使用gurobipy在pycharm中编写代码，建立车联网中路径规划优化模型，车辆数为10，道路数为10，节点数为8，各车辆的起始点与终点各不相同，各道路存在限速，以及道路容量，在有车辆在此道路行驶时，道路容量数减少1，当道路数小于0时，视为该道路封闭，为不可通行状态，在车辆行驶时可能存在中间节点的情况，需要在某节点等待一段时间，在此等待时视为行驶成本增加，目标函数为行驶时间最短，车辆行驶时能耗与行驶路径长度呈线性关系

首先，需要导入相关的库和数据：

from gurobipy import *

# 车辆数为10
n_vehicles = 10

# 道路数为10
n_roads = 10

# 节点数为8
n_nodes = 8

# 道路限速
road_speed_limit = [40, 40, 60, 60, 60, 80, 100, 100, 120, 120]

# 道路容量
road_capacity = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

# 起始点与终点(仅示例，具体根据实际情况设置)
start_nodes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
end_nodes = [8, 7, 6, 5, 4, 3, 2, 1, 10, 9]

# 行驶时间
travel_time = [
    [0, 10, 20, 30, 40, 50, 60, 70],
    [10, 0, 10, 20, 30, 40, 50, 60],
    [20, 10, 0, 10, 20, 30, 40, 50],
    [30, 20, 10, 0, 10, 20, 30, 40],
    [40, 30, 20, 10, 0, 10, 20, 30],
    [50, 40, 30, 20, 10, 0, 10, 20],
    [60, 50, 40, 30, 20, 10, 0, 10],
    [70, 60, 50, 40, 30, 20, 10, 0]
]

# 等待时间
wait_time = [
    [0, 1, 2, 3, 4, 5, 6, 7],
    [1, 0, 1, 2, 3, 4, 5, 6],
    [2, 1, 0, 1, 2, 3, 4, 5],
    [3, 2, 1, 0, 1, 2, 3, 4],
    [4, 3, 2, 1, 0, 1, 2, 3],
    [5, 4, 3, 2, 1, 0, 1, 2],
    [6, 5, 4, 3, 2, 1, 0, 1],
    [7, 6, 5, 4, 3, 2, 1, 0]
]

接下来，需要定义模型和变量：

# 创建模型
model = Model("vehicle_routing")

# 创建变量
# 路径变量
x = [[[0] * n_nodes for i in range(n_nodes)] for j in range(n_vehicles)]
for k in range(n_vehicles):
    for i in range(n_nodes):
        for j in range(n_nodes):
            if i != j:
                x[k][i][j] = model.addVar(lb=0, ub=1, vtype=GRB.BINARY, name=f'x_{k}_{i}_{j}')

# 等待时间变量
w = [[0] * n_nodes for i in range(n_vehicles)]
for k in range(n_vehicles):
    for i in range(n_nodes):
        w[k][i] = model.addVar(lb=0, ub=10, vtype=GRB.INTEGER, name=f'w_{k}_{i}')

# 道路容量变量
c = [[0] * n_roads for i in range(n_nodes)]
for i in range(n_nodes):
    for j in range(n_roads):
        c[i][j] = model.addVar(lb=0, ub=road_capacity[j], vtype=GRB.INTEGER, name=f'c_{i}_{j}')

接着，需要定义约束条件：

# 每个车辆只能从起始点出发一次，到达终点一次
for k in range(n_vehicles):
    model.addConstr(quicksum(x[k][start_nodes[k]][j] for j in range(n_nodes) if j != start_nodes[k]) == 1)
    model.addConstr(quicksum(x[k][i][end_nodes[k]] for i in range(n_nodes) if i != end_nodes[k]) == 1)

# 保证路径连通性
for k in range(n_vehicles):
    for j in range(n_nodes):
        if j != start_nodes[k] and j != end_nodes[k]:
            model.addConstr(quicksum(x[k][i][j] for i in range(n_nodes) if i != j) ==
                            quicksum(x[k][j][i] for i in range(n_nodes) if i != j))

# 道路容量约束
for j in range(n_roads):
    for i in range(n_nodes):
        model.addConstr(quicksum(x[k][i][j] for k in range(n_vehicles)) <= c[i][j])
        model.addConstr(c[i][j] <= road_capacity[j])

# 每个节点的入度等于出度
for k in range(n_vehicles):
    for i in range(n_nodes):
        model.addConstr(quicksum(x[k][j][i] for j in range(n_nodes) if j != i) ==
                        quicksum(x[k][i][j] for j in range(n_nodes) if j != i))

# 限速约束
for k in range(n_vehicles):
    for i in range(n_nodes):
        for j in range(n_nodes):
            if i != j:
                model.addConstr(travel_time[i][j] / x[k][i][j] <= 3600 / road_speed_limit[i])

# 等待时间约束
for k in range(n_vehicles):
    for i in range(n_nodes):
        for j in range(n_nodes):
            if i != j:
                model.addConstr(w[k][j] <= 10 * (1 - x[k][i][j]))
                model.addConstr(w[k][j] >= wait_time[i][j] - 10 * (1 - x[k][i][j]))

最后，需要定义目标函数并求解：

# 定义目标函数
obj = quicksum(travel_time[i][j] * x[k][i][j] + w[k][j] * x[k][i][j] for k in range(n_vehicles)
               for i in range(n_nodes) for j in range(n_nodes) if i != j)
model.setObjective(obj, GRB.MINIMIZE)

# 求解模型
model.optimize()

完整代码如下：