论文部分内容阅读
为了提高动态配流模型的通用性和稳定性,基于约束程序累积调度和字典序多目标优化,以作业之间实施逻辑和优先级关系、班计划和列车编组计划要求、资源容量限制等为约束,按照配流成功的出发列车优先级总和最大、车辆平均中停时最小和资源利用率最高3个目标的优先级,建立适应于不同解体方式的动态配流字典序多目标累积调度的3层模型.为提高算法效率,设计了约束传播和多点构建性搜索混合的带初始解迭代算法,每层先通过约束传播算法化简模型,再通过带约束传播的多点构建性搜索算法快速求解,以决策出优化的作业排程和配流方案.实验表明,模型扩展性更强、更稳定、更符合现场实际;算法效率高,能够满足现场对计划编制和调整的实施性需求.
In order to improve the generality and stability of the dynamic distribution model, constraint-based cumulative scheduling and dictionary-order multi-objective optimization are constrained by the implementation of logical and priority relations between jobs, the requirements of class planning and train grouping planning, resource capacity constraints, The three-layer model of multi-objective cumulative scheduling of dynamic allocation dictionary with different disassembly methods is established according to the priority of the priority of the train with the highest priority, the lowest average parking time and the highest resource utilization rate. In order to improve the efficiency of the algorithm, a hybrid iterative algorithm with constrained propagation and multi-point constructive search is designed. Each layer first simplifies the model through constrained propagation algorithm and then quickly solves the problem by multi-point constructive search algorithm with constrained propagation. The optimized job scheduling and the distribution scheme are shown.Experiments show that the model is more scalable and more stable, which is more in line with the actual situation in the field. The algorithm is efficient and can meet the on-site implementation requirements of planning and adjustment.