面向共享单车调配的带约束游牧算法研究

The bike-sharing allocation is an important way to optimize the urban traffic resources rebalancing, but the current optimal-route allocation method is sensitive to the bike system magnitude. Therefore, a time-based and inter-regional bike-sharing allocation method is researched, and the nomad algorithm with constraints (NCA) is proposed to obtain the optimal allocation solution. Firstly, with the bike flow as the constraints and the minimal operation loss as the target, the allocation problem is modeled as a multi-constrained objective optimization problem. Then, NCA is proposed to predict the optimal bike inventory in the stations and the transfer amount among the stations. Compared with the original nomadic algorithm without constraint thinking, NCA improves the local search strategies and the global optimization strategies, and optimizes the tribe generation methodology. Finally, based on the predicted inventory and transfer amount, the interregional allocation scheme in different time periods is obtained. The comparative experimental results on the relevant datasets in Shanghai and New York show that the running time is about 15% of other methods. The demand response rate is 0.15% higher than the branch-and-bound algorithm. The bike quantity and the operating losses are reduced by about 10% compared to the genetic algorithm. It can be seen that the proposed method has higher optimization efficiency and user demand response rate.