基于动态罚函数的铁路车流分配与径路优化模型python源码

# -- coding: gbk -- import mip from mip import Model, BINARY, xsum, minimize # from enum import Enum import networkx as nx from matplotlib import pyplot as plt # 路网 class Network: def __init__(self): self.NodeList = [] self.EdgeList = [] self.ODList = [] self.PrevEdge = {} # 节点入弧 self.NextEdge = {} # 节点出弧 def add_node(self, node): self.NodeList.append(node) self.PrevEdge[node] = [] self.NextEdge[node] = [] # 通过车站名称获取区间 def get_edge(self, sta1, sta2): for sec in self.EdgeList: if sec.start_station == sta1 and sec.end_station == sta2: return sec # 增加区间 def add_edge(self, s_node, e_node, distance): if s_node not in self.NodeList: self.add_node(s_node) if e_node not in self.NodeList: self.add_node(e_node) for edge in self.EdgeList: if s_node == edge.start_station and e_node == edge.end_station: return edge = Edge(s_node, e_node, distance) self.EdgeList.append(edge) if e_node not in self.PrevEdge.keys(): self.PrevEdge[e_node] = [edge] else: self.PrevEdge[e_node].append(edge) if s_node not in self.NextEdge.keys(): self.NextEdge[s_node] = [edge] else: self.NextEdge[s_node].append(edge) # 弧段 class Edge: def __init__(self, start_node, end_node, distance): self.start_station = start_node self.end_station = end_node self.distance = distance class Section: def __init__(self, start_node, end_node, capacity): self.start_station = start_node self.end_station = end_node self.capacity = capacity # 各产品的运输需求 class ODFlow: def __init__(self): self.Id = 0 # 编号 self.Origin = '' # 出发地 self.Destination = '' # 目的地 self.Num = 0 # 重量 self.shortest_distance = 0 class Demand: def __init__(self): self.Flows = [] def add_flow(self, origin, dest, num): flow = ODFlow() flow.Origin = origin flow.Destination = dest flow.Num = num self.Flows.append(flow) def get_flow_num(self, origin, dest): result = list(filter(lambda x: x.Origin == origin and x.Destination == dest, self.Flows)) if len(result) > 0: return result[0].Num else: return 0 def get_flow(self, origin, dest): result = list(filter(lambda x: x.Origin == origin and x.Destination == dest, self.Flows)) if len(result) > 0: return result[0] else: return None # 路径 class Route: def __init__(self, *list): self.List = list def glb_generate_test_world(): section_list = [] section_list.append(Section("1", "2",300)) section_list.append(Section("1", "4", 300)) section_list.append(Section("1", "2", 210)) section_list.append(Section("1", "4", 300)) section_list.append(Section("2", "3", 300)) section_list.append(Section("2", "6", 300)) section_list.append(Section("3", "9", 380)) section_list.append(Section("4", "5", 300)) section_list.append(Section("4", "7", 300)) section_list.append(Section("5", "6", 300)) section_list.append(Section("5", "8", 380)) section_list.append(Section("6", "9", 380)) section_list.append(Section("6", "10", 300)) section_list.append(Section("6", "11", 300)) section_list.append(Section("9", "12", 300)) section_list.append(Section("10", "11", 300)) section_list.append(Section("10", "13", 300)) section_list.append(Section("11", "12", 300)) section_list.append(Section("11", "14", 300)) section_list.append(Section("13", "14", 300)) section_list.append(Section("7", "8", 380)) section_list.append(Section("8", "10", 400)) net = Network() net.add_edge("1", "2", 210) net.add_edge("1", "4", 265) net.add_edge("2", "3", 232) net.add_edge("2", "6", 123) net.add_edge("3", "9", 480) net.add_edge("4", "5", 109) net.add_edge("4", "7", 190) net.add_edge("5", "6", 172) net.add_edge("5", "8", 149) net.add_edge("6", "9", 368) net.add_edge("6", "10", 246) net.add_edge("6", "11", 280) net.add_edge("9", "12", 503) net.add_edge("10", "11", 130) net.add_edge("10", "13", 115) net.add_edge("11", "12", 336) net.add_edge("11", "14", 218) net.add_edge("13", "14", 158) net.add_edge("7", "8", 117) net.add_edge("8", "10", 220) net.add_edge("2", "1", 210) net.add_edge("4", "1", 265) net.add_edge("3", "2", 232) net.add_edge("6", "2", 123) net.add_edge("9", "3", 480) net.add_edge("5", "4", 109) net.add_edge("7", "4", 190) net.add_edge("6", "5", 172) net.add_edge("8", "5", 149) net.add_edge("9", "6", 368) net.add_edge("10", "6", 246) net.add_edge("11", "6", 280) net.add_edge("12", "9", 503) net.add_edge("11", "10", 130) net.add_edge("13", "10", 115) net.add_edge("12", "11", 336) net.add_edge("14", "11", 218) net.add_edge("14", "13", 158) net.add_edge("8", "7", 117) net.add_edge("10", "8", 220) demand = Demand() demand.add_flow("3", "7", 55) demand.add_flow("7", "9", 35) demand.add_flow("7", "14", 50) demand.add_flow("12", "7", 45) demand.add_flow("8", "9", 64) demand.add_flow("8", "12", 52) demand.add_flow("1", "8", 60) demand.add_flow("1", "14", 40) demand.add_flow("12", "1", 50) demand.add_flow("3", "8", 65) demand.add_flow("3", "11", 52) demand.add_flow("10", "3", 40) demand.add_flow("2", "12", 50) demand.add_flow("4", "9", 45) demand.add_flow("11", "4", 46) demand.add_flow("5", "13", 70) demand.add_flow("5", "12", 68) demand.add_flow("14", "5", 62) demand.add_flow("3", "13", 50) demand.add_flow("10", "1", 45) return net, demand, section_list def glb_finding_path(net, demand): # 定义了一个函数glb_finding_path,作用是寻找从一个起点到终点的路径. # 创建图 G = nx.DiGraph() # 创建了一个空的有向图G for edge in net.EdgeList: # for循环，用于遍历problem_input对象的EdgeList属性中的每个边 G.add_edge(edge.start_station, edge.end_station, weight=edge.distance) for i in net.NodeList: for j in net.NodeList: flow = demand.get_flow(i, j) if flow is not None: flow.shortest_distance = nx.dijkstra_path_length(G, i, j, "distance") # 在循环体内，向有向图G中添加一个边。这个边连接edge.start_station和edge.end_station，并将权重设置为edge.Distance # 绘制路网图 nx.draw(G, with_labels=True) # 使用NetworkX的draw函数绘制有向图G。参数with_labels=True表示将显示节点的标签 plt.title('Continue in 5 seconds') plt.axis('on') plt.xticks([]) plt.yticks([]) plt.savefig('路网图.png') # plt.show() # 显示图表 def glb_solve_mip(net, demand, section_list): model = Model() y = {(i, j, e): model.add_var(var_type=BINARY) for i in net.NodeList for j in net.NodeList for e in net.EdgeList} model.objective = minimize(xsum(y[i, j, e] * e.distance * demand.get_flow_num(i, j) for i in net.NodeList for j in net.NodeList for e in net.EdgeList)) for i in net.NodeList: for j in net.NodeList: if demand.get_flow_num(i, j) > 0: model += xsum(y[i, out_edge.end_station, out_edge] for out_edge in net.NextEdge[i]) == 1 for i in net.NodeList: for j in net.NodeList: