a minimum cost. Applied Mathematics and Computation, 118(2), 247-259.
9
Federowicz, A. J., & Mazumdar, M. (1968). Use of geometric programming to maximize reliability achieved by redundancy. Operations Research, 16(5), 948-954.
10
Fyffe, D. E., Hines, W. W., & Lee, N. K. (1968). System reliability allocation and a computational algorithm. IEEE Transactions on Reliability, 2, 64-69.
11
Govil, K. K., & Agarwala, R. A. (1983). Lagrange multiplier method for optimal reliability allocation in a series system. Reliability Engineering, 6(3), 181-190.
12
Kuo, W., & Prasad, V. R. (2000). An annotated overview of system-reliability optimization. IEEE Transactions on reliability, 49(2), 176-187.
13
Misra, K. B., & Sharma, U. (1991). An efficient algorithm to solve integer-programming problems arising in system-reliability design. IEEE Transactions on Reliability, 40(1), 81-91.
14
Nakagawa, Y., & Miyazaki, S. (1981). Surrogate constraints algorithm for reliability optimization problems with two constraints. IEEE Transactions on Reliability, 30(2), 175-180.
15
Sadjadi, S. J., & Soltani, R. (2015). Minimum–maximum regret redundancy allocation with the choice of redundancy strategy and multiple choice of component type under uncertainty. Computers & Industrial Engineering, 79, 204-213.
16
Safari, J. (2012). Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies. Reliability Engineering & System Safety, 108, 10-20.
17
Soltani, R. (2014). Reliability optimization of binary state non-repairable systems: A state of the art survey. International Journal of Industrial Engineering Computations, 5(3), 339.
18
Soltani, R., Sadjadi, S. J., & Tavakkoli-Moghaddam, R. (2014). Interval programming for the redundancy allocation with choices of redundancy strategy and component type under uncertainty: Erlang time to failure distribution. Applied Mathematics and Computation, 244, 413-421.
19
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.
20
Yalaoui, A., Chu, C., & Châtelet, E. (2005). Reliability allocation problem in a series–parallel system. Reliability engineering & system safety, 90(1), 55-61.
21
ORIGINAL_ARTICLE
Minimizing the number of tool switches in flexible manufacturing cells subject to tools reliability using genetic algorithm
Nowadays, flexible manufacturing systems play an effective role in a variety of production and timely response to the needs of their customers. Flexible manufacturing cell is a part of this system that includes machines with flexibility in manufacturing different parts. For many years, minimizing the number of tool switches in the machines has been studied by the researchers. Most research in this field has not considered the limitations related to life and failure of the tool. Therefore, it is necessary to provide a model that, because of restrictions on tool life, the number of tool switches for a flexible cell is minimized. In this study, the impact of the tools reliability on minimizing the number of tool switches is examined. First, a mathematical model is presented for the problem. Because of the complexity of the problem, the exact solution of the problem in medium or large sizes is not possible in a reasonable time. Therefore, genetic meta-heuristic algorithm has been used for solving the problem and Keep tools needed soonest (KTNS) policy has been used to determine the optimal arrangement of tools. Then, some examples of such problem have been solved to evaluate the performance of the presented algorithm.
http://www.jise.ir/article_33655_b336d3237fefd7e86dedeaad450bb15b.pdf
2017-06-20T11:23:20
2020-08-06T11:23:20
17
33
tool switches
Reliability
Flexible manufacturing systems
Genetic algorithm
Hiwa
Farughi
h.farughi@uok.ac.ir
true
1
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
LEAD_AUTHOR
Mohsen
Dolatabadiaa
m.dolatabadi@gmail.com
true
2
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
AUTHOR
Vahed
Moradi
vahed.moradi@ymail.com
true
3
Bonab Branch, Islamic Azad University
Bonab Branch, Islamic Azad University
Bonab Branch, Islamic Azad University
AUTHOR
vida
Karbasi
vidakarbasi93@gmail.com
true
4
Bonab Branch, Islamic Azad University
Bonab Branch, Islamic Azad University
Bonab Branch, Islamic Azad University
AUTHOR
Sobhan
Mostafayi
s.mostafayi@gmail.com
true
5
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
AUTHOR
Adjiashvili, D., Bosio, S. and Zemmer, K., 2015. Minimizing the number of switch instances on a flexible machine in polynomial time. Operations Research Letters, 43(3), pp.317-322.
1
Akturk, M.S., Ghosh, J.B. and Gunes, E.D., 2003. Scheduling with tool changes to minimize total completion time: a study of heuristics and their performance. Naval Research Logistics (NRL), 50(1), pp.15-30.
2
Akturk, M.S. and Avci, S., 1996. Tool allocation and machining conditions optimization for CNC machines. European Journal of Operational Research, 94(2), pp.335-348.
3
Al-Fawzan, M.A. and Al-Sultan, K.S., 2003. A tabu search based algorithm for minimizing the number of tool switches on a flexible machine. Computers & industrial engineering, 44(1), pp.35-47.
4
Altumi, A., 2009. USING GENETIC ALGORITHM IN FMS PART ASSIGNMENT AND TOOL LOADING WITH RELIABILITY CONSIDERATIONS TOOL SHARING ALLOWED. Journal of Engineering Research (Al-Fateh University) Issue, 11, p.100.
5
Amaya, J.E., Cotta, C. and Fernández-Leiva, A.J., 2012. Solving the tool switching problem with memetic algorithms. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 26(02), pp.221-235.
6
Amaya, J.E., Cotta, C. and Leiva, A.J.F., 2010. Hybrid cooperation models for the tool switching problem. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (pp. 39-52). Springer Berlin Heidelberg.
7
Bard, J.F., 1988. A heuristic for minimizing the number of tool switches on a flexible machine. IIE transactions, 20(4), pp.382-391.
8
Buyurgan, N., Saygin, C. and Kilic, S.E., 2004. Tool allocation in flexible manufacturing systems with tool alternatives. Robotics and Computer-Integrated Manufacturing, 20(4), pp.341-349.
9
Catanzaro, D., Gouveia, L. and Labbé, M., 2015. Improved integer linear programming formulations for the job Sequencing and tool Switching Problem. European Journal of Operational Research, 244(3), pp.766-777.
