×

Approximations of time-dependent unreliable flow lines with finite buffers. (English) Zbl 1342.90042

Summary: Flow lines process discrete workpieces on consecutive machines, which are coupled by buffers. Their operating environment is often stochastic and time-dependent. For the flow line under consideration, the stochasticity is generated by random breakdowns and successive stochastic repair times, whereas the processing times are deterministic. However, the release rate of workpieces to the line is time-dependent, due to changes in demand. The buffers between the machines may be finite or infinite. We introduce two new sampling approaches for the performance evaluation of such flow lines: one method utilizes an approximation based on a mixed-integer program in discrete time with discrete material, while the other approximation is based on partial and ordinary differential equations in continuous time and with a continuous flow of material. In addition, we sketch a proof that these two approximations are equivalent under some linearity assumptions. A computational study demonstrates the accuracy of both approximations relative to a discrete-event simulation in continuous time. Furthermore, we reveal some effects occurring in unreliable flow lines with time-dependent processing rates.

MSC:

90B15 Stochastic network models in operations research
90B30 Production models
Full Text: DOI

References:

[1] Alfieri A, Matta A (2012) Mathematical programming formulations for approximate simulation of multistage production systems. Eur J Oper Res 219(3):773-783 · Zbl 1253.90101 · doi:10.1016/j.ejor.2011.12.044
[2] Bendoly E, Donohue K, Schultz KL (2006) Behavior in operations management: assessing recent findings and revisiting old assumptions. J Oper Manag 24(6):737-752 · doi:10.1016/j.jom.2005.10.001
[3] Buzacott JA, Hanifin LE (1978) Models of automatic transfer lines with inventory banks a review and comparison. AIIE Trans 10(2):197-207 · doi:10.1080/05695557808975204
[4] Chan WK, Schruben L (2008) Optimization models of discrete-event system dynamics. Oper Res 56(5):1218-1237 · Zbl 1167.90443 · doi:10.1287/opre.1080.0559
[5] Coclite GM, Garavello M, Piccoli B (2005) Traffic flow on a road network. SIAM J Math Anal 36(6):1862-1886 · Zbl 1114.90010 · doi:10.1137/S0036141004402683
[6] Dallery Y, Gershwin SB (1992) Manufacturing flow line systems: a review of models and analytical results. Queueing Syst 12(1-2):3-94 · Zbl 0782.90048 · doi:10.1007/BF01158636
[7] D’Apice C, Göttlich S, Herty M, Piccoli B (2010) Modeling, simulation and optimization of supply chains: a continuous approach. SIAM, Philadelphia · Zbl 1205.90003
[8] Davis MHA (1984) Piecewise-deterministic Markov processes: a general class of non-diffusion stochastic models. J R Stat Soc Ser B 46(3):353-388 · Zbl 0565.60070
[9] Davis MHA (1993) Markov models and optimization. Monographs on statistics and applied probability. Chapman & Hall, Boca Raton · Zbl 0780.60002 · doi:10.1007/978-1-4899-4483-2
[10] Degond P, Ringhofer C (2007) Stochastic dynamics of long supply chains with random breakdowns. SIAM J Appl Math 68(1):59-79 · Zbl 1144.90314 · doi:10.1137/060674302
[11] Demir L, Tunali S, Eliiyi DT (2014) The state of the art on buffer allocation problem: a comprehensive survey. J Intell Manuf 25(3):371-392 · doi:10.1007/s10845-012-0687-9
[12] 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. J Math Model Algorithms 1(2):89-104 · Zbl 1031.90026 · doi:10.1023/A:1016560109076
[13] Fan W (1976) Simulation of queueing network with time varying arrival rates. Math Comput Simul 18(3):165-170 · Zbl 0324.68067 · doi:10.1016/0378-4754(76)90048-3
[14] Fügenschuh A, Göttlich S, Herty M, Klar A, Martin A (2008) A discrete optimization approach to large scale supply networks based on partial differential equations. SIAM J Sci Comput 30(3):1490-1507 · Zbl 1161.90002 · doi:10.1137/060663799
