from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp
import numpy as np
import datetime
import math
# ================== 数据结构定义 ==================
class EnhancedVehicle:
def __init__(self, vid, capacity, is_own, start_node, partitions):
self.vid = vid # 车辆ID
self.capacity = capacity # 车辆容量
self.is_own = is_own # 是否自有车
self.start_node = start_node # 出发节点
self.partitions = partitions # 允许访问分区
class EnhancedCustomer:
def __init__(self, demand, time_window, partition, is_overdue):
self.demand = demand # 需求重量
self.time_window = time_window # 时间窗（分钟）
self.partition = partition # 所属分区
self.is_overdue = is_overdue # 是否超时订单
# ================== 数据生成器 ==================
def create_enhanced_data_model(orders, vehicles, distance_matrix, time_matrix, fixed_cross_time, current_time, convertDistanceTime):
"""生成增强的测试数据集"""
data = {}
np.random.seed(42)
# 基本参数
data['depot'] = 0 # 分拣中心节点
# current_time = datetime.datetime.now()
data['base_time'] = current_time
data['cross_time'] = fixed_cross_time # 允许跨区时间（分钟）
# 生成200个客户订单（包含20%超时订单）
data['customers'] = orders
data['lenCustomers'] = len(data['customers'])
for i in range(len(data['customers'])):
if data['customers'][i].isOutTime:
data['customers'][i].startTW = 0
data['customers'][i].endTW = 3600 * 48
# start = np.random.randint(0, 480)
# end = start + np.random.randint(60, 240)
# is_overdue = np.random.rand() < 0.2 # 20%超时
# data['customers'].append(
# EnhancedCustomer(
# demand=np.random.randint(1, 5),
# time_window=(start, end) if not is_overdue else (0, 1440),
# partition=np.random.randint(0, 10),
# is_overdue=is_overdue
# )
# )
# 生成15辆车（自有车10辆，外请车5辆）
data['vehicles'] = vehicles
data['lenVehicles'] = len(data['vehicles'])
for vid in range(len(data['vehicles'])):
data['vehicles'][vid].start_node = vid + 1 # 每辆车有独立出发位置
# data['vehicles'].append(
# EnhancedVehicle(
# vid=vid,
# capacity=np.random.randint(20, 50) if vid < 10 else np.random.randint(15, 40),
# is_own=(vid < 10),
# start_node=start_node,
# partitions=[vid % 10] + ([] if vid < 10 else [(vid % 10 + 1) % 10])
# )
# )
# 构建距离矩阵（包含分拣中心+车辆出发位置+客户位置）
# node_count = 1 + len(data['vehicles']) + len(data['customers']) # 分拣中心(0) + 车辆起点(1-15) + 客户(16-215)
data['distance_matrix'] = distance_matrix #np.random.randint(5, 100, (node_count, node_count))
data['time_matrix'] = time_matrix
# np.fill_diagonal(data['distance_matrix'], 0)
data['convertDistanceTime'] = convertDistanceTime
data['customer_start_idx'] = 1 + len(data['vehicles'])
#更改字段名称
for i in range(len(data['vehicles'])):
data['vehicles'][i].vid = i
for item in data['vehicles']:
item.capacity = item.residualVolumeMax
all_allowed_zones = []
for item in data['vehicles']:
all_allowed_zones += item.areaIds
all_allowed_zones = list(set(all_allowed_zones))
for item in data['vehicles']:
# item.allowed_zones = item.areaIds
if item.areaIds == []:
item.allowed_zones = all_allowed_zones
else:
item.allowed_zones = item.areaIds
item.allowed_zones = [int(item1) for item1 in item.allowed_zones]
for item in data['customers']:
item.zone = int(item.areaId)
return data
# ================== 核心约束实现 ==================
def add_partition_constraints(routing, manager, data, time_dimension, fixed_cross_time):
