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

Kumar, Ravi Shankar; Goswami, Adrijit

doi:10.1080/00207721.2013.823527

Cited by 14 publications

(5 citation statements)

References 26 publications

(36 reference statements)

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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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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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“…Numerous articles have been found in fuzzy system. Some notable recent works in fuzzy system are discussed as follows: Kumar and Goswami [34,35] proposed a fuzzy random EPQ model for imperfect quality items with possibility and necessity constraints. Mahata [36] investigated the learning effect of the unit production time on optimal lot size for the imperfect production process with partial backlogging of shortage quantity in fuzzy random environments.…”

Section: Introductionmentioning

confidence: 99%

An EPQ model for three-layer supply chain with partial backordering and disruption: Triangular dense fuzzy lock set approach

Mahata

2019

Sādhanā

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This article deals with three-echelon supply chain (SC) network involving flow of raw materials with imperfect quality, the manufacturer and multiple retailers under the effect of learning experiences in fuzzy decision-making process. Existing literature explores the SC model under full backordering and disruption. Thus, in this study we first develop a production-inventory control problem accompanied with partial backlogging and random disruptions. Any batch received from the supplier is inspected by the manufacturer and if any of them are found to be flawed then all the goods in the inspected batch are rejected. However, we present a case study for problem definition and to comprehend the model into practical applicability. To minimize the aggregate cost of the SC we have utilized the Triangular dense fuzzy lock set for controlling the cost vector of the proposed objective function of the model. Utilizing new defuzzification method and applying the proper keys, chosen by the decision maker, it is possible to minimize the average system cost exclusively. Finally, graphical illustrations and sensitivity analysis are made to justify the model.

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“…Huang and Wu [12] developed a continuous-time Markov decision process for analyzing the optimal control for a production-inventory system with a shortage cost function involving a nonzero fixed term. For further review of the EPQ model with imperfect quality and permissible shortages, the reader can refer to the works of Sarkar et al [38]; Kumar and Goswami [16]; Barron and Hermel [2]; and Taleizadeh et al [43].…”

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confidence: 99%

Effect of warranty and quantity discounts on a deteriorating production system with a Markovian production process and allowable shortages

Lin¹

2021

Journal of Industrial &Amp; Management Optimization

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This paper explores the retailer's optimal lot sizing and quantity backordering for a deteriorating production system with a two-state Markov production process in which quantity discounts are provided by the supplier. The products are sold with the policy of free reasonable repair warranty employing the fraction of nonconforming items in a lot size. Unlike the traditional economic production quantity (EPQ) model with warranty policy based on the elapsed time of the system in the control state follows an exponential distribution, this paper not only constructs an alternative mathematical model for EPQ model based on the fraction of nonconforming items in a lot size for an imperfect production system but also extends the topics of optimal quantity and shortage to a wider scope of academic research and further finds that some results are different from the traditional EPQ models. We seek to minimize the expected total relevant cost through optimal lot sizing and quantity backordering. We also demonstrate that the optimal lot size is bounded in a finite interval. An efficient algorithm is developed to determine the optimal solution. Moreover, a numerical example is given and sensitivity analysis is conducted to highlight management insights.

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

Cited by 14 publications

References 26 publications

Driving control based on bilevel optimization and fuzzy logic

Driving control based on bilevel optimization and fuzzy logic

An EPQ model for three-layer supply chain with partial backordering and disruption: Triangular dense fuzzy lock set approach

Effect of warranty and quantity discounts on a deteriorating production system with a Markovian production process and allowable shortages

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