A fuzzy random continuous review inventory system

Dey, Oshmita; Chakraborty, Debjani

doi:10.1016/j.ijpe.2011.03.015

Cited by 27 publications

(7 citation statements)

References 31 publications

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“…Fuzzy rate of deterioration was established in the works of De and Goswami (2001), De et al (2003), among others. Dutta et al (2005), Chang et al (2006), Dutta et al (2007, Dey and Chakraborty (2011), Dey and Chakraborty (2012), Kumar and Goswami (2015), Kumar et al (2016), Chakraborty and Bhuiya (2017) are some milestones in the literature addressing inventory models in fuzzy random environment. Among the most recent studies, Rout et al (2020b) demonstrated scenario-dependent demand pattern based on historical records which is achieved using Mamdani fuzzy inference scheme.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Demand is known to be one of the major parameters in inventory modelling which depends on various uncertain and unreliable activities of market as well as past records (Dey and Chakraborty 2011). Thus, there can arise situations where consideration of constant demand, time-varying demand or even fuzzy demand with crisp membership grade will not be suitable at all.…”

Section: Introductionmentioning

confidence: 99%

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Designing a single-vendor and multiple-buyers’ integrated production inventory model for interval type-2 fuzzy demand and fuzzy rule based deterioration

Rout

Kumar²,

Paul

et al. 2021

RAIRO-Oper. Res.

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In this paper, a single-vendor and multiple-buyers' integrated production inventory model is investigated where deterioration rate of the item is assumed to change in accordance with the weather conditions of a particular region. It relies upon the values of certain attributes that have a direct influence on the extent of deterioration. These parameter values are easily forecasted and thereby can be utilized to determine the item depletion rate, which is executed here using Mamdani fuzzy inference scheme. Besides, a nearest interval approximation formula for the defuzzification of interval type-2 fuzzy number (IT2FN) is developed. Its application in the proposed model is brought off by considering imprecise demand patterns at the buyers' locations which are in the form of IT2FNs. The model optimizes the total number of shipments to be made to the buyers within a complete cycle so as to minimize the overall integrated cost incurred. An optimization problem with interval objective function is formulated. A detailed illustration of the theoretical results is further demonstrated with the help of numerical example, followed by sensitivity analysis which provides insights into better decision making.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Designing a single-vendor and multiple-buyers’ integrated production inventory model for interval type-2 fuzzy demand and fuzzy rule based deterioration

Rout

Kumar²,

Paul

et al. 2021

RAIRO-Oper. Res.

Self Cite

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“…Some models incorporate fuzzy techniques to model the uncertainty of demand (Dey & Chakraborty, 2012;Dutta, Chakraborty & Roy, 2007;Garg, 2015;Sarkar & Mahapatra, 2015). Other models uses it to determine the costs of the inventory systems (Dey & Chakraborty, 2011;Katagiri & Ishii, 2002;Pai & Hsu, 2003;Taleizadeh, Niaki & Aryanezhad, 2010) or introduce fuzzy numbers to handle the uncertain information in the data (Garai & Garg, 2019). Thus, application of fuzzy techniques facilitates dealing with real practical situations in a more proper way.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy modeling approach to on-hand stock levels estimation in (R, S) inventory system with lost sales

Tarradellas

Babiloni

et al. 2020

JIEM

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Purpose: One challenge in inventory control models is to know the stock available at the beginning of the cycle to satisfy future demands, i.e. to know the on-hand stock levels at order delivery. For inventory managers, this knowledge is necessary to both determine service levels and establish the control parameters of the inventory policy. However, the calculation of on-hand stock levels when unfilled demand is lost is mathematically complex since on-hand stock cannot be negative by definition. The purpose of this paper is to propose a new approach to estimate on-hand stock levels when the inventory is periodically reviewed and unfilled demand is lost, through the use of fuzzy techniques.Design/methodology/approach: This paper applies fuzzy set techniques for the calculation of the on-hand stock levels at order delivery in the lost sales context, based on the uncertainty that real demand introduces. To this end, we propose a new approach based on modeling the on-hand stock as an imprecise Markov chain using possibility functions, which reduces significantly the computational effort required to obtain the on-hand stock levels.Findings: To illustrate the performance of the proposed method, two experiments are carried out. The first experiment shows that the proposed fuzzy method correctly calculates on-hand stock levels with insignificant deviation with respect the exact vector. Additionally, the results illustrate that the fuzzy method simplifies the calculation and highly reduces the computational efforts. The second experiment shows the performance of the fuzzy method when it is used to estimate service levels by means of the fill rate. The results show that the proposed method accurately estimates the fill rate with average deviations lower than 0.00015.Practical implications: Knowing the on-hand stock vector is important for inventory managers to establish the control parameters of the system, i.e. to determine the minimum base stock level, S, that guarantees the achievement of a target service level. The difficulty of this estimation is that to obtain the on-hand stock vector in a lost sales context requires a huge computational effort and it is difficult to implement in companies’ information systems. However, the proposed fuzzy method leads to a very accurate calculation of the on-hand stock vector significantly reducing the computational costs, which makes this method easily implementable in practical environments.Originality/value: Fuzzy set techniques have been widely used in inventory models to introduce the uncertainty of demand, costs or shortage. However, to the best of our knowledge, this is the first paper which deals directly with fuzzy estimation of on-hand levels.

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“…Kumar et al [21] developed an integrated production-inventory for vendor-buyer integrated supply chain by considering fuzzy random demand rate. The continuous review and periodic review inventory systems and their variants were extended in fuzzy environment by considering fuzzy/fuzzy random demand by [22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Modelling a type-2 fuzzy inventory system considering items with imperfect quality and shortage backlogging

Kumar

2018

Sādhanā

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In this paper, an inventory model considering items with imperfect quality is developed in fuzzy environment, wherein shortages are allowed and are backlogged. In order to detect the items with imperfect quality, all items are screened before they are sent for consumption, and all imperfect quality items are sold at discounted price, called salvage value. In view of the fact that demand may not be predicted precisely, because it depends upon many uncertain and perturbing market activities, it is assumed to be a type-2 fuzzy variable. Quantity of imperfect quality items in the received lot may not be predicted precisely; hence percentage of imperfect quality items is also considered as a type-2 fuzzy variable. In this regard, this study developed a defuzzification method of type-2 fuzzy variable pertaining to interval approximation. The mathematical model is analysed to find closed form formulae of order quantity and backlogging quantity. Finally, the proposed methodology and model are testified on numerical examples, and sensitivity of decision variables is examined and discussed to underline the managerial insights as well as to establish the robustness of the mathematical model.

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A fuzzy random continuous review inventory system

Cited by 27 publications

References 31 publications

Designing a single-vendor and multiple-buyers’ integrated production inventory model for interval type-2 fuzzy demand and fuzzy rule based deterioration

Designing a single-vendor and multiple-buyers’ integrated production inventory model for interval type-2 fuzzy demand and fuzzy rule based deterioration

Fuzzy modeling approach to on-hand stock levels estimation in (R, S) inventory system with lost sales

Modelling a type-2 fuzzy inventory system considering items with imperfect quality and shortage backlogging

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