"""实现动态分区访问控制"""
solver = routing.solver()
# 1. 创建分区状态维度
def zone_callback(from_index):
from_node = manager.IndexToNode(from_index)
# if from_node == data['depot']:
# return -2 # 分拣中心特殊标记
if from_node < data['customer_start_idx']:
return -1 # 车辆起点特殊标记
customer_idx = from_node - data['customer_start_idx']
return data['customers'][customer_idx].zone
zone_callback_idx = routing.RegisterUnaryTransitCallback(zone_callback)
routing.AddDimension(
zone_callback_idx,
0, # slack_max
1000, # 分区容量上限
False, # start_cumul_to_zero
'Zone'
)
zone_dim = routing.GetDimensionOrDie('Zone')
# 2. 为每辆车添加动态分区约束
for vehicle in data['vehicles']:
vehicle_id = vehicle.vid
allowed_zones = set(vehicle.allowed_zones)
cross_time = int(fixed_cross_time) #vehicle.cross_time
# 动态分区检查回调
def zone_check_callback(from_index, to_index):
nonlocal allowed_zones, cross_time
to_node = manager.IndexToNode(to_index)
# 允许返回分拣中心
if to_node == data['depot']:
return True
# 非客户节点直接允许
if to_node < data['customer_start_idx']:
return True
# 获取目标分区
zone = zone_callback(to_index)
# 获取到达时间
arrival_time_var = time_dimension.CumulVar(to_index)
min_arrival = int(solver.Min(arrival_time_var))
# 判断是否达到切换阈值
if min_arrival < cross_time:
# 检查当前是否已有访问分区
start_index = routing.Start(vehicle_id)
first_node_index = routing.NextVar(start_index)
# 获取首个分区变量
first_zone_var = zone_dim.CumulVar(first_node_index)
# 添加约束：时间阈值前必须与首个分区一致
solver.Add(
solver.IfThen(
zone_dim.CumulVar(to_index) != -1,
zone_dim.CumulVar(to_index) == first_zone_var
)
)
# 添加约束：首个分区必须在允许分区内
solver.Add(
solver.IfThen(
first_zone_var != -1,
solver.Member(first_zone_var, list(allowed_zones))
)
)
else:
# 添加约束：时间阈值后必须在允许分区内
solver.Add(
solver.Member(zone_dim.CumulVar(to_index), list(allowed_zones))
)
# # # 添加约束：时间阈值前必须与首个分区一致
# # if solver.Value(zone_dim.CumulVar(to_index)) != -1:
# # solver.Add(zone_dim.CumulVar(to_index) == first_zone_var)
# #
# # # 添加约束：首个分区必须在允许分区内
# # if solver.Value(first_zone_var) != -1:
# # solver.Add(solver.Member(first_zone_var, list(allowed_zones)))
# # else:
# # # 添加约束：时间阈值后必须在允许分区内
# # if solver.Value(zone_dim.CumulVar(to_index)) != -1:
# # solver.Add(solver.Member(zone_dim.CumulVar(to_index), list(allowed_zones)))
#
# return True
# 注册转移回调
zone_check_idx = routing.RegisterTransitCallback(zone_check_callback)
# # 3. 添加软约束确保路径可行性
# penalty = 1000000 # 大惩罚值
# routing.AddDisjunction(
# [manager.NodeToIndex(i) for i in range(manager.GetNumberOfNodes())],
# penalty
# )
return routing
# for vehicle in data['vehicles']:
# vehicle_id = vehicle.vid
# allowed_zones = vehicle.allowed_zones
# cross_time = fixed_cross_time #vehicle.cross_time
#
# def zone_check(from_index, to_index):
# from_node = manager.IndexToNode(from_index)
# to_node = manager.IndexToNode(to_index)
#
# # 允许返回分拣中心
# if to_node == data['depot']:
# return True
#
# # 客户节点处理
# if to_node >= data['customer_start_idx']:
# customer = data['customers'][to_node - data['customer_start_idx']]
# to_zone = customer.zone
# else:
# return True # 车辆起始点
#
# # 获取当前时间
# time_var = time_dimension.CumulVar(to_index)
# current_time = routing.solver().Min(time_var)
#
# # 判断时间阶段
# if current_time < cross_time:
# # 检查路径中已访问的分区
# path_zones = set()
# index = routing.Start(vehicle_id)
