import numpy as np import copy import random from vrptw_base import VrptwGraph from threading import Event class Ant: def __init__(self, graph: VrptwGraph, start_index=0): super() self.graph = graph self.current_index = start_index self.vehicle_load = 0 self.vehicle_travel_time = 0 self.travel_path = [start_index] self.arrival_time = [0] self.index_to_visit = list(range(graph.node_num)) self.index_to_visit.remove(start_index) self.total_travel_distance = 0 def move_to_next_index(self, next_index): # 更新蚂蚁路径 self.travel_path.append(next_index) self.total_travel_distance += self.graph.node_dist_mat[self.current_index][next_index] dist = self.graph.node_dist_mat[self.current_index][next_index] self.arrival_time.append(self.vehicle_travel_time + dist) if self.graph.nodes[next_index].is_depot: # 如果一下个位置为服务器点，则要将车辆负载等清空 self.vehicle_load = 0 self.vehicle_travel_time = 0 # remove duplicated_depot if next_index in self.index_to_visit: self.index_to_visit.remove(next_index) else: # 更新车辆负载、行驶距离、时间 self.vehicle_load += self.graph.nodes[next_index].demand # 如果早于客户要求的时间窗(ready_time)，则需要等待 self.vehicle_travel_time += dist + max(self.graph.nodes[next_index].ready_time - self.vehicle_travel_time - dist, 0) + self.graph.nodes[next_index].service_time self.index_to_visit.remove(next_index) self.current_index = next_index def index_to_visit_empty(self): return len(self.index_to_visit) == 0 def get_active_vehicles_num(self): return self.travel_path.count(0)-1 def check_condition(self, next_index) -> bool: """ 检查移动到下一个点是否满足约束条件 :param next_index: :return: """ if self.vehicle_load + self.graph.nodes[next_index].demand > self.graph.vehicle_capacity: return False dist = self.graph.node_dist_mat[self.current_index][next_index] wait_time = max(self.graph.nodes[next_index].ready_time - self.vehicle_travel_time - dist, 0) service_time = self.graph.nodes[next_index].service_time # 检查访问某一个旅客之后，能否回到服务店 if self.vehicle_travel_time + dist + wait_time + service_time + self.graph.node_dist_mat[next_index][0] > self.graph.nodes[0].due_time: return False # 不可以服务due time之外的旅客 if self.vehicle_travel_time + dist > self.graph.nodes[next_index].due_time: return False return True def cal_next_index_meet_constrains(self): """ 找出所有从当前位置（ant.current_index）可达的customer :return: """ next_index_meet_constrains = [] for next_ind in self.index_to_visit: if self.check_condition(next_ind): next_index_meet_constrains.append(next_ind) return next_index_meet_constrains def cal_nearest_next_index(self, next_index_list): """ 从待选的customers中选择，离当前位置（ant.current_index）最近的customer :param next_index_list: :return: """ current_ind = self.current_index nearest_ind = next_index_list[0] min_dist = self.graph.node_dist_mat[current_ind][next_index_list[0]] for next_ind in next_index_list[1:]: dist = self.graph.node_dist_mat[current_ind][next_ind] if dist < min_dist: min_dist = dist nearest_ind = next_ind return nearest_ind def cal_total_travel_distance(self, travel_path): distance = 0 current_ind = travel_path[0] for next_ind in travel_path[1:]: distance += self.graph.node_dist_mat[current_ind][next_ind] current_ind = next_ind return distance def try_insert_on_path(self, node_id, stop_event: Event): """ 尝试性地将node_id插入当前的travel_path中 插入的位置不能违反载重，时间，行驶距离的限制 如果有多个位置，则找出最优的位置 :param node_id: :return: """ feasible_insert_index = [] feasible_distance = [] for insert_index in range(len(self.travel_path)): if stop_event.is_set(): # print('[try_insert_on_path]: receive stop event') return if self.graph.nodes[self.travel_path[insert_index]].is_depot: continue front_depot_index = insert_index while front_depot_index >= 0 and not self.graph.nodes[self.travel_path[front_depot_index]].is_depot: front_depot_index -= 1 front_depot_index = max(front_depot_index, 0) check_ant = Ant(self.graph, self.travel_path[front_depot_index]) # 让check_ant 走过 