×
Halim, K. A.; Giri, B. C.; Chaudhuri, K. S.

Fuzzy EPQ models for an imperfect production system. (English) Zbl 1155.90308

Int. J. Syst. Sci. 40, No. 1, 45-52 (2009).
Summary: Manufacturing uncertainties are inevitable as unexpected events may occur at any time. In this article, we consider an imperfect production system in which the production process may shift from the in-control state to the out-of-control state at any random time. We assume that the fraction of defective items produced after process shift is uncertain and can be characterised by a fuzzy number. In another attempt, we take the fraction of defective items produced as a random variable, which follows an exponential probability distribution with fuzzy parameter. The expected fuzzy cost per unit time is derived in each model and defuzzified by using centroid method to obtain crisp values that can be used for comparison purposes. Two numerical examples are taken to demonstrate the optimal results of the proposed fuzzy models numerically and to compare these results with the corresponding ones in crisp models.

Cited in 8 Documents

MSC:

90B05 Inventory, storage, reservoirs
90B30 Production models
93A30 Mathematical modelling of systems (MSC2010)

Keywords:

inventory; imperfect production system; fuzzy set; centroid method

Software:

Mathematica
Cite Review PDF
Full Text: DOI

References:

[1] Bjork, KM. 2008. The Economic Production Quantity Problem with a Finite Production Rate and Fuzzy Cycle Time. Proceedings of the 41st Hawaii International Conference on System Sciences. 2008. pp.1–9.
[2] DOI: 10.1016/S0165-0114(97)00350-3 · Zbl 0947.90003 · doi:10.1016/S0165-0114(97)00350-3
[3] DOI: 10.1016/j.mcm.2005.02.012 · Zbl 1170.90374 · doi:10.1016/j.mcm.2005.02.012
[4] DOI: 10.1016/S0020-0255(96)00085-0 · doi:10.1016/S0020-0255(96)00085-0
[5] DOI: 10.1016/S0360-8352(97)00191-5 · doi:10.1016/S0360-8352(97)00191-5
[6] Harris F, Operations and Cost, Factory Management Service (1915)
[7] DOI: 10.1016/j.amc.2005.10.001 · Zbl 1126.90429 · doi:10.1016/j.amc.2005.10.001
[8] DOI: 10.1016/S0377-2217(97)00173-2 · doi:10.1016/S0377-2217(97)00173-2
[9] Kaufmann A, Introduction of Fuzzy Arithmatic: Theory and Applications (1991)
[10] DOI: 10.1109/21.52552 · Zbl 0707.93037 · doi:10.1109/21.52552
[11] DOI: 10.1016/S0377-2217(97)00200-2 · Zbl 0951.90019 · doi:10.1016/S0377-2217(97)00200-2
[12] Lo, CY, Leu, JH and Hou, CI. 2007. A Study of the EPQ Model using Fuzzy AHP When Flaw of the Products Or Unreliable Machineries Exists. Proceedings of 2007 IEEE International Conference on Industrial Engineering and Engineering Management. 2007. pp.1163–1170.
[13] DOI: 10.1016/S0360-8352(02)00187-0 · doi:10.1016/S0360-8352(02)00187-0
[14] DOI: 10.1016/0925-5273(96)00014-X · doi:10.1016/0925-5273(96)00014-X
[15] DOI: 10.1080/07408178608975329 · doi:10.1080/07408178608975329
[16] DOI: 10.1016/S0925-5273(99)00044-4 · doi:10.1016/S0925-5273(99)00044-4
[17] DOI: 10.1016/0925-5273(95)00149-2 · doi:10.1016/0925-5273(95)00149-2
[18] Wolfram S, The Mathematica Book, 3. ed. (1996)
[19] DOI: 10.1016/S0019-9958(65)90241-X · Zbl 0139.24606 · doi:10.1016/S0019-9958(65)90241-X
[20] Zimmermann HJ, Fuzzy Set Theory and Its Application, 3. ed. (1996) · doi:10.1007/978-94-015-8702-0
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.