Agent-based Modeling in Supply Chain Management using Improved C4-5

Balasubramaniam, Chara; Thigarasu, V.

doi:10.19026/rjaset.9.1382

Cited by 3 publications

(1 citation statement)

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“…Warkentin et al (2012) suggested opportunities to utilize knowledge discovery through data mining in Web-based electronic commerce. Balasubramaniam and Thigarasu (2015) used agents in generating decision tree analysis to avoid over-fitting when measuring supply chain efficiency with respect to both cost and customer satisfaction.…”

Section: Data Mining Methodologiesmentioning

confidence: 99%

A review of supply chain data mining publications

Olson

2020

Journal of Supply Chain Management Science

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The use of data mining in supply chains is growing, and covers almost all aspects of supply chain management. A framework of supply chain analytics is used to classify data mining publications reported in supply chain management academic literature. Scholarly articles were identified using SCOPUS and EBSCO Business search engines. Articles were classified by supply chain function. Additional papers reflecting technology, to include RFID use and text analysis were separately reviewed. The paper concludes with discussion of potential research issues and outlook for future development.

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Section: Data Mining Methodologiesmentioning

confidence: 99%

A review of supply chain data mining publications

Olson

2020

Journal of Supply Chain Management Science

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A survey on agent‐based modelling assisted by machine learning

et al. 2023

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Agent‐based models have diversified their applications across various domains due to the ease with which different phenomena can be represented and simulated. These models incorporate heterogeneous, autonomous agents, local interactions, bounded rationality, and often feature explicit spatial representations. However, certain challenges have been identified in their application, including the complexity of design, difficulty in calibrating parameters, and interpreting and analysing results. Therefore, incorporating machine learning (ML) tools in the various stages of the agent‐based modelling and simulation process presents a promising approach for current and future research. The main hypothesis of this study is that integrating ML techniques and tools into agent‐based modelling can help address challenges encountered during different stages of implementation, ultimately leading to more accurate and effective simulations. The methodology employed in this study involves a comprehensive search and analysis of relevant literature on the topic. This survey reviews significant developments in the integration of ML into the agent‐based modelling and simulation process in recent years. The results of this study summarize the fundamental concepts of ML and its applications in agent‐based modelling, and provide insights into the prospects and challenges for ML‐assisted agent‐based modelling in the near future.

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A Review of Supply Chain Data Mining Publications

Olson

2015

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Agent-based Modeling in Supply Chain Management using Improved C4-5

Cited by 3 publications

References 0 publications

A review of supply chain data mining publications

A review of supply chain data mining publications

A survey on agent‐based modelling assisted by machine learning

A Review of Supply Chain Data Mining Publications

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