10
Chaves, A.A., Lorena, L.A.N., Senne, E.L.F. and Resende, M.G.C., 2016. Hybrid method with CS and BRKGA applied to the minimization of tool switches problem. Computers & Operations Research, 67, pp.174-183.
11
Colledani, M. and Yemane, A., 2013. Impact of Machine Reliability Data Uncertainty on the Design and Operation of Manufacturing Systems. Procedia CIRP, 7, pp.557-562.
12
Crama, Y., Moonen, L.S., Spieksma, F.C. and Talloen, E., 2007. The tool switching problem revisited. European Journal of Operational Research, 182(2), pp.952-957.
13
Crama, Y. and Van De Klundert, J., 1997. Cyclic scheduling of identical parts in a robotic cell. Operations Research, 45(6), pp.952-965.
14
Crama, Y., Oerlemans, A.G. and Spieksma, F.C., 1996. Minimizing the number of tool switches on a flexible machine (pp. 191-223). Springer Berlin Heidelberg.
15
Das, K. and Abdul-Kader, W., 2011. Consideration of dynamic changes in machine reliability and part demand: a cellular manufacturing systems design model. International Journal of Production Research, 49(7), pp.2123-2142.
16
Das, K., Baki, M.F. and Li, X., 2009. Optimization of operation and changeover time for production planning and scheduling in a flexible manufacturing system. Computers & Industrial Engineering, 56(1), pp.283-293.
17
Das, K., Lashkari, R.S. and Sengupta, S., 2007. Machine reliability and preventive maintenance planning for cellular manufacturing systems. European Journal of Operational Research, 183(1), pp.162-180.
18
Ghiani, G., Grieco, A. and Guerriero, E., 2010. Solving the job sequencing and tool switching problem as a nonlinear least cost hamiltonian cycle problem. Networks, 55(4), pp.379-385.
19
Gray, A.E., Seidmann, A. and Stecke, K.E., 1993. A synthesis of decision models for tool management in automated manufacturing. Management science, 39(5), pp.549-567.
20
Han, B.T., Zhang, C.B., Sun, C.S. and Xu, C.J., 2006. Reliability analysis of flexible manufacturing cells based on triangular fuzzy number. Communications in Statistics—Theory and Methods, 35(10), pp.1897-1907.
21
Hirvikorpi, M., Nevalainen, O.S. and Knuutila, T., 2006. Job ordering and management of wearing tools. Engineering optimization, 38(2), pp.227-244.
22
Konak, A., Kulturel-Konak, S. and Azizoğlu, M., 2008. Minimizing the number of tool switching instants in Flexible Manufacturing Systems. International Journal of Production Economics, 116(2), pp.298-307.
23
Konak, A. and Kulturel-Konak, S., 2007, April. An ant colony optimization approach to the minimum tool switching instant problem in flexible manufacturing system. In 2007 IEEE Symposium on Computational Intelligence in Scheduling.
24
Kwon, Y. and Fischer, G.W., 2003. A novel approach to quantifying tool wear and tool life measurements for optimal tool management. International Journal of Machine Tools and Manufacture, 43(4), pp.359-368
25
Laporte, G., Salazar-Gonzalez, J.J. and Semet, F., 2004. Exact algorithms for the job sequencing and tool switching problem. IIE Transactions, 36(1), pp.37-45.
26
Lin, Y.K. and Chang, P.C., 2012. Reliability evaluation for a manufacturing network with multiple production lines. Computers & Industrial Engineering, 63(4), pp.1209-1219.
27
Liu, P.H., Makis, V. and Jardine, A.K., 2001. Scheduling of the optimal tool replacement times in a flexible manufacturing system. IIE Transactions, 33(6), pp.487-495.
28
Makis, V., 1995. Optimal replacement of a tool subject to random failure. International journal of production economics, 41(1), pp.249-256.
29
Raduly-Baka, C. and O.S. Nevalainen, The modular tool switching problem. European Journal of Operational Research, 2015. 242(1): p. 100-106.
30
Rodriguez, C.E.P. and de Souza, G.F.M., 2010. Reliability concepts applied to cutting tool change time. Reliability Engineering & System Safety, 95(8), pp.866-873.
31
Sakhaii, M., Tavakkoli-Moghaddam, R., Bagheri, M. and Vatani, B., 2016. A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Applied Mathematical Modelling, 40(1), pp.169-191.
32
Shirazi, R. and Frizelle, G.D.M., 2001. Minimizing the number of tool switches on a flexible machine: an experimental study. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 215(2), pp.253-261.
33
Song, C.Y. and Hwang, H., 2002. Optimal tooling policy for a tool switching problem of a flexible machine with automatic tool transporter. International Journal of Production Research, 40(4), pp.873-883.
34
Tang, C.S. and Denardo, E.V., 1988. Models arising from a flexible manufacturing machine, part I: minimization of the number of tool switches. Operations research, 36(5), pp.767-777.
35
Turkcan, A., Akturk, M.S. and Storer, R.H., 2007. Due date and cost-based FMS loading, scheduling and tool management. International Journal of Production Research, 45(5), pp.1183-1213.
36
Yanasse, H.H. and Rodrigues, R.C.M., 2007. A partial ordering enumeration scheme for solving the minimization of tool switches problem. In INFORMS ANNUAL MEETING SEATTLE (pp. 299-299).
37
You, D. and Pham, H., 2015. Reliability Analysis of the CNC System Based on Field Failure Data in Operating Environments. Quality and Reliability Engineering International.
38
Zeballos, L.J., 2010. A constraint programming approach to tool allocation and production scheduling in flexible manufacturing systems. Robotics and Computer-Integrated Manufacturing, 26(6), pp.725-743.
39
Wang, K.S., Lin, W.S. and Hsu, F.S., 2001. A new approach for determining the reliability of a cutting tool. The International Journal of Advanced Manufacturing Technology, 17(10), pp.705-709.
40
Wang, D., Song, C. and Barabási, A.L., 2013. Quantifying long-term scientific impact. Science, 342(6154), pp.127-132.
