A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification

Hadi-Vencheh, A.; Mohamadghasemi, A.

doi:10.1016/j.eswa.2010.08.119

Cited by 116 publications

(64 citation statements)

References 19 publications

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“…AHP' nin amacı uzmanların bilgisini elde etmek olmasına karşın, geleneksel AHP karar vericinin belirsiz tercihlerini yansıtamamaktadır. Bu nedenle; BAHP, AHP metodunun belirsizliklerini gidererek hiyerarşik bulanık problemleri çözmek için geliştirilmiştir [12,18,19]. Çok kriterli ABC analizi ile ilgili yapılmış çalışmalar incelendiğinde, BAHP ve TOPSIS yöntemlerinin birlikte kullanımına rastlanmamıştır.…”

Section: Literatür Araştırmasıunclassified

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A Variant Perspective to Multi Criteria ABC Analysis Problem: Fuzzy Analytic Hierarchy Process - Technique for Order Preference by Similarity to Ideal Solution

Kiliç¹,

Aygün²,

Keskin³

et al. 2014

Pamukkale J Eng Sci

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Section: Literatür Araştırmasıunclassified

“…Şekil 1: Üçgensel bulanık sayı ̃ [18]. Bulanık bir sayı her zaman her bir üyelik derecesinin karşılık geldiği sağ ve sol gösterimlerle verilebilir.…”

Section: Bulanık Küme Ve Bulanık Sayılarunclassified

A Variant Perspective to Multi Criteria ABC Analysis Problem: Fuzzy Analytic Hierarchy Process - Technique for Order Preference by Similarity to Ideal Solution

Kiliç¹,

Aygün²,

Keskin³

et al. 2014

Pamukkale J Eng Sci

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“…The ABC method is most commonly used for inventory classification, 24,55,56 and it is often used with other methods, such as weighted linear optimization, fuzzy analytical hierarchy process (AHP)-data envelopment analysis (DEA), NN, the AHP, and the technique for order preference by similarity to ideal solution (TOPSIS) model. [57][58][59][60][61] The ABC method is formed on the basis of Pareto analysis and is easy to use and understand. The ABC method divides elements of classification into three groups A, B, and C, according to the criterion crisp values.…”

Section: Literature Reviewmentioning

confidence: 99%

Production process parameter optimization with a new model based on a genetic algorithm and ABC classification method

Erić

Stefanović

Đorđević

et al. 2016

Advances in Mechanical Engineering

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The difference between the production cost and selling price of the products may be viewed as a criterion that determines an organization's competitiveness and market success. In such circumstances, it is necessary to impact these criteria in order to maximize this difference. The selling products' price, in modern market conditions, is a category which may not be significantly affected. So organizations have one option, which is the production cost reduction. This is the motive for business organizations and the imperative of each organization. The key parameters that influence the costs of production and therefore influence the competitiveness of organizations are the parameters of production machines and processes used to create products. To define optimal parameter values for production machines and processes that will reduce production costs and increase competitiveness of production organizations, the authors have developed a new mathematical model. The model is based on application of the ABC classification method to classify production line processes based on their costs and an application of a genetic algorithm to find the optimal values of production machine parameters used in these processes. It has been applied in three different modern production line processes; the costs obtained by the model application have been compared with the real production costs.

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“…They claim that other factors, including such as lead time, criticality and commonality, should be incorporated in making classification decisions (see Refs. [1][2][3][4][5][6][7][8][9][10][11]. Therefore, multi-criteria should be used instead of single criterion to classify inventory.…”

Section: Introductionmentioning

confidence: 99%

Integrating Replenishment Policy with GSAA-FCM Based Multi-Criteria Inventory Classification

Zhang¹,

Zhao²,

Li³

2018

IJCIS

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It is neither practical nor economic to assign a specific inventory policy for each item if there are thousands of items in one firm. This paper seeks to solve the stock problem from an integrated perspective by taking into account of both classification of items and replenishment policies for each group. The items are first classified into different groups with respect to the similarity of predefined criteria. The fuzzy clustering-means algorithm (FCM) could help conduct the multi-criteria inventory classification, which considers annual dollar usage, lead time and criticality. Genetic algorithm and simulated annealing algorithm (GSAA) are introduced to eliminate the drawbacks (initial value sensitivity and local optimal convergence) of FCM. A modified FCM algorithm, the GSAA-FCM algorithm, is therefore proposed for classification. Based on the classification, each group is then assigned an appropriate replenishment policy through optimizing both joint replenishment period and the total costs. To demonstrate the usefulness and effectiveness of our method, an illustrative example is provided with a real dataset compared to other 9 methods in previous literature.

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A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification

Cited by 116 publications

References 19 publications

A Variant Perspective to Multi Criteria ABC Analysis Problem: Fuzzy Analytic Hierarchy Process - Technique for Order Preference by Similarity to Ideal Solution

A Variant Perspective to Multi Criteria ABC Analysis Problem: Fuzzy Analytic Hierarchy Process - Technique for Order Preference by Similarity to Ideal Solution

Production process parameter optimization with a new model based on a genetic algorithm and ABC classification method

Integrating Replenishment Policy with GSAA-FCM Based Multi-Criteria Inventory Classification

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