# while not routing.IsEnd(index):
# node = manager.IndexToNode(index)
# if node >= data['customer_start_idx']:
# zone = data['customers'][node - data['customer_start_idx']].zone
# path_zones.add(zone)
# index = routing.NextVar(index) #solution.Value(routing.NextVar(index))
#
# # 允许访问的情况
# return (len(path_zones) == 0 and to_zone in allowed_zones) or \
# (len(path_zones) > 0 and to_zone in path_zones)
# else:
# return to_zone in allowed_zones
#
# next_var = routing.NextVar(routing.Start(vehicle_id))
# # 添加约束到车辆
# routing.solver().AddConstraint(
# routing.solver().CheckAssignment(next_var, zone_check))
# ================== 主求解逻辑 ==================
def optimized_vrp_solver(orders, vehicles, distance_matrix, time_matrix, fixed_cross_time, current_time, convertDistanceTime):
data = create_enhanced_data_model(orders, vehicles, distance_matrix, time_matrix, fixed_cross_time, current_time, convertDistanceTime)
# a = len(data['distance_matrix'])
# data = {}
# data['distance_matrix'] = [[0,1,2,3,4,5,6,7,8,9,10],
# [1,0,2,3,4,5,6,7,8,9,10],
# [2,1,0,3,4,5,6,7,8,9,10],
# [3,1,2,0,4,5,6,7,8,9,10],
# [4,1,2,3,0,5,6,7,8,9,10],
# [5,1,2,3,4,0,6,7,8,9,10],
# [6,1,2,3,4,5,0,7,8,9,10],
# [7,1,2,3,4,5,6,0,8,9,10],
# [8,1,2,3,4,5,6,7,0,9,10],
# [9,1,2,3,4,5,6,7,8,0,10],
# [10,1,2,3,4,5,6,7,8,9,0],
# ]
# data['demands'] = [0] * 6 + [item.demand for item in data['customers']]
# data["vehicle_capacities"] = [item.capacity for item in data['vehicles']] # 3辆车的容量
# data["demands"] = [0, 0, 0, 0, 0, 0, 2.7, 2, 2, 1, 3] # 分拣中心和起点需求为0
# data["vehicle_capacities"] = [5, 7, 10, 6, 1] # 3辆车的容量
# print(data["demands"])
# print(data["vehicle_capacities"])
# 数据准备阶段
SCALE_FACTOR = 1000 # 转换为克/毫升
# data['demands'] = [int(d * SCALE_FACTOR) for d in data['demands']]
# data['demands'] = [int(d * SCALE_FACTOR) for d in data['demands']]
# data['customers'] = [int(item.demand * SCALE_FACTOR) for item in data['customers']]
for item in data['customers']:
item.demand = int(item.demand * SCALE_FACTOR)
for item in data['vehicles']:
item.capacity = int(item.capacity * SCALE_FACTOR)
# data['vehicles'][0].capacity = 5000
# data['vehicles'][1].capacity = 2500
# data['vehicles'][2].capacity = 3500
# data['vehicles'][3].capacity = 15000
# data['vehicles'][4].capacity = 10000
# data["vehicle_capacities"] = [int(c * SCALE_FACTOR) for c in data["vehicle_capacities"]]
# # 添加安全边界
# SAFETY_FACTOR = 0.999 # 99.9%容量利用率
# data["vehicle_capacities"] = [cap * SAFETY_FACTOR for cap in data["vehicle_capacities"]]
print([item.demand for item in data['customers']])
print([item.capacity for item in data['vehicles']])
# print(data["demands"])
# print(data["vehicle_capacities"])
data["num_vehicles"] = len(data['vehicles'])
# 每辆车的起点（索引1,2,3），终点都是分拣中心（索引0）
# data["starts"] = [1, 2, 3, 4, 5]
# data["ends"] = [0, 0, 0,0,0]
# # 距离矩阵（包含分拣中心和所有起点）
# data["distance_matrix"] = [
# [0, 5, 8, 6, 7, 1, 2, 10], # 分拣中心（索引0）
# [5, 0, 6, 3, 2, 1, 2, 9], # 起点1
# [8, 6, 0, 8, 4, 1, 2, 8], # 起点2
# [6, 3, 8, 0, 5, 1, 2, 7], # 起点3
# [7, 2, 4, 5, 0, 1, 2, 3], # 客户点
# [7, 2, 4, 5, 2, 0, 2, 6], # 客户点
# [7, 2, 4, 5, 6, 1, 0, 5], # 客户点
# [7, 2, 4, 5, 6, 1, 2, 0], # 客户点
# ]
# data["demands"] = [0, 0, 0, 0, 3,4,5,6] # 分拣中心和起点需求为0
# data["vehicle_capacities"] = [25, 20, 10] # 3辆车的容量
# data["num_vehicles"] = 3
# # 每辆车的起点（索引1,2,3），终点都是分拣中心（索引0）
# data["starts"] = [1, 2, 3]
# data["ends"] = [0, 0, 0]
manager = pywrapcp.RoutingIndexManager(
