def __init__(self):
#数据输入
self.base_profit = -5 #multipliers
data = Excel_processor(self.base_profit)
self.node_list, self.g_number_of_nodes, self.link_list, self.g_number_of_links, self.task_list, \
self.g_number_of_tasks,self.virtual_depot_id=data.read_links()
# 输入车辆属性*5
self.g_number_of_vehicles = 4
self.vehicle_capacity = 100
self.vehicle_departure_time_beginning = 0
self.vehicle_arrival_time_ending = 200
self.big_M = 100
#ADMM
self.ADMM_iteration_times = 100
self.rpo = 0.5 #惩罚参数
# DP参数
self.g_ending_state_vector = [None] * (self.g_number_of_vehicles + 1)
# self.g_ending_state_vector_for_lr = [None] * self.g_number_of_vehicles
self.g_time_dependent_state_vector = []
for v in range(self.g_number_of_vehicles + 1):
self.g_time_dependent_state_vector.append([])
intervals = self.vehicle_arrival_time_ending - self.vehicle_departure_time_beginning
for t in range(intervals + 1):
self.g_time_dependent_state_vector[v].append(
[None] * self.g_number_of_nodes)
# beam searching：每个结点保留最多两个状态，成本最小
self.Best_K_Size = 2
# SST路径
self.path_space_seq = []
self.path_time_seq = []
self.path_state_seq = []
# 统计误差、记录乘子
self.served_time = []
self.repeat_served = []
self.un_served = []
self.record_profit = []
self.max_label_cost = float("inf")
# 上下界-ADMM
self.ADMM_local_LB = [0] * self.ADMM_iteration_times
self.ADMM_local_UB = [0] * self.ADMM_iteration_times
self.ADMM_global_LB = [-self.max_label_cost
] * self.ADMM_iteration_times
self.ADMM_global_UB = [self.max_label_cost] * self.ADMM_iteration_times
print("初始化完成！")
#part 1 Column pool information
self.column_pool_path_seq = [[]
for i in range(self.g_number_of_vehicles)]
self.column_pool_path_cost = [[]
for i in range(self.g_number_of_vehicles)
]
self.column_pool_path_serving = [
[] for i in range(self.g_number_of_vehicles)
]
def __init__(self):
# Data input
self.base_profit = -100000 #big profit for initial feasible solution
data = Excel_processor(self.base_profit)
self.node_list, self.g_number_of_nodes, self.link_list, self.g_number_of_links, self.task_list, \
self.g_number_of_tasks, self.virtual_depot_id = data.read_links()
# vehicle data
self.g_number_of_vehicles = 1
self.vehicle_capacity = 100
self.vehicle_departure_time_beginning = 0
self.vehicle_arrival_time_ending = 200
self.big_M = 100
#DW parameter
self.DW_iteration_time = 500
# DP parameters
self.g_ending_state_vector = [None] * (self.g_number_of_vehicles + 1)
self.g_time_dependent_state_vector = []
for v in range(self.g_number_of_vehicles + 1):
self.g_time_dependent_state_vector.append([])
intervals = self.vehicle_arrival_time_ending - self.vehicle_departure_time_beginning
for t in range(intervals + 1):
self.g_time_dependent_state_vector[v].append(
[None] * self.g_number_of_nodes)
# beam searching：每个结点保留最多两个状态，成本最小
self.Best_K_Size = 3
#Column information
self.g_Column_SST_path = [] #[[[state],[space],[time]]]
self.g_Column_cost = [] #[cost]
self.g_Column_task_to_path_matrix = [] #[[all tasks]]
#Output information
#1.record LB and UB
#2.path information (Iteration/vehicle/SST path)
self.optimal_SST_path = []
self.optimal_serving_state = []
self.selected_columns = []
self.reduced_cost_for_LB = []
#3.dual variables(iteration/|P|+1)
# self.dual_variables=[]
print("Input is complete！")