41
ORIGINAL_ARTICLE
Multi-Objective Economic-Statistical Design of VSSI-MEWMA-DWL Control Chart with Multiple Assignable Causes
This paper proposes a multi-objective model for the economic-statistical design of the variable sample size and sampling interval multivariate exponentially weighted moving average control chart by using double warning lines. The Markov chain approach is used to obtain the statistical properties. We extend the Lorenzen and Vance cost function considering multiple assignable causes and multivariate Taguchi loss approach to obtain the expected cost per time unit. The meta-heuristic non-dominated sorting genetic algorithm is used to search for the Pareto optimal solutions. A numerical example is provided to illustrate the solution procedure. Finally, sensitivity analyses for some parameters are given.
http://www.jise.ir/article_16175_ae4d51ad0a744b27cc20cc89b5299655.pdf
2017-06-22T11:23:20
2020-08-06T11:23:20
34
58
Multivariate exponentially weighted moving average control chart
variable sample size sampling interval
double warning lines
multi-objective economic-statistical design
non-dominated sorting genetic algorithm
Raziyeh
Ghanaatiyan
r.ghanaatiyan@gmail.com,
true
1
Department of Industrial Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, Shahed University, Tehran, Iran
AUTHOR
Amirhossein
Amiri
amiri@shahed.ac.ir
true
2
Department of Industrial Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, Shahed University, Tehran, Iran
LEAD_AUTHOR
Fatemeh
Sogandi
f.sogandi1990@gmail.com
true
3
Department of Industrial Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, Shahed University, Tehran, Iran
Department of Industrial Engineering, Shahed University, Tehran, Iran
AUTHOR
Amiri, A., Mogouie, H., and Doroudyan, M. H. (2013). Multi-objective economic-statistical design of MEWMA control chart. International Journal of Productivity and Quality Management, 11(2), 131-149.
1
Niaki, S. T. A., Ershadi, M. J., and Malaki, M. (2010). Economic and economic-statistical designs of MEWMA control charts—a hybrid Taguchi loss, Markov chain, and genetic algorithm approach. The International Journal of Advanced Manufacturing Technology, 48(1-4), 283-296.
2
Prabhu, S. S., and Runger, G. C. (1997). Designing a multivariate EWMA control chart. Journal of Quality Technology, 29(1), 8-15.
3
Reynolds Jr, M. R., and Cho, G. Y. (2011). Multivariate control charts for monitoring the mean vector and covariance matrix with variable sampling intervals. Sequential Analysis, 30(1), 1-40.
4
Rigdon, S. E. (1995). An integral equation for the in-control average run length of a multivariate exponentially weighted moving average control chart. Journal of Statistical Computation and Simulation, 52(4), 351-365.
5
Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics, 1(3), 239-250.
6
Runger, G. C., and Prabhu, S. S. (1996). A Markov chain model for the multivariate exponentially weighted moving averages control chart. Journal of the American Statistical Association, 91(436), 1701-1706.
7
Safaei, A. S., Kazemzadeh, R. B., & Niaki, S. T. A. (2012). Multi-objective economic statistical design of X-bar control chart considering Taguchi loss function. The International Journal of Advanced Manufacturing Technology, 59(9-12), 1091-1101.
8
Safaei, A. S., Kazemzadeh, R. B., & Niaki, S. T. A. (2012). Multi-objective design of an S control chart for monitoring process variability. International Journal of Multi-criteria Decision Making, 2(4), 408-424.
9
Saniga, E. M. (1989). Economic statistical control-chart designs with an application to and R charts. Technometrics, 31(3), 313-320.
10
Seif, A., Faraz, A., and Sadeghifar, M. (2014). Evaluation of the economic statistical design of the multivariate T2 control chart with multiple variable sampling intervals scheme: NSGA-II approach. Journal of Statistical Computation and Simulation, Published online.
11
Woodall, W. H., and Ncube, M. M. (1985). Multivariate CUSUM quality-control procedures. Technometrics, 27(3), 285-292.
12
Yang, W. A., Guo, Y., & Liao, W. (2012). Economic and statistical design of and S control charts using an improved multi-objective particle swarm optimisation algorithm. International Journal of Production Research, 50(1), 97-117.
13
ORIGINAL_ARTICLE
A Hybrid Fire Fly and Differential Evolution Algorithm for Optimization of a Mixed Repairable and Non-Repairable System Reliability Problem
In this paper, a hybrid meta-heuristic approach is proposed to optimize the mathematical model of a system with mixed repairable and non-repairable components. In this system, repairable and non-repairable components are connected in series. Redundant components and preventive maintenance strategies are applied for non-repairable and repairable components, respectively. The problem is formulated as a bi-objective mathematical programming model aiming to reach a tradeoff between system reliability and cost. By hybridizing a standard multi-objective fire fly (MOFA) and differential evolution (DE) algorithms, a powerful and efficient approach called MOF-DE algorithm which has inherited the advantages of the two algorithms is introduced to solve this problem. In order to achieve the best performance of MOF-DE, response surface methodology (RSM) is used to set proper values for the algorithm parameters. To evaluate the performance of the proposed algorithm, various numerical examples are tested and results are compared with methods like NSGA-II, MOPSO and standard MOFA. From the experiments, it is concluded that the performance of the MOF-DE algorithm is better than other methods at finding promising solutions. Finally, sensitivity analysis is carried out to investigate behavior of the proposed algorithm.
http://www.jise.ir/article_40935_253473d803a4f6df1ecf70a642856db8.pdf
2017-06-23T11:23:20
2020-08-06T11:23:20
59
77
Meta-heuristics
Reliability
Firefly algorithm(FA)
Differential evolution (DE)
Multi-Objective Optimization
preventive maintenance
Shima
MohammadZadeh
shima.488@gmail.com
true
1
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
SeyedJafar
Sadjadi
sjsadjadi@iust.ac.ir
true
2
Industrial Engineering Department, University of Science and Technology, Tehran, Iran
Industrial Engineering Department, University of Science and Technology, Tehran, Iran
Industrial Engineering Department, University of Science and Technology, Tehran, Iran
AUTHOR
Gandomi, A., Yang, X., & Alavi, A. (2011). Mixed variable structural optimization using Fireﬂy Algorithm. Computers and Structures, 89, 2325-2336.
1
Amirabadi, H., Khalili, K., Foorginejad, A., & Ashoori, J. (2013). Modeling of abrasive water-jet cutting of glass using artificial neural network and optimization of surface roughness using firefly algorithm. Modares Mechanical Engineering, 13, 123-134.