len(data['distance_matrix']),
data["num_vehicles"],
[v.start_node for v in data['vehicles']], # 各车起始位置 data["starts"], #
[data['depot']] * len(data['vehicles']) #data["ends"] #
) # 统一返回分拣中心
routing = pywrapcp.RoutingModel(manager)
# ========== 注册核心回调函数 ==========
def distance_callback(from_index, to_index):
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return data["distance_matrix"][from_node][to_node]
transit_callback_idx = routing.RegisterTransitCallback(distance_callback)
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_idx)
# ========== 时间维度约束 ==========
service_time = 10 # 每个客户服务时间10分钟
def time_callback(from_index, to_index):
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
travel_time = data['time_matrix'][from_node][to_node]
# travel_time = distance_callback(from_index, to_index) * data['convertDistanceTime'] * 60 # 假设速度1单位/分钟
travel_time = travel_time + (data['customers'][from_node - data['customer_start_idx']].servTime if from_node >= data[
'customer_start_idx'] else 0)
return int(travel_time)
time_callback_idx = routing.RegisterTransitCallback(time_callback)
routing.AddDimension(
time_callback_idx,
slack_max=0, # 不允许等待
capacity=24 * 3600, # 最大时间限制（24小时）24 * 3600, # 车辆最大工作时间
fix_start_cumul_to_zero=False,# 起始时间为当前时间
name='Time'
)
time_dimension = routing.GetDimensionOrDie('Time')
# 设置车辆起始时间（当前系统时间）
for vehicle in data['vehicles']:
vehicle_id = vehicle.vid
start_index = routing.Start(vehicle_id)
time_dimension.CumulVar(start_index).SetMin(current_time)
time_dimension.CumulVar(start_index).SetMax(current_time)
# 设置客户时间窗
for customer_idx in range(len(data['customers'])):
index = manager.NodeToIndex(customer_idx + data['customer_start_idx']) # 客户节点从16开始
print(index)
if not data['customers'][customer_idx].isOutTime:
time_dimension.CumulVar(index).SetRange(
data['customers'][customer_idx].startTW,
data['customers'][customer_idx].endTW
)
# ========== 容量约束 ==========
def demand_callback(from_index):
node = manager.IndexToNode(from_index)
if node < data['customer_start_idx']: return 0 # 非客户节点无需求
return data['customers'][node - data['customer_start_idx']].demand
# def demand_callback(from_index):
# from_node = manager.IndexToNode(from_index)
# # print('from_index', from_index)
# # print('node',node)
# return data['demands'][from_node]
demand_callback_idx = routing.RegisterUnaryTransitCallback(demand_callback)
routing.AddDimensionWithVehicleCapacity(
demand_callback_idx,
0, # null capacity slack
[v.capacity for v in data['vehicles']], #data["vehicle_capacities"], #
True,
'Capacity'
)
capacity_dimension = routing.GetDimensionOrDie('Capacity')
# capacity_dimension.SetCumulVarSoftUpperBoundToZero() # 严格约束
# ========== 高级约束 ==========
#1. 分区动态约束
routing = add_partition_constraints(routing, manager, data, time_dimension, fixed_cross_time)
# 2. 自有车优先（设置不同固定成本）
for vid in range(len(data['vehicles'])):
routing.SetFixedCostOfVehicle(100 if data['vehicles'][vid].isHaveTask == 0 else 10000, vid)
