GA-MPG:用于生成简易移动计划的高效遗传算法

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-08-27 DOI:10.1007/s12652-024-04846-3
Rohan S. Shukla, Ekta A. Ghuse, Tausif Diwan, Jitendra V. Tembhurne, Parul Sahare
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引用次数: 0

摘要

在竞争激烈的电信行业,通信服务提供商的成功取决于其是否有能力根据客户的不同需求提供有吸引力的移动计划。这不仅能增加公司利润,还能提高每用户平均收入(ARPU)、客户生命周期价值等指标,并减少客户流失。如何在这些目标之间取得平衡是一项艰巨的任务。为了应对这一挑战,我们提出了一种名为遗传算法移动计划生成(GA-MPG)的新方法。所提出的方法因其确定性方法而与众不同,它同样注重最大限度地减少客户流失。具体做法是为他们提供最合适的计划,而不会让他们为自己会使用的功能支付额外费用。利用 GA-MPG 生成高效的移动计划是通过 AdaBoost 分类器和模糊模型的结合来实现的。AdaBoost 可用于生成可行的移动计划,并预测各种计划中的最佳解决方案。此外,模糊模型根据客户的典型服务使用情况推荐个性化计划。这也使公司利润最大化,与各种电信公司采用的侧重于两个问题之一的现有战略形成鲜明对比。所提出的 GA-MPG 算法在一个包含约 7000 名客户的著名美国电信数据集上取得了可喜的成果,客户流失率大幅降低了 44%。这些结果是基于模拟结果得出的。该算法还显示,在规定时间内,ARPU 和公司利润分别提高了 13% 和 18%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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GA-MPG: efficient genetic algorithm for improvised mobile plan generation

In the competitive landscape of the telecom sector, a Communication Service Provider's success hinges on its ability to offer compelling mobile plans tailored to diverse customer needs. This not only boosts company profits but also enhances metrics like average revenue per user (ARPU), customer lifecycle value, and reduces customer churn. Striking a balance between these objectives presents a formidable task. To address this challenge, we propose a novel approach called Genetic Algorithm Mobile Plan Generation (GA-MPG). The proposed method stands out for its deterministic approach that equally focuses on minimizing customer churn. This is done by providing them with the best-suited plans without making them pay extra for features they would use. The efficient mobile plan generation using GA-MPG is accomplished by the combination of the AdaBoost classifier and the Fuzzy model. The AdaBoost is utilized for feasible mobile plan generation and predicting the optimal solution amongst the various plans. Additionally, a fuzzy model recommends personalized plans based on customers' typical service usage. This also maximizes company profits, contrasting with existing strategies employed by various telecom companies which focus on one of the two problems. The proposed GA-MPG algorithm demonstrated promising results on a prominent US-based telecom dataset encompassing around 7000 customers, with a substantial 44% reduction in customer churn. These findings are based on the simulation results. The algorithm also shows improvements of 13% and 18% in ARPU and company profit, respectively, over a defined period.

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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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