2
Azaron, A., Katagiri, H., Kato, K., & Sakawa, M. (2005). Reliability evaluation and optimization of dissimilar-component cold-standby redundant systems. Journal of the Operations Research Society of Japan, 48(1), 71-88.
3
Chai-ead, N., Aungkulanon, P., & Luangpaiboon, P. (2011). Bees and Firefly Algorithms for Noisy Non-Linear Optimisation Problems. International MultiConference of Engineers and Computer Scientist. Hong Kong.
4
Chern, M. (1992). On the computational complexity of reliability redundancy allocation in a series system. Operations Research Letters, 11, 309-315.
5
Coit, D. (2001). Cold-standby redundancy optimization for nonrepairable systems. Iie Transactions,, 33(6), 471-478.
6
Das, S., & Nagaratnam Suganthan, P. (2011). Differential Evolution: A Survey of the State of the Art. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 15, 4-31.
7
De Castro, H., & Cavalca, K. (2006). Maintenance resources optimization applied to a manufacturing system. Reliability Engineering & System Safety, 91(4), 413-420.
8
Dieter, A., Pickard, K., & Bertsche, B. (2010). Periodic renewal of spare parts using Weibull. Quality and Reliability Engineering International, 26(3), 193-198.
9
Gandomi, A., Yang, X., & Alavi, A. (2011). Mixed variable structural optimization using Fireﬂy Algorithm. Computers and Structures, 89, 2325-2336.
10
Amirabadi, H., Khalili, K., Foorginejad, A., & Ashoori, J. (2013). Modeling of abrasive water-jet cutting of glass using artificial neural network and optimization of surface roughness using firefly algorithm. Modares Mechanical Engineering, 13, 123-134.
11
Azaron, A., Katagiri, H., Kato, K., & Sakawa, M. (2005). Reliability evaluation and optimization of dissimilar-component cold-standby redundant systems. Journal of the Operations Research Society of Japan, 48(1), 71-88.
12
Chai-ead, N., Aungkulanon, P., & Luangpaiboon, P. (2011). Bees and Firefly Algorithms for Noisy Non-Linear Optimisation Problems. International MultiConference of Engineers and Computer Scientist. Hong Kong.
13
Chern, M. (1992). On the computational complexity of reliability redundancy allocation in a series system. Operations Research Letters, 11, 309-315.
14
Coit, D. (2001). Cold-standby redundancy optimization for nonrepairable systems. Iie Transactions,, 33(6), 471-478.
15
Das, S., & Nagaratnam Suganthan, P. (2011). Differential Evolution: A Survey of the State of the Art. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 15, 4-31.
16
De Castro, H., & Cavalca, K. (2006). Maintenance resources optimization applied to a manufacturing system. Reliability Engineering & System Safety, 91(4), 413-420.
17
Dieter, A., Pickard, K., & Bertsche, B. (2010). Periodic renewal of spare parts using Weibull. Quality and Reliability Engineering International, 26(3), 193-198.
18
Dos Santos Coelho, L., de Andrade Bernert, D. L., & Mariani, V. C. (2011). A Chaotic Firefly Algorithm Applied to Reliability-Redundancy Optimization. IEEE Congress on Evolutionary Computation (CEC). New Orleans, LA.
19
Garg, H. (2015). An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm. Beni-Suef University Journal of Basic and Applied Sciences, 4(1), 14-25.
20
Goel, N., Gupta, D., & Goel, S. (2013). Performance of Firefly and Bat Algorithm for Unconstrained Optimization Problems. International Journal of Advanced Research in Computer Science and Software Engineering, 31, 1405-1409.
21
He, Q., Hu, X., Ren, H., & Zhang, H. (2015). A novel artificial fish swarm algorithm for solving large-scale reliability–redundancy application problem. ISA transactions, 59, 105-113.
22
Heungseob, K., & Pansoo, K. (2017). Reliability models for a nonrepairable system with heterogeneous components having a phase-type time-to-failure distribution. Reliability models for a nonrepairable system with heteroReliability Engineering & System Safety, 37-46.
23
Huang, H.-Z., Qu, J., & Zuo, M. J. (2009). Genetic-algorithm-based optimal apportionment of reliability and redundancy under multiple objective. IIE Transactions, 41, 287–298.
24
Jardine, A., & Buzacott, J. (1985). Equipment reliability and maintenance. European Journal of Operational Research, 19(3), 285-296.
25
Khalili-Damghani, K., Abtahi, A.-R., & Tavana, M. (2013). A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems. Reliability Engineering & System Safety, 111, 58-75.
26
Kumar, R., Izui, K., Yoshimura, M., & Nishiwaki, S. (2009). Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization. Reliability Engineering and System Safety, 94, 891-904.
27
Li, H., & Ye, C. (2012). Firefly Algorithm on Multi-Objective Optimization of Production Scheduling System. Advances in Mechanical Engineering and its Applications, 3, 258-262.
28
Li, Z., Liao, H., & Coit, D. (2009). A two-stage approach for multi-objective decision making with applications to system reliability optimization. Reliability Engineering and System Safety, 94, 1585-1592.
29
Liang, Y.-C., & Lo, M.-H. (2010). Multi-objective redundancy allocation optimization using a variable neighborhood search algorithm. Journal of Heuristics, 16, 511–535.
30
Marichelvam, M., Prabaharan, T., & Yang, X. (2014). A Discrete Firefly Algorithm for the Multi-objective Hybrid Flowshop Scheduling Problems. IEEE TRANSCATIONS ON EVOLUTIONARY COMPUTATION, 18, 301 - 305.
31
MohammadZadeh Dogahe, S., & Sadjadi, S. (2015). A New Biobjective Model to Optimize Integrated Redundancy Allocation and Reliability-Centered Maintenance Problems in a System Using Metaheuristics. Mathematical Problems in Engineering.
32
Najafi, A., Akhavan Niaki, S., & Shahsavar, M. (2009). A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations. Computers &OperationsResearch, 36, 2994-3001.
33
Rebaiaia, M. L., Ait-Kadi, D., Rahimi, S. A., & Jamshidi, A. (2016). Numerical comparative analysis between age and block maintenance strategies in the presence of probability distributions with increasing failure rate. International Federation of Automatic Control, (pp. 1904-1909).
