Inventory control for a MARKOVIAN remanufacturing system with stochastic decomposition process

Takahashi, Katsuhiko; Morikawa, Katsumi; Myreshka,; Takeda, Daisuke; Mizuno, A.

doi:10.1016/j.ijpe.2006.12.023

Cited by 31 publications

(8 citation statements)

References 6 publications

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“…Nakashima et al (2002Nakashima et al ( , 2004 develop Markov chain models to analyze the behavior of stochastic remanufacturing systems. Takahashi et al (2007) consider a decomposition process in which recovered products are decomposed into parts, materials and waste. The performance of the proposed policies is evaluated by using a Markov chain model of the system.…”

Section: Inventory Modelsmentioning

confidence: 99%

Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art

Ilgın

Gupta

2010

Journal of Environmental Management

807

377

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Section: Inventory Modelsmentioning

confidence: 99%

Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art

Ilgın

Gupta

2010

Journal of Environmental Management

807

377

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“…In order to analyze remanufacturing and disposal decision, Aras et al [4] emphasized on quality levels of returned product and constructed a continuous time Markov chain model and investigated quality based remanufacturing lead times and disposal cost. Takahashi et al [53] used Markov analysis to study a remanufacturing system where recovered products are decomposed and classified into wasted to be disposed and materials and parts to be used in the processes for producing parts and products. Recently, Jin et al [38] investigated the assembly strategies for product remanufacturing with variation in the quality level of returns.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the merging capacity for two streams of product returns in remanufacturing systems

Fathi

Zandi

Jouini

2015

Journal of Manufacturing Systems

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International audienceWe consider a remanufacturing system with two streams of returned products and different variability levels (high and low). The arrival of returns with high variability is modeled with a hyperexponential renewal process and that of returns with low variability is modeled with a Poisson process. The reman-ufacturing facility can process the returned products in two ways. For the first way, each type of returns is remanufactured by a dedicated capacity. For the second way, returns from two different markets are remanufactured by a merged capacity. Analytical queueing models with the time value of money consideration are proposed for the admission decision, which decides on the acceptance or not of returned products based on quality and processing time. The proposed modeling determines the admission decision threshold value in order to maximize the total expected profit of the remanufacturing system. Our analysis also allows to study the interaction between the overall utilization and the arrival process variability. The results show the impact of the model parameters on the admission decision value and the total expected discounted profit. Also, the total expected discounted profit under the separated and merged capacities are compared

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“…A discrete Markov chain is a random process that describes a sequence of events from a set of finite possible states, whereas current event depends only on the preceding event. It has been commonly used to model uncertain events in various fields such as, engineering [28], economics [29], and physics [30]. In recent decades, the use of Markov chain is common in many applications to capture the behavior of drought classification states using multiscalar drought indices [31,32].…”

Section: Drought Classes As a Markov Chainmentioning

confidence: 99%

A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes

Ali

Hussain

Faisal

et al. 2018

Advances in Meteorology

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Drought is a complex stochastic natural hazard caused by prolonged shortage of rainfall. Several environmental factors are involved in determining drought classes at the specific monitoring station. erefore, efficient sequence processing techniques are required to explore and predict the periodic information about the various episodes of drought classes. In this study, we proposed a new weighting scheme to predict the probability of various drought classes under Weighted Markov Chain (WMC) model. We provide a standardized scheme of weights for ordinal sequences of drought classifications by normalizing squared weighted Cohen Kappa. Illustrations of the proposed scheme are given by including temporal ordinal data on drought classes determined by the standardized precipitation temperature index (SPTI). Experimental results show that the proposed weighting scheme for WMC model is sufficiently flexible to address actual changes in drought classifications by restructuring the transient behavior of a Markov chain. In summary, this paper proposes a new weighting scheme to improve the accuracy of the WMC, specifically in the field of hydrology.

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Inventory control for a MARKOVIAN remanufacturing system with stochastic decomposition process

Cited by 31 publications

References 6 publications

Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art

Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art

Modeling the merging capacity for two streams of product returns in remanufacturing systems

A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes

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