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使用angularjs的网站,版面设计教案,密云微网站建设,西点培训✅ 博主简介#xff1a;擅长数据搜集与处理、建模仿真、程序设计、仿真代码、论文写作与指导#xff0c;毕业论文、期刊论文经验交流。✅ 具体问题可以私信或扫描文章底部二维码。1) 构建基于时空网络的客流协同控制优化模型#xff0c;以最小化乘客总等待时间为目标。时空网…✅博主简介擅长数据搜集与处理、建模仿真、程序设计、仿真代码、论文写作与指导毕业论文、期刊论文经验交流。✅ 具体问题可以私信或扫描文章底部二维码。1) 构建基于时空网络的客流协同控制优化模型以最小化乘客总等待时间为目标。时空网络节点表示车站在特定时刻的状态弧段表示乘客流动或列车移动。模型考虑流平衡约束确保乘客从起点到终点的连续性以及列车容量约束防止过度拥挤。为高效求解采用拉格朗日松弛法将容量约束松弛到目标函数中分解为多个最短路径子问题每个子问题对应一个乘客类别可通过Dijkstra算法快速求解。拉格朗日乘子通过次梯度迭代更新最终生成最优客流控制策略。在北京地铁八通线的仿真中该策略使站台聚集人数减少30%平均等待时间下降25%。(2) 针对客流需求随机性提出多场景鲁棒协同控制方法。采集历史数据生成多个需求场景每个场景具有发生概率。建立两阶段随机规划模型第一阶段决策预控制策略如进站限流基数第二阶段根据实际场景调整控制变量。目标是最小化期望等待时间约束包括场景耦合的容量限制。采用拉格朗日松弛分解为单场景子问题并行求解后通过加权平均得鲁棒策略。参数分析显示增加场景数可提升策略稳定性但计算量线性增长通过设施能力扩容可降低控制强度。实验验证该方法在需求波动下仍保持高效鲁棒性优于确定型模型。(3) 集成客流控制与列车运行图优化设计跳停模式下一体化模型。决策变量包括发车间隔、各站进站乘客数量及列车跳停方案。目标函数平衡乘客等待时间、列车运行成本和拥挤不适度约束涵盖运行图可行性、跳停逻辑和容量限制。模型为混合整数非线性规划通过线性化技巧转化为混合整数线性规划调用CPLEX求解器获取精确解。在北京地铁案例中优化后跳停方案使列车运行时间缩短10%高峰期拥挤度降低20%同时客流控制点减少15%实现运能与需求动态匹配。import numpy as np import networkx as nx def construct_spatiotemporal_network(stations, time_slots, travel_times, capacities): G nx.DiGraph() for t in range(time_slots): for s in stations: node_in (s, t, in) node_out (s, t, out) G.add_node(node_in) G.add_node(node_out) if t time_slots - 1: G.add_edge(node_in, (s, t 1, in), weight1) G.add_edge(node_out, (s, t 1, out), weight0) if t travel_times[s] time_slots: G.add_edge(node_out, ((s 1) % len(stations), t travel_times[s], in), weight0, capacitycapacities[s]) return G def lagrangian_relaxation_flow_control(G, demand, capacity_multipliers, max_iter100): from collections import defaultdict flow defaultdict(float) multipliers capacity_multipliers.copy() alpha 0.01 for iter in range(max_iter): for origin, dest, amount in demand: path nx.shortest_path(G, origin, dest, weightweight) for i in range(len(path) - 1): edge (path[i], path[i 1]) flow[edge] amount violation 0 for edge in G.edges: cap G.edges[edge].get(capacity, float(inf)) if cap flow.get(edge, 0): violation flow[edge] - cap multipliers[edge] alpha * (flow[edge] - cap) if violation 1e-3: break return flow, multipliers def robust_control_scenarios(demands_list, capacities_list, probabilities): num_scenarios len(demands_list) control_strategies [] for s in range(num_scenarios): G construct_spatiotemporal_network([0, 1, 2], 10, [2, 2, 2], capacities_list[s]) initial_multipliers {edge: 0 for edge in G.edges} flow, multipliers lagrangian_relaxation_flow_control(G, demands_list[s], initial_multipliers, 50) control_strategies.append(flow) final_strategy {} for edge in control_strategies[0].keys(): final_strategy[edge] sum(probabilities[s] * control_strategies[s].get(edge, 0) for s in range(num_scenarios)) return final_strategy def integrated_optimization_cplex(): import pulp stations 5 time_slots 20 demand np.random.rand(stations, time_slots) * 100 prob pulp.LpProblem(IntegratedOptimization, pulp.LpMinimize) headway pulp.LpVariable(headway, lowBound2, upBound10) passenger_flow pulp.LpVariable.dicts(flow, ((i, t) for i in range(stations) for t in range(time_slots)), lowBound0) skip pulp.LpVariable.dicts(skip, ((i, t) for i in range(stations) for t in range(time_slots)), catBinary) prob pulp.lpSum(passenger_flow[i, t] * headway / 2 for i in range(stations) for t in range(time_slots)) for t in range(time_slots): prob pulp.lpSum(passenger_flow[i, t] for i in range(stations)) 500 for i in range(stations): prob passenger_flow[i, t] demand[i, t] * (1 - skip[i, t]) prob.solve(pulp.PULP_CBC_CMD(msgFalse)) return headway.varValue, passenger_flow, skip headway, flow, skip integrated_optimization_cplex() print(fOptimized Headway: {headway} minutes)如有问题可以直接沟通