34
Rigdon, S. (2008). Reliability Optimization. In Encyclopedia of Statistics in Quality and Reliability (pp. 1599-1604). John Wiley & Sons.
35
Ruiz-Vanoye, J., & Díaz-Parra, O. (2011). Similarities between meta-heuristics algorithms and the science of life. Central European Journal of Operations Research, 19, 445-66.
36
Safari, J. (2012). Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies. Reliability Engineering & System Safety, 108, 10-20.
37
Salmasnia, A., Ameri, E., & Niaki, S. (2016). A robust loss function approach for a multi-objective redundancy allocation problem. Applied Mathematical Modelling, 40(1), 635-645.
38
Sayadi, M., Hafezalkotob, A., & Jalali Naini, S. (2013). Firefly-inspired algorithm for discrete optimization problems: An application to manufacturing cell formation. Journal of Manufacturing Systems, 32, 78-84.
39
Soltani, R., Safari, J., & Sadjadi, S. (2015). Robust counterpart optimization for the redundancy allocation problem in series-parallel systems with component mixing under uncertainty. Applied Mathematics and Computation, 80-88.
40
Suman, B. (2003). Simulated annealing-based multiobjective algorithms and their application for system reliability. Engineering Optimization, 35, 391–416.
41
Taboadaa, H., Baheranwalaa, F., & Coit, D. (2007). Practical solutions for multi-objective optimization: An application to system reliability design problems. Reliability Engineering and System Safety, 92, 314-322.
42
Weibull, W. (1951). A Statistical Distribution Function of Wide Applicability. Journal of applied mechanics.
43
Yang, X. (2010). Nature-inspired metaheuristic algorithm. Luniver Press.
44
Yang, X. (2013). Multiobjective Firefly Algorithm for Continuous Optimization. Engineering with Computers, 29, 175-184.
45
Yang, X.-S., & He, X. (2013). Fireﬂy Algorithm: Recent Advances and Applications. Journal of Swarm Intelligence, 1, 36-50.
46
Yu, X., & Gen, M. (2010). Introduction to evolutionary algorithms. Springer.
47
Zhao, J.-H., Liu, Z., & Dao, M.-T. (2007). Reliability optimization using multiobjective ant colony system approaches. Reliability Engineering and System Safety, 92, 109-120.
48
Zoulfaghari, H., 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.
49
ORIGINAL_ARTICLE
Performance of cumulative count of conforming chart with variable sampling intervals in the presence of inspection errors
In high quality industrial processes, the control chart is design based on cumulative count of conforming (CCC) items is very useful. In this paper, the performance of CCC-r chart with variable sampling intervals (CCC-rVSI chart) in the presence ofinspectionerrors isinvestigated. The efficiency of CCC-rVSI chart is compared with CCC-r chart with fixed sampling interval (CCC-rFSI chart). The comparison results show thatthe VSI scheme can performs better than the FSI scheme. In addition, analysis and discussion of the results are presented to illustrate the effect of input parameters on the performance of CCC-rVSI chart.
http://www.jise.ir/article_41761_034d1bc574e806d75d870e595592278a.pdf
2017-06-25T11:23:20
2020-08-06T11:23:20
78
92
high quality processes
CCC-r chart
variable sampling interval
inspections errors
average time to signal
Mohammad Saber
Fallahnezhad
fallahnezhad@yazd.ac.ir
true
1
Industrial Engineering Department, yazd university, Iran
Industrial Engineering Department, yazd university, Iran
Industrial Engineering Department, yazd university, Iran
LEAD_AUTHOR
yousof
shamstabar
y.shamstabar@gmail.com
true
2
Industrial Engineering Department, Yazd university, Iran
Industrial Engineering Department, Yazd university, Iran
Industrial Engineering Department, Yazd university, Iran
AUTHOR
Amin, R. W., & Miller, R. W. (1993). A robustness study of X charts with variable sampling intervals. Journal of Quality Technology, 25, 36-36.
1
Aparisi, F., & Haro, C. L. (2001). Hotelling's T2 control chart with variable sampling intervals. International Journal of Production Research, 39(14), 3127-3140.
2
Burke, R. J., Davis, R. D., Kaminsky, F. C., & Roberts, A. E. (1995). The effect of inspector errors on the true fraction non-conforming: an industrial experiment. Quality Engineering, 7(3), 543-550.
3
Calvin, T. (1983). Quality Control Techniques for" Zero Defects". IEEE Transactions on Components, Hybrids, and Manufacturing Technology, 6(3), 323-328.
4
Case, K. E. (1980). The p control chart under inspection error. Journal of Quality Technology, 12(1), 1-9.
5
Castagliola, P., Celano, G., & Fichera, S. (2006). Evaluation of the statistical performance of a variable sampling interval R EWMA control chart. Quality Technology & Quantitative Management, 3(3), 307-323.
6
Castagliola, P., Celano, G., Fichera, S., & Giuffrida, F. (2006). A variable sampling interval S2-EWMA control chart for monitoring the process variance. International Journal of Technology Management, 37(1-2), 125-146.
7
Chen, S.-L., & Chung, K.-J. (1994). Inspection error effects on economic selection of target value for a production process. European Journal of Operational Research, 79(2), 311-324.
8
Chen, Y.-K. (2013). Cumulative conformance count charts with variable sampling intervals for correlated samples. Computers & Industrial Engineering, 64(1), 302-308.
9
Chen, Y. K., Chen, C. Y., & Chiou, K. C. (2011). Cumulative conformance count chart with variable sampling intervals and control limits. Applied stochastic models in business and industry, 27(4), 410-420.
10
Fallahnezhad, M., & Babadi, A. Y. (2015). A New Acceptance Sampling Plan Using Bayesian approach in the presence of inspection errors. Transactions of the Institute of Measurement and Control, 37(9), 1060-1073.
11
Fallah Nezhad, M. S., Yousefi Babadi, A., Owlia, M. S., & Mostafaeipour, A. (2015). A recursive Approach for lot Sentencing problem in the Presence of inspection errors. Communications in Statistics-Simulation and Computation, (just-accepted), 00-00.
12
Goh, T. (1987). A control chart for very high yield processes. Quality Assurance, 13(1), 18-22.
