A Study of an EOQ Model Under Cloudy Fuzzy Demand Rate

Karmakar, Snigdha; De, Sujit Kumar; Goswami, Adrijit

doi:10.1007/978-981-13-0023-3_15

Cited by 4 publications

(5 citation statements)

References 29 publications

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“…For the classic EOQ model inventory management problem, Karmakar [46] introduced a new fuzzy number called the cloudy fuzzy and its new de-fuzzification method. By using the demand rate as a general fuzzy number as well as a cloudy fuzzy number, he first solved the crisp model.…”

Section: Basic Cloudy Fuzzy Eoq Model Without Backordermentioning

confidence: 99%

“…He then solved the general fuzzy and cloudy fuzzy problem using the standard Yager's index method and the extension of Yager's index method, respectively. Figure 3, obtained from the results of Karmakar's [46] study, shows that when the fuzzy and crisp solutions follow an exponential path, cloudy fuzzy follows a hyperbolic path. In the cloudy fuzzy case, inventory costs were very high, which began to decrease with cycle time, and the minimum value was obtained at 7 days cycle time only.…”

Section: Basic Cloudy Fuzzy Eoq Model Without Backordermentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Inventory Management: A Comprehensive Analysis of Models Integrating Diverse Fuzzy Demand Functions

Mittal,

Jain,

Pandey

et al. 2023

Mathematics

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This review study provides a comprehensive analysis of the classification of inventory models, with a focus on incorporating various fuzzy demand functions. The incorporation of fuzzy sets theory within inventory models is highlighted as a significant advancement in the field. The study emphasizes the importance of efficiently locating pertinent publications on this topic, rendering it a valuable resource for individuals interested in exploring inventory models that incorporate fuzzy demand functions. There was a need for a systematic and complete examination of recent breakthroughs in fuzzy inventory management. Our objective was to provide an illuminating overview of the significant developments in this field and offer insights into the probable future directions of research. Our evaluation of various model components has unveiled new and underexplored territories that may warrant further exploration. Perhaps it would be prudent to consider the possibility of establishing simpler models or incorporating qualitative methods into existing models and initiating a discourse on this topic.

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Section: Basic Cloudy Fuzzy Eoq Model Without Backordermentioning

confidence: 99%

Section: Basic Cloudy Fuzzy Eoq Model Without Backordermentioning

confidence: 99%

Optimizing Inventory Management: A Comprehensive Analysis of Models Integrating Diverse Fuzzy Demand Functions

Mittal,

Jain,

Pandey

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…In their model, they allowed shortages that are completely backlogged. Karmakar et al [13] discussed the cloudy fuzzy in the EOQ model for the uncertainty of demand. De and Mahata [6] developed the EOQ model in cloudy fuzzy to produce poor quality in which proportionate discount allowed.…”

Section: Inventory Model Based On Fuzzy Logicmentioning

confidence: 99%

“…They provided an example to satisfy the result in both environments. Dai et al [4] De and Mahata [6] Singh et al [37] Jaggi et al [11] Karmakar et al [13] Panda et al [20] Saha, S [26] Saranya and Varadarajan [27] Lu et al [16] This Paper…”

Section: Inventory Model Based On Fuzzy Logicmentioning

confidence: 99%

Joint replenishment strategy for deteriorating multi-item through multi-echelon supply chain model with imperfect production under imprecise and inflationary environment

Padiyar

vandana²,

Bhagat

et al. 2022

RAIRO-Oper. Res.

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As the industry environment becomes more competitive, the supply chain management for multi items has become an essential part of the industries. In this paper, a multi-echelon inventory model for deteriorating multi items with imperfect production has been developed under the environment of fuzzy and inflation. In this study, a single producer, multi-supplier, and multi-retailer are considered from the integrated point of view. Here, the producer only produces the retailer's need to have a tremendous advantage and minimum loss. It is observed that the inflation rate is almost uncertain for deteriorating goods in every supply chain. In this paper, the inflation rate is taken as a triangular fuzzy number, and the centroid method is used to defuzzify the profit function. The shortage is not allowed in any part, an imperfect production process is also considered, but it is not reworkable in this supply chain. Different inflation rates are considered for additional items because inflation has strained the most vulnerable consumers (the daily wage earners), who mainly demand goods in short and small quantities. This entire model is developed based on the retailer's demand and due to which the profit potential is maximized. The central premise of this study is to get maximum benefit by creating a production model for deterioration items. Finally, a numerical example and sensitivity analysis illustrate the present study. It is also observed that if the number of shipments taken from the supplier increases during the production period, the total profit increases in crisp and fuzzy. If a positive change occurs in the number of shipments received through the producer to the retailer, then the fuzzy model has positive, but a slight negative change occurs in the crisp model. This paper shows the effect of a joint replenishment policy for multi-item compared with the independent approaches.

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“…14 Since then, efforts have been made to apply this theory in many fields, and many articles have been published on its application, and improvement according to the characteristics of the field of research, and other influencing factors, which resulted from this study many branches of this theory, such as fuzzy lock set, dense fuzzy set, and cloudy fuzzy set. For example, on inventory modeling, there is many research works have developed various models using these branches of fuzzy logic theory, such as, De, 15,16 De and Mahata, 17,18 De and Sana, 19,20 Karmakar and De, 21,22 De and Beg, 23 Kumar and Goswami, 24 Karmakar et al, 21,22 and so on. In the road traffic, especially in the CF driving behavior, few of models have been developed based on the fuzzy logic theory, such as References [8,[25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

Driving control based on bilevel optimization and fuzzy logic

Bennajeh¹,

Saïd²

2021

Int J of Intelligent Sys

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Driving control in the car‐following (CF) driving behavior has two aspects. First, in what measure an approximation distance is taken as a safe distance guaranteeing the safety of the follower drivers. Second, how to control the follower's vehicle velocities based on the stimulus of the leading vehicle. In this context, to resolve the driving control problem in the CF driving behavior, a bilevel optimization is presented in this paper, based on the behaviors of the follower and leader drivers. Bearing in mind that mathematics has contributed to the imitation of human behaviors, they are now reaching a level of complexity requiring the entry on the scene of a new player, which is artificial intelligence. Thus, in this paper; we used the fuzzy logic theory for modeling a follower driver with a nonnormative behavior. To validate our model, we used a data set from the program of the US Federal Highway Administration. Therefore, according to the experimental results, there is homogeneity between the actual and the simulated travel trajectories in terms of deviation. Besides, the driver's behavior adopted (normative or nonnormative) is reflected in his reactions to the various components of the road.

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A Study of an EOQ Model Under Cloudy Fuzzy Demand Rate

Cited by 4 publications

References 29 publications

Optimizing Inventory Management: A Comprehensive Analysis of Models Integrating Diverse Fuzzy Demand Functions

Optimizing Inventory Management: A Comprehensive Analysis of Models Integrating Diverse Fuzzy Demand Functions

Joint replenishment strategy for deteriorating multi-item through multi-echelon supply chain model with imperfect production under imprecise and inflationary environment

Driving control based on bilevel optimization and fuzzy logic

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