一种有效的基于模因置换的训练调度进化算法
An efficient memetic, permutation-based evolutionary algorithm for real-world train timetabling
摘要：
训练编排是一个困难的和非常严格约束的组合问题，涉及训练时刻表的建设。我们专注于一个小扰动后的时间表的局部重建的特定问题，寻求最小化的总累积延迟通过适应时间出发和到达每个训练和资源分配（轨道，路由节点等）。我们描述了一种基于置换的进化算法，它依赖于一个半贪婪的启发式逐步重建的时间表插入列车一个接一个的排列。该算法可以与CNOG商业MIP编程工具CPLEX以粗粒度的方式进行混合：进化部分用于快速获得良好但次优的解，并且使用CPLEX对该中间解进行细化。实验结果是在一个大型的现实世界的情况下，涉及超过100万个变量和200万个约束。结果是令人惊讶的好的进化算法，单独或混合，产生优秀的解决方案比单独CPLEX快得多。
英文摘要：
Train timetabling is a difficult and very tightly constrained combinatorial problem that deals with the construction of train schedules. We focus on the particular problem of local reconstruction of the schedule following a small perturbation, seeking minimisation of the total accumulated delay by adapting times of departure and arrival for each train and allocation of resources (tracks, routing nodes, etc.). We describe a permutation-based evolutionary algorithm that relies on a semi-greedy heuristic to gradually reconstruct the schedule by inserting trains one after the other following the permutation. This algorithm can be hybridised with ILOG commercial MIP programming tool CPLEX in a coarse-grained manner: the evolutionary part is used to quickly obtain a good but suboptimal solution and this intermediate solution is refined using CPLEX. Experimental results are presented on a large real-world case involving more than one million variables and 2 million constraints. Results are surprisingly good as the evolutionary algorithm, alone or hybridised, produces excellent solutions much faster than CPLEX alone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
