Distance-based consensus method for ABC analysis

Bhattacharya, Arijit; Sarkar, Bijan; Mukherjee, Sanat Kumar

doi:10.1080/00207540600847145

Cited by 97 publications

(41 citation statements)

References 11 publications

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“…Ramanathan (2006), çok kriterli envanter sınıflandırma problemi için veri zarflama analizine benzeyen, basit ağırlıklı bir doğrusal optimizasyon modeli önermiştir [14]. Zhou [16]. Chu vd.…”

Section: Literatür Araştırmasıunclassified

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

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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“…Lolli et al [22] established a multi-criteria classification model called AHP-K-Veto, based on the AHP method and the K-means algorithm. Bhattacharya et al [3] developed a model which combines Topsis (Technique for Order Preferences by Similarity to the Ideal Solution) with AHP method in order to generate a ranking items and then an ABC classification. Chen et al [6] proposed an alternative approach to MCIC problem by using Topsis and two virtual items.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-criteria Inventory Classification Approach: Artificial Bee Colony Algorithm with VIKOR Method

Cherif

Ladhari

2016

Communications in Computer and Information Science

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Abstract. ABC analysis is a well-established categorization technique based on the Pareto Principle which dispatches all the items into three predefined and ordered classes A, B and C, in order to derive the maximum benefit for the company. In this paper, we present a new approach for the ABC Multi-Criteria Inventory Classification problem based on the Artificial Bee Colony (ABC) algorithm with the Multi-Criteria Decision Making method namely VIKOR. The ABC algorithm tries to learn and optimize the criteria weights, which are then used as an input parameters by the method VIKOR. The MCDM method generates a ranking items and therefore an ABC classification. Each established classification is evaluated by an estimation function, which also represents the objective function. The results of our proposed approach were obtained from a widely used data set in the literature, and outperforms the existing classification models from the literature, by obtaining better inventory cost.

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Distance-based consensus method for ABC analysis

Cited by 97 publications

References 11 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

A Novel Multi-criteria Inventory Classification Approach: Artificial Bee Colony Algorithm with VIKOR Method

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