多调机环境下的树枝形铁路专用线作业车同步取送优化

围绕服务铁路枢纽地方货物流的小运转作业系统，研究一类多调机环境下的树枝形铁路专用线作业车同步取送优化问题。考虑取送顺序间隔、调机牵引能力等约束条件，以调机作业均衡为上层优化目标，以调机取送成本和货车停留成本最小化为下层优化目标建立双重目标规划模型。根据模型特点，提出融合综合关联度和异步启发式过程的两阶段融合求解方法。该方法首先基于聚类划分思想，引入综合关联度确定调机最佳数量，并对作业区进行划分，从而为调机指派作业范围。进而基于迭代寻优思路，设计异步循环启发式过程，该过程根据多调机取送车作业特点赋予循环体表述，设计循环体更新规则，引入遗传算法中的交叉与变异操作对循环体进行寻优，进而导入人工鱼群聚群行为实现循环体二次寻优，从而完成所有调机在各自作业区内取送顺序的逐步寻优过程。最后，设计实验场景对所提出的两阶段算法进行过程验证，并设计不同规模试验进行对比测试，结果表明了所提算法的有效性和较优性。

Optimization of Simultaneous Placing-in and Taking-out Wagons with Multiple Engines on Branch-shaped Siding

Aiming at the local freight trains transship system for the local freight flow in railway terminal, the simultaneous placing-in and taking-out wagons with multiple engines on branch-shaped sidings is presented. The problem is formulated as a bi-objective programming model. The first term in objective function focuses on balancing the workload of each engine. The second term in objective function aims at minimizing shunting cost which including the running cost of engines and idling cost of wagons. Some constraints indicating the time interval of placing-in and taking-out wagons and traction number of engine are considered into the model. And then a hybrid procedure combining comprehensive correlation indicator and asynchronous iteration heuristic for solving the model is given and is abbreviated as HP-CCI&AIH. HP-CCI&AIH includes two stages: clustering approach with comprehensive correlation indicator for shunting engine allocation, and asynchronous iteration procedure for placing-in and taking-out routing. In first stage, engine allocation strategy based on comprehensive correlation indicator is proposed. According to the idea of clustering division, the comprehensive correlation indicator abbreviated as CCI is proposed to determine the optimum number of engines, divide the work area and assign the tasks to each engine. In second stage, shunting strategy based on asynchronous iteration heuristic is provided. On the basis of the technique of finding the optimal solution using iterative procedure, an asynchronous iteration heuristic is provided and is abbreviated as AIH. Here the cycle body and updating procedure of solution are designed. Further the crossover and mutation procedure of genetic algorithm is introduced to optimize the cycle body. And then the artificial fish swarm behavior is also introduced to achieve the secondary optimization procedure. Finally, the experimental scenarios are designed to test the proposed algorithm, and the proposed algorithm is compared with some other algorithms by some different sized cases. The results show that the proposed algorithm is effective and superior.