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Analysis of a multicriterial buffer capacity optimization problem for a production line. (English. Russian original) Zbl 1373.93379
Autom. Remote Control 78, No. 7, 1276-1289 (2017); translation from Avtom. Telemekh. 2017, No. 7, 125-140 (2017).
Summary: We consider a multicriterial optimization problem for volumes of buffers in a production line. We assume that the line has a series-parallel structure, and during its operation equipment stops occur due to failures, stops that are random in the moments when they arise and in their durations. The volumes of buffers are integer-valued and bounded from above. As criteria we consider the average production rate of the line, capital costs for installing buffers, and the inventory cost for intermediate products. To approximate the Pareto optimal set we use evolutionary algorithms SIBEA and SEMO. Problems with larger dimension experimentally support the advantage of the modified SEMO algorithm with respect to the hypervolume of the resulting set of points.
MSC:
93E20 Optimal stochastic control
90B30 Production models
90B05 Inventory, storage, reservoirs
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[1] Tempelmeier, H., Practical considerations in the optimization of flow production systems, Int. J. Product. Res., 41, 149-170, (2003)
[2] Patchong, A.; Lemoine, T.; Kern, G., Improving car body production at PSA peugeot citroen, Interfaces, 33, 36-49, (2003)
[3] Dallery, Y.; Gershwin, S.B., Manufacturing flow line systems: A review of models and analytical results, Queueing Syst., 12, 3-94, (1992) · Zbl 0782.90048
[4] Dolgui, A.B.; Svirin, Yu.P., Models for estimating the probabilistic productivity of automated industrial complexes, Vestn. Akad. Nauk Belarusi, 1, 59-67, (1995)
[5] Levin, A.A.; Pas’ko, N.I., Computing the productivity of automated lines, Stanki Instrum., 8, 8-10, (1969)
[6] Dubois, D.; Forestier, J.-P., Productivité et en cours moyen d’un ensemble de deux machines séparées par une zone de stockage, RAIRO Automat., 16, 105-132, (1982) · Zbl 0486.90048
[7] Li, J. and Meerkov, S.M., Production Systems Engineering, New York: Springer, 2009. · Zbl 1156.90002
[8] Altiparmak, A.; Bugak, A.; Dengiz, B., Optimization of buffer sizes in assembly systems using intelligent techniques, 1157-1162, (2002)
[9] D’Souza, K.; Khator, S., System reconfiguration to avoid deadlocks in automated manufacturing systems, Comput. Indust. Eng., 32, 445-465, (1997)
[10] Hamada, M.; Martz, H.; Berg, E.; Koehler, A., Optimizing the product-based avaibility of a buffered industrial process, Reliab. Eng. Syst. Safety, 91, 1039-1048, (2006)
[11] Abdul-Kader, W., Capacity improvement of an unreliable production line—an analytical approach, Comput. Oper. Res., 33, 1695-1712, (2006) · Zbl 1087.90021
[12] Dolgui, A.; Eremeev, A.; Kolokolov, A.; Sigaev, V., A genetic algorithm for the allocation of buffer storage capacities in a production line with unreliable machines, J. Math. Modeling Algorithms, 1, 89-104, (2002) · Zbl 1031.90026
[13] Chehade, H.; Yalaoui, F.; Amodeo, L.; Guglielmo, P., Optimisation multiobjectif pour le probl‘eme de dimensionnement de buffers, J. Decision Syst., 18, 257-287, (2009)
[14] Zitzler, E.; Laumanns, M.; Thiele, L., SPEA2: improving the strength Pareto evolutionary algorithm, (2001)
[15] Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evolut. Comput., 6, 182-197, (2002)
[16] Cruz, F.R.B.; Woensel, T.; Smith, J.M., Buffer and throughput trade-offs in M/G/1/K queuing networks: A bicriteria approach, Int. J. Product. Econom., 125, 224-234, (2010)
[17] Sevast’yanov, B.A., The problem of how bunker capacity influences averages idle time for an automated line of machines, Teor. Veroyat. Primen., 7, 438-447, (1962)
[18] Ancelin, B.; Semery, A., Calcul de la productivité d’une ligne integrée de fabrication, RAIRO Autom., Productiq. Inform. Industrielle, 21, 209-238, (1987)
[19] Terracol, C.; David, R., Performance d’une ligne composée de machines et de stocks intermédiaires, RAIRO Automatiq., Productiq. Informatiq. Industrielle, 21, 239-262, (1987)
[20] Dolgui, A.; Eremeev, A.; Kovalyov, M.Y.; Sigaev, V., Complexity of buffer capacity allocation problems for production lines with unreliable machines, J. Math. Modell. Algorithms, 12, 155-165, (2013) · Zbl 1311.90195
[21] Brockhoff, D.; Friedrich, T.; Neumann, F., Analyzing hypervolume indicator based algorithms, Proc. Parallel Probl. Solving from Nature—PPSN X: 10th Int. Conf. 2008, 5199, 651-660, (2008)
[22] Laumanns, M.; Thiele, L.; Zitzler, E.; Welzl, E.; Deb, K., Running time analysis of a multi- objective evolutionary algorithm on a simple discrete optimization problem, Parallel Probl. Solving from Nature, 2002, 2439, 44-53, (2002)
[23] Belous, V.V.; Groshev, S.V.; Karpenko, A.P.; Shibitov, I.A., Software systems for evaluating the quality of Pareto approximations in multicriterial optimization problems. A survey, 300-320, (2014)
[24] Zitzler, E.; Brockhoff, D.; Thiele, L., The hypervolume indicator revisited: on the design of Pareto-compliant indicators via weighted integration, Proc. Conf. Evolut. Multi-Criter. Optim. (EMO 2007), 4403, 862-876, (2007)
[25] Doob, J.L., Stochastic Processes, New York: Wiley, 1953. Translated under the title Veroyatnostnye protsessy, Moscow: Inostrannaya Literatura, 1956. · Zbl 0053.26802
[26] Gershwin, S.B. and Schick, I.C., Continuous Model of an Unreliable Two-Stage Material Flow System with a Finite Interstage Buffer, Report LIDS-R-1039, Massachusetts Inst. of Technology, Cambridge, 1980.
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