Modeling of individual customer delivery satisfaction: an AutoML and multi-agent system approach

Abstract

PurposeThe purpose of this paper is to propose an automated machine learning (AutoML) and multi-agent system approach to improve overall product delivery satisfaction under limited resources.Design/methodology/approachAn AutoML method is purposed to model delivery satisfaction of individual customer, and a heuristic method and multi-agent system are proposed to improve overall satisfaction under limited processing capability. A series of simulation experiments have been conducted to illustrate the effectiveness of the proposed methodology.FindingsThe simulated results show that the proposed method can effectively improve overall delivery satisfaction, especially when the demand of customer orders is highly fluctuating and when the customer satisfaction models are highly diversified.Practical implicationsThe proposed framework provides a more dynamic and continuously improving way to model delivery satisfaction of individual customer, thereby supports companies to provide personalized services and develop scalable and flexible business at a lower cost, and ultimately improves the overall quality, efficiency and effectiveness of delivery services.Originality/valueThe proposed methodology utilizes AutoML and multi-agent system to model individual customer delivery satisfaction and improve the overall satisfaction. It can cooperate with the existing delivery resource planning methods to further improve customer delivery satisfaction. The authors propose an AutoML approach to model individual customer delivery satisfaction, which enables continuous update and improvements. The authors propose multi-agent system and a heuristic method to improve overall delivery satisfaction. The numerical results show that the proposed method can improve overall delivery satisfaction with limited processing capability.