A Multi-agent Approach for the Newsvendor Problem with Word-of-Mouth Marketing Strategies

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• 关键词：Newsvendor problem ; Word of mouth marketing ; Multi ; agent modeling ; SIR model
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9772
• 期：1
• 页码：782-792
• 全文大小：498 KB
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• 作者单位：Feng Li (15)
  Ning Lin (15)
  
  15. School of Business Administration, South China University of Technology, Guangzhou, China
  
• 丛书名：Intelligent Computing Theories and Application
• ISBN：978-3-319-42294-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9772

文摘

Word-of-mouth (WOM) marketing is increasingly playing an important role in consumers’ purchase decision with the development of mobile Internet and various social media APP. We are particularly interested in such a problem as how to make decisions under effects of WOM campaigns? To answer this question, we develop a multi-agent model that emulates WOM or viral marketing process as spread of disease among people. Assume that each “infected” individual will purchase one unit of product. Then, the total “infected” people form the demand of the product, as an input of newsvendor problem. Besides finding the optimal order quantity of newsvendor problem, we also identify the most influential source node for kick off of the WOM marketing. The simulation results reveal that social network and WOM have a great influence on demand and profit of the firm. Even the source node has significant effect on output of WOM marketing. According to our simulation, the closeness centrality in social network analysis is the best measure to recognize the most influential source node, comparing to degree centrality, or betweenness centrality, etc. Finally, parameter analysis infers that profit of the firm will increase with higher the spreading probability or/and lower the resistant probability.