Abstract:

The dock-less feature brings a new and challenging problem to reallocate the dock-less sharing bikes. Decision-makers need to consider how to reallocate these bikes， which are randomly parked over the entire area. This is significantly different from the traditional public bikes reallocating problem. Taking a brand bicycle as the research background， the active point （delivery point） and the inactive point （pick-up point） are defined by analyzing mass trajectory data of dock-less sharing bikes in this paper. Due to the status of pick-up points will be constantly changed， three scenarios are proposed to depict it， which include changing the number of bikes， disappearing the pick-up points， and pop-up new pick-up points. Based on the theory of disruption management， a multi-objective optimization model is proposed for reallocating the dock-less sharing bikes and a non-dominated sorting genetic algorithm II is present to solve it. The test result demonstrates that the proposed algorithm can quickly converge to the Pareto solution set of the problem， and thus it can provide effective suggestions and decision support for reallocating the dock-less sharing bikes.

Key words: dock-less sharing bike, data-driven, disruption management, multi-objective optimization, NSGA-II.