Optimal production run length with imperfect production processes and backorder in fuzzy random environment

Hu, Jinsong; Zheng, Hui; Guo, Caiyun; Ji, Ya-Ping

doi:10.1016/j.cie.2010.01.012

Cited by 21 publications

(16 citation statements)

References 24 publications

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“…For fixed t(q i ), 1 and 2 , the variable x may belongs to any one of the interval: [0, Hu et al 2010). …”

Section: Determination Of D E[ñ]mentioning

confidence: 99%

“…Furthermore, in same article, another discussion is made by considering fraction of defective items as exponentially distributed random variable with fuzzy parameter. Hu, Zheng, Guo, and Ji (2010) assumed elapsed time until machine shifts from one state to another state as a fuzzy random variable and permissible shortages are fully backlogged. In above discussed fuzzy inventory models, there is no consideration of learning effect in unit production time.…”

Section: Introductionmentioning

confidence: 99%

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EPQ model with learning consideration, imperfect production and partial backlogging in fuzzy random environment

Kumar

Goswami

2013

International Journal of Systems Science

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The article scrutinises the learning effect of the unit production time on optimal lot size for the uncertain and imprecise imperfect production process, wherein shortages are permissible and partially backlogged. Contextually, we contemplate the fuzzy chance of production process shifting from an 'in-control' state to an 'out-of-control' state and re-work facility of imperfect quality of produced items. The elapsed time until the process shifts is considered as a fuzzy random variable, and consequently, fuzzy random total cost per unit time is derived. Fuzzy expectation and signed distance method are used to transform the fuzzy random cost function into an equivalent crisp function. The results are illustrated with the help of numerical example. Finally, sensitivity analysis of the optimal solution with respect to major parameters is carried out.

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“…For fixed t(q i ), 1 and 2 , the variable x may belongs to any one of the interval: [0, Hu et al 2010). …”

Section: Determination Of D E[ñ]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EPQ model with learning consideration, imperfect production and partial backlogging in fuzzy random environment

Kumar

Goswami

2013

International Journal of Systems Science

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“…In general, estimation of machine shifting time depends upon previous data records and experts' experiments, which may engender fuzziness as well as randomness in final estimation of shifting time. However, Hu et al [18] enhanced the Chung and Hou [2] model by considering elapsed time as a FRV. Wang and Tang [19] assumed it as a fuzzy variable, while Zhang et al [20] assumed it as a random fuzzy variable.…”

Section: Introductionmentioning

confidence: 98%

“…With the development of fuzzy set theory [8][9][10], fuzzy random variable (FRV) [11,12] and fuzzy mathematical programming [13][14][15][16][17], inventory modeling problem in fuzzy framework received attention of researchers. Hu et al [18] pointed out, the elapsed time until the production process shifts may be a FRV. In general, estimation of machine shifting time depends upon previous data records and experts' experiments, which may engender fuzziness as well as randomness in final estimation of shifting time.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy random EPQ model for imperfect quality items with possibility and necessity constraints

Kumar

Goswami

2015

Applied Soft Computing

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“…Zhang et al [22] assumed that the inventory set-up cost, holding cost and the other parameters are fuzzy variables. Hu et al [23] studied a model of fuzzy random variable where the elapsed time when the machine shifts from one state to another has been considered. Chakraborty et al [24] developed a deteriorating multi-item inventory model with price discount and variable demands via fuzzy logic under resource constraints.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive study of an economic order quantity model under fuzzy monsoon demand

Mahata

2019

Sādhanā

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This article deals with a classical economic order quantity (EOQ) model under monsoon type fuzzy demand rate. It is nothing but the generalization of cloudy fuzzy model. We split the EOQ model into three parts according to the real-time fuzzy components of the demand rate. To defuzzify the model we develop an algorithm and the solution is obtained with the help of a nonlinear optimization technique that requires maximum aspiration level of the fuzzy membership of the objective function. Moreover, for comparative study we take numerical results of the crisp, general fuzzy and cloudy fuzzy model also. By this study we have shown that the decision maker might have to choose the monsoon type fuzzy environment all the time to control the proposed inventory. Moreover, sensitivity analysis and graphical illustrations are made to justify the new fuzzy approach. adhana(0123456789().,-volV)FT3 ](0123456789().,-volV)

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Optimal production run length with imperfect production processes and backorder in fuzzy random environment

Cited by 21 publications

References 24 publications

EPQ model with learning consideration, imperfect production and partial backlogging in fuzzy random environment

EPQ model with learning consideration, imperfect production and partial backlogging in fuzzy random environment

A fuzzy random EPQ model for imperfect quality items with possibility and necessity constraints

A comprehensive study of an economic order quantity model under fuzzy monsoon demand

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