# # 3. 超时订单优先（设置不同惩罚值）
# penalty = 1000000 # 未服务的惩罚
# for customer_idx in range(len(data['customers'])):
# penalty = 1000000 if data['customers'][customer_idx].isOutTime else 1000
# routing.AddDisjunction(
# [manager.NodeToIndex(customer_idx + data['customer_start_idx'])],
# penalty
# )
# ========== 求解参数配置 ==========
search_params = pywrapcp.DefaultRoutingSearchParameters()
search_params.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
)
search_params.local_search_metaheuristic = (
routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
)
search_params.time_limit.seconds = 60 # 60秒求解时间
# search_params.log_search = True
# ========== 执行求解 ==========
solution = routing.SolveWithParameters(search_params)
# ========== 结果解析 ==========
if solution:
"""打印解决方案"""
total_distance = 0
for vehicle_id in range(data["num_vehicles"]):
print('vehicle_id', vehicle_id)
index = routing.Start(vehicle_id)
node_index = manager.IndexToNode(index)
print('node_index', node_index)
plan_output = f"车辆 {vehicle_id} 路线:\n"
route_distance = 0
route_load = 0
while not routing.IsEnd(index):
node_index = manager.IndexToNode(index)
if node_index == 0:
node_type = "中心"
elif node_index >= data['customer_start_idx']:
node_type = f"客户{node_index - data['customer_start_idx'] + 1}"
else:
node_type = f"车辆{node_index}起始点"
# 获取时间信息
time_var = time_dimension.CumulVar(index)
arrival_time = solution.Min(time_var)
if node_index < data['customer_start_idx']:
departure_time = arrival_time
else:
departure_time = arrival_time + data['customers'][node_index-data['customer_start_idx']].servTime
# 格式化为可读时间
arr_time_str = arrival_time #min_to_time(arrival_time).strftime("%H:%M")
dep_time_str = departure_time #min_to_time(departure_time).strftime("%H:%M")
# 添加到输出
plan_output += (
f"节点 {node_index}({node_type}) | "
f"到达: {arrival_time}s ({arr_time_str}) | "
f"离开: {departure_time}s ({dep_time_str})\n"
)
next_node_index = manager.IndexToNode(solution.Value(routing.NextVar(index)))
if node_index >= data['customer_start_idx']:
route_load += data['customers'][node_index-data['customer_start_idx']].demand #data["demands"][node_index]
plan_output += f" {node_index} ->"
previous_index = index
index = solution.Value(routing.NextVar(index))
route_distance += routing.GetArcCostForVehicle(
previous_index, index, vehicle_id
)
# 输出终点(仓库)
node_index = manager.IndexToNode(index)
time_var = time_dimension.CumulVar(index)
arrival_time = solution.Min(time_var)
arr_time_str = arrival_time #min_to_time(arrival_time).strftime("%H:%M")
plan_output += (
f"节点 {node_index}(中心) | "
f"到达: {arrival_time}s ({arr_time_str})\n"
)
plan_output += f" {manager.IndexToNode(index)}\n"
plan_output += f"行驶距离: {route_distance}\t载重量: {route_load}\n"
print(plan_output)
total_distance += route_distance
print(f"总行驶距离: {total_distance}")
else:
print("No solution found!")
# if __name__ == '__main__':
# optimized_vrp_solver(orders, vehicles, distance_matrix, time_matrix, fixed_cross_time, current_time)
# optimized_vrp_solver()
本文详细解释了六种可能导致数据库索引分区变为INDEXUNUSABLE状态的维护操作，包括导入分区、常规路径SQL*Loader、直接路径SQL*Loader、分区维护操作如移动分区、截断分区、分裂分区，以及索引维护操作如分裂分区。这些操作可能需要重建索引分区来恢复正常。
扫一扫
957
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