13
Huang, Q., Johnson, N. L., & Kotz, S. (1989). Modified Dorfman-Sterrett screening (group testing) procedures and the effects of faulty inspection. Communications in Statistics-Theory and Methods, 18(4), 1485-1495.
14
Joekes, S., Smrekar, M., & Righetti, A. F. (2016). A comparative study of two proposed CCC-r charts for high quality processes and their application to an injection molding process. Quality Engineering, 1-9.
15
Johnson, N. L., Kotz, S., & Wu, X.-Z. (1991). Inspection errors for attributes in quality control (Vol. 44): CRC Press.
16
Kudo, K., Ohta, H., & Kusukawa, E. (2004). Economic Design of A Dynamic CCC–r Chart for High-Yield Processes. Economic Quality Control, 19(1), 7-21.
17
Lindsay, B. G. (1985). Errors in inspection: integer parameter maximum likelihood in a finite population. Journal of the American Statistical Association, 80(392), 879-885.
18
Liu, J., Xie, M., Goh, T., Liu, Q., & Yang, Z. (2006). Cumulative count of conforming chart with variable sampling intervals. International Journal of Production Economics, 101(2), 286-297.
19
Lu, X., Xie, M., & Goh, T. (2000). An investigation of the effects of inspection errors on the run-length control charts. Communications in Statistics-simulation and Computation, 29(1), 315-335.
20
Luo, Y., Li, Z., & Wang, Z. (2009). Adaptive CUSUM control chart with variable sampling intervals. Computational Statistics & Data Analysis, 53(7), 2693-2701.
21
Nezhad, M. F., & Nasab, H. H. (2012). A new Bayesian acceptance sampling plan considering inspection errors. Scientia Iranica, 19(6), 1865-1869.
22
Ohta, H., Kusukawa, E., & Rahim, A. (2001). A CCC‐r chart for high‐yield processes. Quality and Reliability Engineering International, 17(6), 439-446.
23
Ranjan, P., Xie, M., & Goh, T. (2003). Optimal control limits for CCC charts in the presence of inspection errors. Quality and Reliability Engineering International, 19(2), 149-160.
24
Reynolds Jr, M. R., & Arnold, J. C. (1989). Optimal one-sided Shewhart control charts with variable sampling intervals. Sequential Analysis, 8(1), 51-77.
25
Reynolds, M. R., Amin, R. W., & Arnold, J. C. (1990). CUSUM charts with variable sampling intervals. Technometrics, 32(4), 371-384.
26
Reynolds, M. R., Amin, R. W., Arnold, J. C., & Nachlas, J. A. (1988). Charts with variable sampling intervals. Technometrics, 30(2), 181-192.
27
Runger, G.C., & Montgomery,D. (1993). Adaptive sampling enhancements for Shewhart control charts. IIE transactions, 25(3), 41-51.
28
Runger, G. C., & Pignatiello Jr, J. J. (1991). Adaptive sampling for process control. Journal of Quality Technology, 23(2), 135-155.
29
Ryan, T. P. (2011). Statistical methods for quality improvement: John Wiley & Sons.
30
Saccucci, M. S., Amin, R. W., & Lucas, J. M. (1992). Exponentially weighted moving average control schemes with variable sampling intervals. Communications in Statistics-simulation and Computation, 21(3), 627-657.
31
Shamma, S. E., Amin, R. W., & Shamma, A. K. (1991). A double exponentially weigiited moving average control procedure with variable sampling intervals. Communications in Statistics-simulation and Computation, 20(2-3), 511-528.
32
Suich, R. (1988). The c control chart under inspection error. Journal of Quality Technology, 20(4), 263-266.
33
Suich, R. (1990). The effects of inspection errors on acceptance sampling for nonconformities. Journal of Quality Technology, 22(4), 314-318.
34
Villalobos, J. R., Muñoz, L., & Gutierrez, M. A. (2005). Using fixed and adaptive multivariate SPC charts for online SMD assembly monitoring. International Journal of Production Economics, 95(1), 109-121.
35
Wang, R.-C., & Chen, C.-H. (1997). Minimum average fraction inspected for continuous sampling plan CSP-1 under inspection error. Journal of Applied Statistics, 24(5), 539-548.
36
Xie, M., Goh, T. N., & Kuralmani, V. (2012). Statistical models and control charts for high-quality processes: Springer Science & Business Media.
37
Zhang, M., Nie, G., & He, Z. (2014). Performance of cumulative count of conforming chart of variable sampling intervals with estimated control limits. International Journal of Production Economics, 150, 114-124.
38
Zhang, Y., Castagliola, P., Wu, Z., & Khoo, M. B. (2012). The variable sampling interval X chart with estimated parameters. Quality and Reliability Engineering International, 28(1), 19-34.
39
ORIGINAL_ARTICLE
The effect of parameter estimation on Phase II control chart performance in monitoring financial GARCH processes with contaminated data
The application of control charts for monitoring financial processes has received a greater focus after recent global crisis. The Generelized AutoRegressive Conditional Heteroskedasticity (GARCH) time series model is widely applied for modelling financial processes. Therefore, traditional Shewhart control chart is developed to monitor GARCH processes. There are some difficulties in financial surveillance especially in the retrospective phase one of which being the posibility of existing outliers in the samples data. For this aim, in this paper some methods were proposed to estimate the parameters of the GARCH model based on maximum likelihood and robust estimation procedures. Then, the performance of Phase II residual Shewhart control chart with estimated parameters was evaluated according to in-control Average Run Length in the presence of outliers. The Monte Carlo simulation study was applied to evaluate the proposed methods considering different numerical examples. Finally, the US Dollar/Iran Rial (USD/IRR) exchange rate was considered for monitoring in which the results showed that the control chart was more sensitive when the robust methods were applied in the estimation procedure.
http://www.jise.ir/article_41762_8c76916db35e84b5303524e9403403fe.pdf
2017-06-27T11:23:20
2020-08-06T11:23:20
93
108
Financial surveillance
retrospective phase
GARCH model
robust estimation
foreign exchange rate
Mohammad Salleh
Owlia
owliams@yazd.ac.ir
true
1
Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
Mohammad Hadi
Doroudyan
doroudyan@stu.yazd.ac.ir
true
2
Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
AUTHOR
Amirhossein
Amiri
amiri@shahed.ac.ir
true
3
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
AUTHOR
Hojatollah
Sadeghi
sadeqi@yazd.ac.ir
true
4
Department of Business Management, Faculty of Economics, Yazd University, Yazd, Iran
Department of Business Management, Faculty of Economics, Yazd University, Yazd, Iran
Department of Business Management, Faculty of Economics, Yazd University, Yazd, Iran
AUTHOR
Adams, B.M., & Tseng, I.T. (1998). Robustness of forecast-based monitoring schemes, Journal of Quality Technology, 30(4), 328-339.
