# An Electric Fence-Based Intelligent Scheduling Method for Rebalancing Dockless Bike Sharing Systems
## 1 Introduction  
Existing for nearly 50 years, bike sharing systems (BSS) have sharply increased both in terms of their prevalence and popularity recently [1]. Similar to electric vehicles [2; 3], bikes are environmentally friendly, and they have been frequently cited as a method for solving the \"last mile\" problem [1; 2; 3; 4; 5]. Public bikes on the market are of two types: docked bikes and dockless bikes. The mainstream form of the Chinese BSS are dockless bikes, which allows users the freedom to pick up a bike and park it anywhere they want and, thus, is highly convenient and flexible for users. However, they generate new urban issues. The first problem involves inappropriate parking behaviors. A number of users park bikes at places that are unsuitable as parking spaces (e.g., on a pedestrian street, closely adjacent to a metro entrance), thereby resulting in negative impacts [6; 7; 8]. The second problem is the imbalance caused by the tidal phenomenon [9]. Affected by the lack of restraints on parking locations, bikes become spatially imbalanced over time. Moreover, the imbalance between users' concentrated travel demand and the distribution of bikes is further exaggerated because the number of bikes fluctuates dramatically during rush hours [10; 11; 12]. To solve the imbalance problem mentioned above, vehicle-based methods and user-based methods have been proposed. In vehicle-based methods, operators deploy trucks to rebalance the bike inventory. With regard to static rebalancing, [PERSON] et al. presented a relaxation of the original model, which was an MIP (mixed integer programming),to address the single-vehicle one-commodity capacitated pickup and delivery problem (SVOCPDP), in which a branch-and-cut algorithm was used [13]. Based on the multiple traveling salesman problem, three formulations were described by [PERSON] et al. [12]. The branch-and-cut algorithm was used to solve the problem by combining the three formulations and invoking separation procedures. [PERSON] et al. studied a dynamic BSS with multiple heterogeneous vehicles, depots, and visits. The conclusion was obtained that enhanced chemical reaction optimization (CRO) yielded better solutions than preliminary CRO [14]. [PERSON] et al. proposed the use of mixed-integer programming based on decomposing multivehicle rebalancing matters into single-vehicle problems [15]. They also provided a heuristic of \"cluster first, route second\" to mitigate its running time. Other static rebalancing studies can also be found in [16; 17; 18; 19; 20]. It is difficult to adjust a rebalancing plan in real time to cater to a fluctuating demand for static rebalancing purposes [20]. As a result, dynamic rebalancing solutions began to appear. For dynamic rebalancing, [PERSON] et al. carried out dynamic bike rebalancing with a constant gap time, aiming at making users more satisfied and a low rebalancing cost [21]. To decide the number of bikes to reposition and at which station to carry out the rebalancing process, [PERSON] et al. developed an implementable decision-support tool [22]. [PERSON] et al. proposed a novel mixed-integer programming formulation to solve the dynamic rebalancing problem and provided a linear programming model to capture the bike flows from all trips [23; 24]. A rebalancing framework for the dynamic bike sharing problem was presented in [25]. These methods are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach. However, they have limited predictive capabilities of critical stations caused by the absence of temporal sequences, especially in the case of long-term predictions. The vehicle-based approaches' rebalancing effects depend heavily on the accuracy of demand prediction [26]. Additionally, due to the maintenance and traveling costs of trucks, as well as labor costs, the truck-based approach can deplete a limited budget rapidly. User-based methods reposition bikes from the perspective of demand/inventory management [27].