[15] Garavello M, Goatin P (2012) The Cauchy problem at a node with buffer. Discrete Contin Dyn Syst Ser A 32(6):1915-1938 · Zbl 1238.90033 · doi:10.3934/dcds.2012.32.1915
[16] Gershwin SB, Schick IC (1983) Modeling and analysis of three-stage transfer lines with unreliable machines and finite buffers. Oper Res 31(2):354-380 · Zbl 0507.90042 · doi:10.1287/opre.31.2.354
[17] Gillespie DT (1976) A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J Comput Phys 22(4):403-434 · doi:10.1016/0021-9991(76)90041-3
[18] Gillespie DT (2001) Approximate accelerated stochastic simulation of chemically reacting systems. J Chem Phys 115(4):1716-1733 · doi:10.1063/1.1378322
[19] Göttlich S, Herty M, Klar A (2005) Network models for supply chains. Commun Math Sci 3(4):545-559 · Zbl 1115.90008 · doi:10.4310/CMS.2005.v3.n4.a5
[20] Göttlich S, Martin S, Sickenberger T (2011) Time-continuous production networks with random breakdowns. Netw Heterog Media 6(4):695-714 · Zbl 1260.90083 · doi:10.3934/nhm.2011.6.695
[21] 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 · Zbl 1209.90135 · doi:10.1007/s10479-010-0692-3
[22] Jaikumar R, Bohn RE (1992) A dynamic approach to operations management: an alternative to static optimization. Int J Prod Econ 27(3):265-282 · doi:10.1016/0925-5273(92)90101-C
[23] Kirchner C, Herty M, Göttlich S, Klar A (2006) Optimal control for continuous supply network models. Netwo Heterog Media 1(4):675-688 · Zbl 1131.90009 · doi:10.3934/nhm.2006.1.675
[24] LeVeque RJ (1992) Numerical methods for conservation laws. Birkhäuser Verlag, Basel · Zbl 0847.65053 · doi:10.1007/978-3-0348-8629-1
[25] Li J, Meerkov SM (2009) Production systems engineering. Springer, New York · Zbl 1156.90002 · doi:10.1007/978-0-387-75579-3
[26] Mourani I, Hennequin S, Xie X (2007) Failure models and throughput rate of transfer lines. Int J Prod Res 45(8):1835-1859 · Zbl 1128.90314 · doi:10.1080/00207540600677716
[27] Nasr WW, Taaffe MR (2013) Fitting the \[Ph_t/M_t/s/c\] Pht/Mt/s/c time-dependent departure process for use in tandem queueing networks. INFORMS J Comput 25(4):758-773 · doi:10.1287/ijoc.1120.0538
[28] Schwarz JA, Selinka G, Stolletz R (2015) Performance analysis of time-dependent queueing systems: survey and classification. Omega. doi:10.1016/j.omega.2015.10.013
[29] Stolletz R, Lagershausen S (2013) Time-dependent performance evaluation for loss-waiting queues with arbitrary distributions. Int J Prod Res 51(5):1366-1378 · doi:10.1080/00207543.2012.678946
[30] Takahashi K, Nakamura N (2002) Decentralized reactive Kanban system. Eur J Oper Res 139(2):262-276 · Zbl 1001.90027 · doi:10.1016/S0377-2217(01)00358-7
[31] Tan B (2015) Mathematical programming representations of the dynamics of continuous-flow production systems. IIE Trans 47(2):173-189 · doi:10.1080/0740817X.2014.892232
[32] Terwiesch C, Bohn RE (2001) Learning and process improvement during production ramp-up. Int J Prod Econ 70(1):1-19 · doi:10.1016/S0925-5273(00)00045-1
[33] Vandergraft JS (1983) A fluid flow model of networks of queues. Manag Sci 29(10):1198-1208 · Zbl 0523.60087 · doi:10.1287/mnsc.29.10.1198
[34] Weiss S, Schwarz JA, Stolletz R (2015) Buffer allocation problems for stochastic flow lines with unreliable machines. In: Proceedings of the 10th conference on stochastic models of manufacturing and service operations. Volos, Greece, pp 271-277
[35] Wu K (2014) Classification of queueing models for a workstation with interruptions: a review. Int J Prod Res 52(3):902-917 · doi:10.1080/00207543.2013.843799
[36] Wu K, McGinnis L, Zwart B (2011) Queueing models for a single machine subject to multiple types of interruptions. IIE Trans 43(10):753-759 · doi:10.1080/0740817X.2010.550907
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.