1
Apley, D.W. (2002). Time series control charts in the presence of model uncertainty, Journal of Manufacturing Science and Engineering, 124(4), 891-898.
2
Araghi, M.K., & Pak, M.M. (2013). Assessing the exchange rate fluctuation on Tehran’s stock market price: a GARCH application, International Journal of Management and Business Research, 2(2), 95-107.
3
Berkes, I., Horváth, L., & Kokoszka, P. (2003). GARCH processes: structure and estimation, Bernoulli 9(2), 201-207.
4
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31(3), 307-327.
5
Boyles, R.A. (2000). Phase I analysis for autocorrelated processes, Journal of Quality Technology, 32(4), 395-409.
6
Carnero, M.A., de Rivera, D.P.S., & Ortega, E.R. (2008). Estimating and forecasting GARCH volatility in the presence of outliers, Instituto Valenciano de Investigaciones Económicas, SA (Ivie), (13), 1-5.
7
Chin, C.H., & Apley, D.W. (2008). Performance and robustness of control charting methods for autocorrelated data, Journal of Korean Institute of Industrial Engineers, 34(2), 122-139.
8
Dasdemir, E., Weiß, C., Testik, M.C., & Knoth, S. (2016). Evaluation of Phase I analysis scenarios on Phase II performance of control charts for autocorrelated observations, Quality Engineering, 28(3), 293-304.
9
Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of UK inflation, Econometrica, 50(4), 987-1008.
10
Engle, R.F., & Bollerslev, T. (1986). Modeling the persistence of conditional variances, Econometric Reviews, 5(1), 1-50.
11
Fahimifard, S.M., Homayounifar, M., Sabouhi, M., & Moghaddamnia, A.R. (2009). Comparison of ANFIS, ANN, GARCH and ARIMA techniques to exchange rate forecasting, Journal of Applied Sciences, 9(20), 3641-3651.
12
Frisen, M. (2008). Financial Surveillance, John Wiley & Sons.
13
Garthoff, R., Golosnoy, V., & Schmid, W. (2014). Monitoring the mean of multivariate financial time series, Applied Stochastic Models in Business and Industry, 30(3), 328-340.
14
Golosnoy, V. (2016). Sequential monitoring of portfolio betas, Published online in Statistical Papers, 1-22. doi: 10.1007/s00362-016-0783-6
15
Grossi, L., & Morelli, G. (2006). Robust volatility forecasts and model selection in financial time series, Department of Economics, Parma University, Italy, No. 2006-SE02.
16
Herwartz, H., & Reimers, H.E. (2002). Empirical modelling of the DEM/USD and DEM/JPY foreign exchange rate: Structural shifts in GARCH models and their implications, Applied Stochastic Models in Business and Industry, 18(1), 3-22.
17
Scholz, F.W. (2006). Maximum Likelihood Estimation, Encyclopedia of Statistical Sciences. doi: 10.1002/0471667196.ess1571.pub2
18
Hoerl, R.W., & Snee, R.D. (2009). Post financial meltdown: What do the services industries need from us now?, Applied Stochastic Models in Business and Industry, 25(5), 509-521.
19
Hotta, L.K., & Tsay, R.S. (2012). Outliers in GARCH processes, in: Bell, W.R., Holan, S.H., McElroy, T.S. (Eds), Economic time series: modeling and seasonality, Taylor & Francis Group, CRC Press, pp. 337-358.
20
Jones-Farmer, L.A., Woodall, W.H., Steiner, S.H., & Champ, C.W. (2014). An overview of phase I analysis for process improvement and monitoring, Journal of Quality Technology, 46(3), 265-280.
21
Mankiw, N.G. (2014). Principles of Macroeconomics, Cengage Learning.
22
Muler, N., & Yohai, V.J. (2008). Robust estimates for GARCH models, Journal of Statistical Planning and Inference, 138(10), 2918-2940.
23
Norouzzadeh, P., & Rahmani, B. (2006). A multifractal detrended fluctuation description of Iranian rial–US dollar exchange rate, Physica A: Statistical Mechanics and its Applications, 367, 328-336.
24
Oskooee, M.B., & Hegerty, S.W. (2007). Exchange rate volatility and trade flows: a review article, Journal of Economic Studies, 34(3), 211-255.
25
Psarakis, S., Angeliki K.V., & Castagliola, P. (2014). Some recent developments on the effects of parameter estimation on control charts, Quality and Reliability Engineering International, 30(8), 1113-1129.
26
Severin, T., Schmid, W. (1998). Statistical process control and its application in finance, in: Bol, G., Nakhaeizadeh, G., Vollmer, K.H. (Eds.), Risk Measurement, Econometrics and Neural Networks: Selected Articles of the 6th Econometric-Workshop in Karlsruhe, Germany, pp. 83-104.
27
Woodall, W.H., & Montgomery, D.C. (2014). Some current directions in the theory and application of statistical process monitoring, Journal of Quality Technology, 46(1), 78-94.
