Two decision makers’ single decision over a back order EOQ model with dense fuzzy demand rate

Maity, Suman; De, Sujit Kumar; Pal, Madhumangal

doi:10.18686/fm.v3i1.1061

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…Karmakar et al [21,22] considered an EPQ model and used the dense fuzzy lock set concept to reduce pollution by reusing waste items of the sponge iron industry. Maity et al [23] developed an EOQ model with dense fuzzy demand rate where two decision makers make a single decision to optimize the model. Maity et al [24] studied an EOQ model under daytime uncertain demand rate.…”

Section: Literature Review On Inventory Modelsmentioning

confidence: 99%

A Study of an EOQ Model of Growing Items with Parabolic Dense Fuzzy Lock Demand Rate

Maity

Pal

et al. 2021

ASI

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In this article, the parabolic dense fuzzy set is defined, and its basic arithmetic operations are studied with graphical illustration. The lock set concept is incorporated in a parabolic dense fuzzy set. Then, it is applied to the problems of fishery culture via the modeling of an economic order quantity model. Here, the fingerlings are fed to reach the ideal size to fulfill the customer’s demand. The growth rate of the fingerlings is assumed as a linear function. After the sales of all fish, the pond is cleaned properly for a new cycle. Here, the model is solved in a crisp sense first. Then, we fuzzify the model considering the demand rate as a parabolic dense lock fuzzy number and obtain the result in a fuzzy environment. The main aim of our study was to find the quantity of the ordering items such that the total inventory cost gets a minimum value. Lastly, sensitivity analysis and graphical illustrations were added for better justification of our model.

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Section: Literature Review On Inventory Modelsmentioning

confidence: 99%

A Study of an EOQ Model of Growing Items with Parabolic Dense Fuzzy Lock Demand Rate

Maity

Pal

et al. 2021

ASI

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“…In learning theory for decision making, some interesting works are dense fuzzy set [31], dense fuzzy Neutrosophic set [32], Lock fuzzy set [33], Moonsoon fuzzy set [34] etc. The applications of the experience-based learning approaches in the study of lot-sizing problem were addressed by Maity et al [35][36][37], Karmakar et al [38,39] in the light of the theory of dense and lock fuzzy number. Rahaman et al [40] contributed a study to find out the joint impact of memory and experience-based learning on the decision of optimization for an EOQ model.…”

Section: 2inventory Model Under Fuzzy Uncertaintymentioning

confidence: 99%

Solution of an Economic Production Quantity model using the generalized Hukuhara derivative approach

Rahaman

Maity²,

et al. 2021

Scientia Iranica

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In this study, an economic production quantity (EPQ) model with deterioration is developed where the production rate is stock dependent and the demand rate is unit selling price and stock dependent. The low unit selling price and more stocks correspond high demandbut more stock corresponds to slow production because of the avoidance of unnecessary stocks. First of all, we develop the production model by solving some ordinary differential equations having deterministic profit function under some specific assumptions. Later, we develop the fuzzymodel by solving the fuzzy differential equations using Generalized Hukuhara derivative. In fact, the differential equation of the model has been split into two parts namely gH(L-R) and gH(R-L) on the basis of left(L) and right(R)𝛼 − cuts of fuzzy numbers for which the problem itself is transformed into multi-objective EPQ problem. A new formula of aggregation of several objective values obtained at different aspiration levelshas been discussed to defuzzify the fuzzy multi-objective problems.We solve the crisp and fuzzy models using LINGO software. Numerical and graphical illustrations confirm thatthe model under Generalized Hukuhara derivative of (R-L) type contributes more profit which is one of the basic novelties of the proposed approach.

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“…De and Mahata [35] extended the dense fuzzy set to cloudy fuzzy set and gave a new formula for defuzzification. Several attempts on the application of dense fuzzy rule were studied by De [36], Karmakar et al [37,38] and Maity et al [39] extensively.…”

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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Two decision makers’ single decision over a back order EOQ model with dense fuzzy demand rate

Cited by 10 publications

References 9 publications

A Study of an EOQ Model of Growing Items with Parabolic Dense Fuzzy Lock Demand Rate

A Study of an EOQ Model of Growing Items with Parabolic Dense Fuzzy Lock Demand Rate

Solution of an Economic Production Quantity model using the generalized Hukuhara derivative approach

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

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