Vehicle Routing Problem for Fresh Products Distribution Considering Customer Satisfaction Through Adaptive Large Neighborhood Search

Under the background of the current COVID-19 epidemic, online purchase of daily necessities such as fresh products have gradually become an essential part of people's lives. To deal with the fresh products distribution problem in city logistics, this paper focuses on the customer psychological behavior considering both delivery time and the fresh products quality from the perspective of customer satisfaction. Thereby, we propose a mixed-integer nonlinear programming model, among which a time satisfaction function depending on customers’ time windows and a quality satisfaction function considering the freshness of the products are introduced as constraints. Then, an improved Adaptive Large Neighborhood Search algorithm is designed with a new strategy to jump out of the local optimal solutions and with new operators considering satisfaction functions. Numerical experiments on benchmark instances of standard Solomon instances demonstrate the effectiveness and the efficiency of the proposed method. The calculation results illustrate the improvements over existing solution method from the literature and provide managemental insights for enterprises on making wise investments while ensuring customer experience through optimizing distribution strategies.