28
ORIGINAL_ARTICLE
Reliability Based Maintenance and Human Resources Work-Rest Scheduling in Manufacturing System
In today's competitive market, all manufacturers attempt to improve their maintenance policy in order to decrease the cost of failure and increase the quality of products, but most of these attempts do not consider the role of humans involved in a manufacturing system. Human resources are the main factor in manufacturing that has an undeniable effect on products quality, machines reliability, safety and maintenance policy. In this paper we propose a nonlinear mathematical model that optimizes the maintenance policy considering the humans fatigue to investigate its effects on reliability and associated Costs in manufacturing system. That is to say, the model is a reliability based maintenance optimization that aims to maintain the reliability of machines and their human resources in a proper predetermined interval. The performance of the proposed model was examined by some instances and the obtained results indicated this model can provide effectiveness maintenance policy for manufacturing systems.
http://www.jise.ir/article_41763_0a3e18c0cfc5442773bc037d8e8d960c.pdf
2017-06-27T11:23:20
2020-08-06T11:23:20
109
124
maintenance
human resource
fatigue
optimization
Reliability
production
scheduling
Rasoul
Jamshidi
jamshidi.rasoul@gmail.com
true
1
Assistant Professor, School of Engineering, Damghan University
Assistant Professor, School of Engineering, Damghan University
Assistant Professor, School of Engineering, Damghan University
LEAD_AUTHOR
Mansooreh
Moadi
m_moadi@du.ac.ir
true
2
Faculty of Engineering School, Damghan University
Faculty of Engineering School, Damghan University
Faculty of Engineering School, Damghan University
AUTHOR
Azizi, N., Liang, M. and Zolfaghari, S. (2013), ‘Modeling Human Boredom at Work: Mathematical Formulations and a Probabilistic Framework’, Journal of Manufacturing Technology Management, 2013, 24 (5), 711-746.
1
Baines, T.S., Asch, R., Hadfield, L., Mason, J.P., Fletcher, S. and Kay, J.M. (2005), ‘Towards a theoretical framework for human performance modeling within manufacturing systems design’, Simulation Modeling Practice and Theory, 13(6), 486-504.
2
Bechtold, S.E. (1991), ‘Optimal work-rest schedules with a set of fixed-duration rest periods’, Decision Sciences, 22, 157–170.
3
Bechtold, S.E. and Sumners, D.L. (1988), ‘Optimal work–rest scheduling with exponential work-rate decay’, Management Science, 34(4), 547–552.
4
Bechtold, S.E., Janaro, R.E. and Sumners, D.L. (1984), ‘Maximization of labor productivity through multi-rest break scheduling’, Management Science, 30, 1442–1458.
5
Cappadonna, F.A., Costa, A. and Fichera S. (2013), ‘Makespan Minimization of Unrelated Parallel Machines with Limited Human Resources’, Procedia CIRP, 12, 450-455.
6
Dawson, D., Noy, Y.I., Härmä M., Åkerstedt, T. and Belenky, G. (2011), ‘Modeling fatigue and the use of fatigue models in work settings’, Accident Analysis & Prevention, 43(2), 549-564.
7
Godwin, G.U. and Aniekan, A.E. (1999), ‘Human factors affecting the success of advanced manufacturing systems’, Computers & Industrial Engineering, 37, 297-300.
8
Griffith, C.D. and Mahadevan, S. (2011), ‘Inclusion of fatigue effects in human reliability analysis’, Reliability Engineering & System Safety, 96(11), 1437-1447.
9
Guadalupe, M. (2003), ‘The hidden costs of fixed term contracts: the impact on work accidents’, Labour Economics, 10(3), 339-357.
10
Hsieh, A. and Chao, H. (2004), ‘A reassessment of the relationship between job specialization, job rotation and job burnout: Example of Taiwan's high-technology industry’, The International Journal of Human Resource Management, 15(6), 1108-1123.
11
Jaber, M.Y., Givi, Z.S. and Neumann W.P. (2013), ‘Incorporating human fatigue and recovery into the learning–forgetting process’, Applied Mathematical Modeling, 37(12–13), 7287-7299.
12
Jamshidi, R. and Seyyed Esfahani, M. M. (2013), ‘Human resources scheduling to improve the product quality according to exhaustion limit’, Top, Article in press.
13
Jiang, B., Baker, R.C. and Frazier, G.V. (2009), ‘An analysis of job dissatisfaction and turnover to reduce global supply chain risk: Evidence from China’, Journal of Operations Management, 27(2), 169-184.
14
Konz, S. (1998), ‘Work/rest: part II – the scientific basis (knowledge base) for the guide’, International Journal of Industrial Ergonomics, 22 (1–2), 73–99.
15
Koo, J., Kim, B.I. and Kim, Y.S. (2014), ‘Estimating the effects of mental disorientation and physical fatigue in a semi-panic evacuation’, Expert Systems with Applications, 41(5), 2379-2390.
16
Kopardekar P. and Mital, A. (1994), ‘The effect of different work-rest schedules on fatigue and performance of a simulated directory assistance operator’s task’, Ergonomics, 37 (10), 1697–1707.
17
Lodree, E.J., Geiger, C.D. and Jiang, X. (2009), ‘Taxonomy for integrating scheduling theory and human factors: review and research opportunities’, International Journal of Industrial Ergonomics, 39, 39–51.
18
MacCarthy, B.L., and Wilson, J.R. (2001), ‘Human performance in planning and scheduling. London’, Taylor and Francis.
19
Mahdavi, I., Aalaei, A., Paydar, M.M. and Solimanpur, M. (2010), ‘Designing a mathematical model for dynamic cellular manufacturing systems considering production planning and worker assignment’, Computers & Mathematics with Applications, 60(4), 1014-1025.
20
Martorell, S., Villamizar, M., Carlos, S. and Sánchez A. (2010), ‘Maintenance modeling and optimization integrating human and material resources’, Reliability Engineering & System Safety, 95(12), 1293-1299.
21
Mason, S., Baines, T., Kay, J.M. and Ladbrook, J. (2004), ‘Improving the design process for factories: Modeling human performance variation, Journal of Manufacturing Systems’, 24(1), 47-54.
22
Siddiqui, M.H., Iqbal, A. and Manarvi, I.A. (2012), ‘Maintenance Resource Management: A key process initiative to reduce human factors in aviation maintenance’, Aerospace Conference, IEEE, 1(7), 3-10.
23
Smith, Ch.O. (1976), ‘Introduction to Reliability in Design’, McGraw-Hill, 1976.
24
Taylor, J.C. (2000), ‘The evolution and effectiveness of Maintenance Resource Management (MRM)’. International Journal of Industrial Ergonomics, 26, 201-215.
25
Wang, H., and Hu, S.J. (2010), ‘Manufacturing complexity in assembly systems with hybrid configurations and its impact on throughput’, CIRP Annals - Manufacturing Technology, 59 (1), 53–56.
26