优化代码：daily_min_cost.append(min_transportation_cost(cost_matrix, demand_matrix))

可以使用列表推导式来优化代码，将循环和 append 操作合并为一步：

daily_min_cost = [min_transportation_cost(cost_matrix, demand_matrix) for demand_matrix in daily_demand_matrices]

这样可以减少循环操作，提高代码运行效率。

相关问题

for start_city, end_cities in around.items(): if start_city in grouped.groups: if start_city not in add_cities: orders_start = grouped.get_group(start_city) add_cities.append(start_city) if end_cities: for end_city in end_cities: if end_city in grouped.groups and end_city not in add_cities: orders_end = grouped.get_group(end_city) add_cities.append(end_city) orders_around = pd.concat([orders_start, orders_end]) result = pd.concat([result, orders_around]) else: if end_cities: for end_city in end_cities: if end_city in grouped.groups and end_city not in add_cities: orders_end = grouped.get_group(end_city) add_cities.append(end_city) result = pd.concat([result, orders_end])这段代码导致result里有重复数据，错误在哪,改正错误，重写代码

这段代码可能会导致result里有重复数据的原因是对于同一对起始地点和终点地点，可能存在多个订单，这些订单被合并到一个DataFrame中后，可能会存在重复的行。为了避免这种情况，可以在合并DataFrame之前，先将其去重。可以使用pandas.DataFrame.drop_duplicates()方法去重。下面是修改后的代码：

result = pd.DataFrame()
add_cities = []

for start_city, end_cities in around.items():
    if start_city in grouped.groups:
        if start_city not in add_cities:
            orders_start = grouped.get_group(start_city)
            add_cities.append(start_city)
            if end_cities:
                for end_city in end_cities:
                    if end_city in grouped.groups and end_city not in add_cities:
                        orders_end = grouped.get_group(end_city)
                        add_cities.append(end_city)
                        orders_around = pd.concat([orders_start, orders_end]).drop_duplicates()
                        result = pd.concat([result, orders_around]).drop_duplicates()
            else:
                result = pd.concat([result, orders_start]).drop_duplicates()
        else:
            if end_cities:
                for end_city in end_cities:
                    if end_city in grouped.groups and end_city not in add_cities:
                        orders_end = grouped.get_group(end_city)
                        add_cities.append(end_city)
                        result = pd.concat([result, orders_end]).drop_duplicates()

在处理orders_around和result时，添加了.drop_duplicates()方法以确保没有重复的行。

优化这段代码：def calTravelCost(route_list,model): timetable_list=[] distance_of_routes=0 time_of_routes=0 obj=0 for route in route_list: timetable=[] vehicle=model.vehicle_dict[route[0]] travel_distance=0 travel_time=0 v_type = route[0] free_speed=vehicle.free_speed fixed_cost=vehicle.fixed_cost variable_cost=vehicle.variable_cost for i in range(len(route)): if i == 0: next_node_id=route[i+1] travel_time_between_nodes=model.distance_matrix[v_type,next_node_id]/free_speed departure=max(0,model.demand_dict[next_node_id].start_time-travel_time_between_nodes) timetable.append((int(departure),int(departure))) elif 1<= i <= len(route)-2: last_node_id=route[i-1] current_node_id=route[i] current_node = model.demand_dict[current_node_id] travel_time_between_nodes=model.distance_matrix[last_node_id,current_node_id]/free_speed arrival=max(timetable[-1][1]+travel_time_between_nodes,current_node.start_time) departure=arrival+current_node.service_time timetable.append((int(arrival),int(departure))) travel_distance += model.distance_matrix[last_node_id, current_node_id] travel_time += model.distance_matrix[last_node_id, current_node_id]/free_speed+\ + max(current_node.start_time - arrival, 0) else: last_node_id = route[i - 1] travel_time_between_nodes = model.distance_matrix[last_node_id,v_type]/free_speed departure = timetable[-1][1]+travel_time_between_nodes timetable.append((int(departure),int(departure))) travel_distance += model.distance_matrix[last_node_id,v_type] travel_time += model.distance_matrix[last_node_id,v_type]/free_speed distance_of_routes+=travel_distance time_of_routes+=travel_time if model.opt_type==0: obj+=fixed_cost+travel_distance*variable_cost else: obj += fixed_cost + travel_time *variable_cost timetable_list.append(timetable) return timetable_list,time_of_routes,distance_of_routes,obj

可以尝试对代码进行如下优化：

1. 使用enumerate函数获取遍历列表时的下标和值，可以避免使用range(len(route))。

2. 减少重复计算，如model.distance_matrix[last_node_id,current_node_id]/free_speed和model.distance_matrix[last_node_id,v_type]/free_speed可以放在循环外面计算。

3. 使用+=运算符可以避免重复的赋值操作。

4. 合并if语句和elif语句，使用continue语句可以减少缩进层数。

5. 将int函数的调用移到循环外面，只进行一次强制类型转换。

优化后的代码如下：

def calTravelCost(route_list, model):
    timetable_list = []
    distance_of_routes = 0
    time_of_routes = 0
    obj = 0
    for route in route_list:
        timetable = []
        vehicle = model.vehicle_dict[route[0]]
        travel_distance = 0
        travel_time = 0
        v_type = route[0]
        free_speed = vehicle.free_speed
        fixed_cost = vehicle.fixed_cost
        variable_cost = vehicle.variable_cost
        last_node_id = None
        for i, current_node_id in enumerate(route):
            if i == 0:
                next_node_id = route[i+1]
                travel_time_between_nodes = model.distance_matrix[v_type, next_node_id] / free_speed
                departure = max(0, model.demand_dict[next_node_id].start_time - travel_time_between_nodes)
                timetable.append((departure, departure))
            else:
                current_node = model.demand_dict[current_node_id]
                travel_distance += model.distance_matrix[last_node_id, current_node_id]
                travel_time_between_nodes = model.distance_matrix[last_node_id, current_node_id] / free_speed
                arrival = max(timetable[-1][1] + travel_time_between_nodes, current_node.start_time)
                departure = arrival + current_node.service_time
                timetable.append((arrival, departure))
                travel_time += travel_time_between_nodes + max(current_node.start_time - arrival, 0)
                if i == len(route) - 1:
                    travel_distance += model.distance_matrix[last_node_id, v_type]
                    travel_time_between_nodes = model.distance_matrix[last_node_id, v_type] / free_speed
                    departure = timetable[-1][1] + travel_time_between_nodes
                    timetable.append((departure, departure))
            last_node_id = current_node_id
        distance_of_routes += travel_distance
        time_of_routes += travel_time
        if model.opt_type == 0:
            obj += fixed_cost + travel_distance * variable_cost
        else:
            obj += fixed_cost + travel_time * variable_cost
        timetable_list.append([(int(arrival), int(departure)) for arrival, departure in timetable])
    return timetable_list, time_of_routes, distance_of_routes, obj