LNR-server-01-input-data-processing

import json import random def allocate_restoration_resource_considering_time(global_json_path): global_json_path = global_json_path.strip().replace('\n', '') # Load global data with open(global_json_path, 'r') as f: global_data = json.load(f) # Load paths from global data network_path = global_data["interdependent_infrastructure_networks_with_different_resource_demand"] resource_constraints_path = global_data["resource_constraints_per_day"] cascading_failure_path = global_data["cascading_failure_information"] # Load data from files with open(network_path, 'r') as f: network_data = json.load(f) with open(resource_constraints_path, 'r') as f: resource_constraints = json.load(f)["resource_constraints"] with open(cascading_failure_path, 'r') as f: failed_nodes = json.load(f)["failed_nodes"] # Extract dynamic resource types resource_types = resource_constraints['repair_teams'].keys() population_size = 50 generations = 100 mutation_rate = 0.1 def fitness(order): available_resources = {rtype: resource_constraints['repair_teams'][rtype]['resource_per_day'] for rtype in resource_types} node_progress = {} node_recovery_day = {} day = 1 remaining_order = order[:] while remaining_order or node_progress: available_resources_today = available_resources.copy() for node in remaining_order[:]: node_data = next((n for n in network_data["nodes"] if n["Code"] == node), None) if not node_data: continue remaining_resources = node_progress.get(node, {rtype: node_data.get(rtype, 0) for rtype in resource_types}) allocation = {} for rtype in resource_types: allocated = min(remaining_resources[rtype], available_resources_today[rtype]) allocation[rtype] = allocated available_resources_today[rtype] -= allocated remaining_resources[rtype] -= allocated if all(v == 0 for v in remaining_resources.values()): node_recovery_day[node] = day remaining_order.remove(node) node_progress.pop(node, None) else: node_progress[node] = remaining_resources day += 1 return sum(node_recovery_day.values()) # Total cumulative recovery time def mutate(order): if random.random() < mutation_rate: idx1, idx2 = random.sample(range(len(order)), 2) order[idx1], order[idx2] = order[idx2], order[idx1] return order def crossover(parent1, parent2): idx = random.randint(0, len(parent1) - 1) child = parent1[:idx] + [n for n in parent2 if n not in parent1[:idx]] return child population = [random.sample(failed_nodes, len(failed_nodes)) for _ in range(population_size)] for _ in range(generations): population = sorted(population, key=lambda x: fitness(x)) next_generation = population[:10] while len(next_generation) < population_size: parents = random.sample(population[:20], 2) child = crossover(parents[0], parents[1]) child = mutate(child) next_generation.append(child) population = next_generation best_order = population[0] # Actual resource allocation simulation with best order node_progress = {} node_recovery_day = {} day = 1 daily_recovery_order = [] remaining_order = best_order[:] while remaining_order or node_progress: available_resources = {rtype: resource_constraints['repair_teams'][rtype]['resource_per_day'] for rtype in resource_types} resource_allocation_today = [] recovered_today = [] for node in remaining_order[:]: node_data = next((n for n in network_data["nodes"] if n["Code"] == node), None) if not node_data: continue remaining_resources = node_progress.get(node, {rtype: node_data.get(rtype, 0) for rtype in resource_types}) allocation = {} for rtype in resource_types: allocated = min(remaining_resources[rtype], available_resources[rtype]) available_resources[rtype] -= allocated allocation[rtype] = allocated remaining_resources[rtype] -= allocated resource_allocation_today.append({"node": node, "resources_used": allocation}) if all(v == 0 for v in remaining_resources.values()): node_recovery_day[node] = day recovered_today.append(node) remaining_order.remove(node) node_progress.pop(node, None) else: node_progress[node] = remaining_resources daily_recovery_order.append({ "day": day, "recovered_nodes": recovered_today, "resource_allocation": resource_allocation_today }) day += 1 total_cumulative_recovery_time = sum(node_recovery_day.values()) output = { "daily_recovery_order": daily_recovery_order, "node_recovery_day": node_recovery_day, "total_cumulative_recovery_time": total_cumulative_recovery_time } output_json_path = 'resource_allocation_considering_time.json' with open(output_json_path, 'w') as f: json.dump(output, f, indent=4) global_data["resource_allocation_considering_time"] = output_json_path with open(global_json_path, 'w') as f: json.dump(global_data, f, indent=4) return "The path to recovery time optimization result has been saved in global_data.json"