path中下标从front_depot_index开始到insert_index-1的点 for i in range(front_depot_index+1, insert_index): check_ant.move_to_next_index(self.travel_path[i]) # 开始尝试性地对排序后的index_to_visit中的结点进行访问 if check_ant.check_condition(node_id): check_ant.move_to_next_index(node_id) else: continue # 如果可以到node_id，则要保证vehicle可以行驶回到depot for next_ind in self.travel_path[insert_index:]: if stop_event.is_set(): # print('[try_insert_on_path]: receive stop event') return if check_ant.check_condition(next_ind): check_ant.move_to_next_index(next_ind) # 如果回到了depot if self.graph.nodes[next_ind].is_depot: feasible_insert_index.append(insert_index) # 计算距离 path = copy.deepcopy(self.travel_path) path.insert(insert_index, node_id) feasible_distance.append(self.cal_total_travel_distance(path)) break # 如果不可以回到depot，则返回上一层 else: break if len(feasible_distance) == 0: return None else: feasible_distance = np.array(feasible_distance) min_insert_ind = np.argmin(feasible_distance) best_ind = feasible_insert_index[int(min_insert_ind)] return best_ind def insertion_procedure(self, stop_even: Event): """ 为每个未访问的结点尝试性地找到一个合适的位置，插入到当前的travel_path 插入的位置不能违反载重，时间，行驶距离的限制 :return: """ if self.index_to_visit_empty(): return ind_to_visit = np.array(copy.deepcopy(self.index_to_visit)) demand = np.zeros(len(ind_to_visit)) for i in range(len(ind_to_visit)): demand[i] = self.graph.nodes[i].demand sorted_ind = np.argsort(demand) ind_to_visit = ind_to_visit[sorted_ind] for node_id in ind_to_visit: if stop_even.is_set(): # print('[insertion_procedure]: receive stop event') return best_insert_index = self.try_insert_on_path(node_id, stop_even) if best_insert_index is not None: self.travel_path.insert(best_insert_index, node_id) self.index_to_visit.remove(node_id) self.total_travel_distance = self.cal_total_travel_distance(self.travel_path) def local_search_procedure(self, stop_event: Event): """ 对当前的已经访问完graph中所有节点的travel_path使用cross进行局部搜索 :return: """ # 找出path中所有的depot的位置 depot_ind = [] for ind in range(len(self.travel_path)): if self.graph.nodes[self.travel_path[ind]].is_depot: depot_ind.append(ind) new_path_travel_distance = [] new_path = [] # 将self.travel_path分成多段，每段以depot开始，以depot结束，称为route for i in range(1, len(depot_ind)): for j in range(i+1, len(depot_ind)): if stop_event.is_set(): # print('[local_search_procedure]: receive stop event') return # 随机在两段route，各随机选择一段customer id，交换这两段customer id start_a = random.randint(depot_ind[i-1]+1, depot_ind[i]-1) end_a = random.randint(depot_ind[i-1]+1, depot_ind[i]-1) if end_a < start_a: start_a, end_a = end_a, start_a start_b = random.randint(depot_ind[j-1]+1, depot_ind[j]-1) end_b = random.randint(depot_ind[j - 1] + 1, depot_ind[j] - 1) if end_b < start_b: start_b, end_b = end_b, start_b path = [] path.extend(self.travel_path[:start_a]) path.extend(self.travel_path[start_b:end_b+1]) path.extend(self.travel_path[end_a:start_b]) path.extend(self.travel_path[start_a:end_a]) path.extend(self.travel_path[end_b+1:]) if len(path) != len(self.travel_path): raise RuntimeError('error') # 判断新生成的path是否是可行的 check_ant = Ant(self.graph, path[0]) for ind in path[1:]: if check_ant.check_condition(ind): check_ant.move_to_next_index(ind) else: break # 如果新生成的path是可行的 if check_ant.index_to_visit_empty() and check_ant.total_travel_distance < self.total_travel_distance: # print('success to search') new_path_travel_distance.append(check_ant.total_travel_distance) new_path.append(path) else: path.clear() # 找出新生成的path中，路程最小的 if len(new_path_travel_distance) > 0: new_path_travel_distance = np.array(new_path_travel_distance) min_distance_ind = np.argmin(new_path_travel_distance) self.travel_path = copy.deepcopy(new_path[int(min_distance_ind)]) self.index_to_visit.clear() for i in range(len(new_path)): new_path[i].clear() new_path.clear() del new_path_travel_distance # print('[new_active_ant]: local search finished')