Multi-criteria inventory classification with reference items

Soylu, Banu; Akyol, Bahar

doi:10.1016/j.cie.2013.12.011

Cited by 49 publications

(26 citation statements)

References 21 publications

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“…Th ough the model is simple enough to run without advanced software, it has the flexibility to add and change constraints and factors as needed. Similar attempts to multi-criteria inventory classification (MCIC) include (Bacchetti et al 2013, Hatefi et al 2014, Lolli et al 2014, Millstein et al 2014, Park et al 2014, Roda et al 2014, Soylu and Akyol 2014, Babai et al 2015.…”

Section: Literature Reviewmentioning

confidence: 99%

Applying inventory classification to a large inventory management system

May

Atkinson

Ferrer

2017

JOSCM

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Inventory classification aims to ensure that business-driving inventory items are efficiently managed in spite of constrained resources. There are numerous single-and multiple-criteria approaches to it. Our objective is to improve resource allocation to focus on items that can lead to high equipment availability. This concern is typical of many service industries such as military logistics, airlines, amusement parks and public works. Our study tests several inventory prioritization techniques and finds that a modified multi-criterion weighted non-linear optimization (WNO) technique is a powerful approach for classifying inventory, outperforming traditional techniques of inventory prioritization such as ABC analysis in a variety of performance objectives.

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Section: Literature Reviewmentioning

confidence: 99%

Applying inventory classification to a large inventory management system

May

Atkinson

Ferrer

2017

JOSCM

View full text Add to dashboard Cite

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“…Chen (2011) proposed an improved approach for the MCABC problem in which all items were peer estimated. In addition, Chen (2012) proposed an alternative approach for the MCABC problem by using In addition to these weighted Euclidean distance-based models, Soylu and Akyol (2014) applied the UTADIS method to the MCABC problem in which the DM specifies exemplary assignment of a number of reference alternatives. Each inventory item is assigned to a suitable class by comparing its global utility with the threshold of each class.…”

Section: Literature Reviewmentioning

confidence: 99%

A classification approach based on the outranking model for multiple criteria ABC analysis

Liu

Liao

Zhao

et al. 2016

Omega

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“…Many other classification algorithms based on mathematical programming optimisation techniques exist in literature but cannot be described here due to limited space (Armutlu, Ozdemir, Uney-Yuksektepe, Kavakli, & Turkay, 2008;Bal, orkcu, & Celebioglu, 2006;Bertsimas & Shioda, 2007;Glen, 2003;Kone & Karwan, 2011;Ma, 2012;Saigo, Nowozin, Kadowaki, Kudo, & Tsuda, 2009;Soylu & Akyol, 2014;Sun & Xiong, 2003;Turkay, Uney, & Yilmaz, 2005;Uney & Turkay, 2006;Xanthopoulos & Razzaghi, 2014;Zhang, Shi, & Zhang, 2009).…”

Section: Mathematical Programmingmentioning

confidence: 99%

Sample re-weighting hyper box classifier for multi-class data classification

Yang

Liu

Tsoka

et al. 2015

Computers & Industrial Engineering

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a b s t r a c tIn this work, we propose two novel classifiers for multi-class classification problems using mathematical programming optimisation techniques. A hyper box-based classifier (Xu & Papageorgiou, 2009) that iteratively constructs hyper boxes to enclose samples of different classes has been adopted. We firstly propose a new solution procedure that updates the sample weights during each iteration, which tweaks the model to favour those difficult samples in the next iteration and therefore achieves a better final solution. Through a number of real world data classification problems, we demonstrate that the proposed refined classifier results in consistently good classification performance, outperforming the original hyper box classifier and a number of other state-of-the-art classifiers.Furthermore, we introduce a simple data space partition method to reduce the computational cost of the proposed sample re-weighting hyper box classifier. The partition method partitions the original dataset into two disjoint regions, followed by training sample re-weighting hyper box classifier for each region respectively. Through some real world datasets, we demonstrate the data space partition method considerably reduces the computational cost while maintaining the level of prediction accuracies.

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Multi-criteria inventory classification with reference items

Cited by 49 publications

References 21 publications

Applying inventory classification to a large inventory management system

Applying inventory classification to a large inventory management system

A classification approach based on the outranking model for multiple criteria ABC analysis

Sample re-weighting hyper box classifier for multi-class data classification

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