The generalized bike-sharing rebalancing problem (BRP) entails driving a fleet of capacitated vehicles to rebalance bicycles among bike-sharing system stations at a minimum cost. To solve this NP-hard problem, we present a highly effective memetic algorithm that combines (i) a randomized greedy construction method for initial solution generation, (ii) a route-copy-based crossover operator for solution recombination, and (iii) an effective evolutionary local search for solution improvement integrating an adaptive randomized mutation procedure. Computational experiments on real-world benchmark instances indicate a remarkable performance of the proposed approach with an improvement in the best-known results (new upper bounds) in more than 46% of the cases. In terms of the computational efficiency, the proposed algorithm shows to be nearly two to six times faster when compared to the existing state-of-the-art heuristics. In addition to the generalized BRP, the algorithm can be easily adapted to solve the one-commodity pickup-and-delivery vehicle routing problem with distance constraints, as well as the multi-commodity many-to-many vehicle routing problem with simultaneous pickup and delivery.

普遍的自行车共享再平衡问题（BRP）要求驾驶容量有限的车辆来以最小的成本在自行车共享系统站点之间重新平衡自行车。为解决此NP难题，我们提出了一种高效的模因算法，该算法结合了（i）用于初始解决方案生成的随机贪婪构建方法，（ii）用于解决方案重组的基于路由复制的交叉算子，以及（iii）有效的进化局部搜索，用于解决方案改进，集成了自适应随机突变过程。在现实世界中的基准实例上进行的计算实验表明，在超过46％的案例中，该方法的显着性能得到了改善，其中最著名的结果（新的上限）有所改善。在计算效率方面，与现有的最新启发式算法相比，提出的算法显示出快了将近两到六倍。除了通用的BRP，该算法还可以轻松地解决具有距离限制的单商品取送车辆路线问题，以及同时取货和取货的多商品多对多车辆路线问题。交货。