Optimization of Big Data Parallel Scheduling Based on Dynamic Clustering Scheduling Algorithm

Liu, Fang; Yanxiang, He; He, Jianxing; Gao, Xing; Huang, Feihu

doi:10.1007/s11265-022-01765-4

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…(Bellini et al, 2022) proposed a recommendation system based on a multi-clustering approach of items and users in fashion retail, where their proposed solution relies on mining techniques. (Liu et al, 2022) uses dynamic clustering scheduling for optimisation of big data parallel scheduling tasks. Their approach can also be adapted to item scheduling for inventory management where multiple criteria are used.…”

Section: Literature Reviewmentioning

confidence: 99%

A Methodology for Clustering Items with Seasonal and Non-seasonal Demand Patterns for Inventory Management

Kandemir¹

2022

Journal of Advanced Research in Natural and Applied Sciences

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Item clustering has become one of the most important topics in terms of effective inventory management in supply chains. Classification of items in terms of their features, sales or consumption volume and variation is a prerequisite to determine differentiated inventory policies as well as parameters, most common of which is service levels. Volume classification is easily obtained by well-known Pareto approach while coefficient of variance is usually used for variation dimension. Hence, it is not always applicable to classify items under different product families with different demand patterns in terms of variation. In this paper, we propose two algorithms, one based on statistical analysis and the other an unsupervised machine learning algorithm using K-means clustering, both of which differentiate seasonal and non-seasonal products where an item’s variation is evaluated with respect to seasonality of the product group it belongs to.

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Section: Literature Reviewmentioning

confidence: 99%