航空関連 意思決定支援ソフトウェア¶

// TCAS 意思決定支援システム
interface TCASTarget {
  transponderCode: string;
  relativePosition: RelativePosition;
  relativeVelocity: RelativeVelocity;
  altitude: number;
  verticalRate: number;
  lastUpdate: Date;
  threatLevel: ThreatLevel;
}

interface RelativePosition {
  distance: number;        // nautical miles
  bearing: number;         // degrees
  relativeAltitude: number; // feet
}

interface RelativeVelocity {
  closure_rate: number;    // knots
  vertical_closure: number; // feet per minute
}

enum ThreatLevel {
  NO_THREAT = 0,
  PROXIMITY_ADVISORY = 1,
  TRAFFIC_ADVISORY = 2,
  RESOLUTION_ADVISORY = 3
}

interface ResolutionAdvisory {
  type: RAType;
  direction: 'CLIMB' | 'DESCEND' | 'MAINTAIN';
  targetRate: number;      // feet per minute
  urgency: 'PREVENTIVE' | 'CORRECTIVE';
  strengthening?: boolean;
  reversal?: boolean;
  message: string;
}

enum RAType {
  CLIMB = 'CLIMB',
  DESCEND = 'DESCEND',
  INCREASE_CLIMB = 'INCREASE_CLIMB',
  INCREASE_DESCENT = 'INCREASE_DESCENT',
  REDUCE_CLIMB = 'REDUCE_CLIMB',
  REDUCE_DESCENT = 'REDUCE_DESCENT',
  MAINTAIN_VERTICAL_SPEED = 'MAINTAIN_VERTICAL_SPEED',
  LEVEL_OFF = 'LEVEL_OFF'
}

class TCASDecisionEngine {
  private ownshipData: OwnshipData;
  private targets: Map<string, TCASTarget> = new Map();
  private activeRA?: ResolutionAdvisory;
  private alertSubject = new Subject<TCASAlert>();

  constructor(ownshipData: OwnshipData) {
    this.ownshipData = ownshipData;
    this.setupContinuousMonitoring();
  }

  // 継続的な脅威監視
  private setupContinuousMonitoring(): void {
    interval(1000).pipe( // 1秒間隔での監視
      map(() => this.evaluateAllThreats()),
      filter(threats => threats.length > 0),
      distinctUntilChanged((prev, curr) => this.compareThreats(prev, curr))
    ).subscribe(threats => {
      this.processThreats(threats);
    });
  }

  // 脅威評価・意思決定
  evaluateAllThreats(): TCASTarget[] {
    const threats: TCASTarget[] = [];

    for (const [id, target] of this.targets) {
      const threatAssessment = this.assessThreat(target);
      if (threatAssessment.threatLevel > ThreatLevel.NO_THREAT) {
        target.threatLevel = threatAssessment.threatLevel;
        threats.push(target);
      }
    }

    return threats.sort((a, b) => b.threatLevel - a.threatLevel);
  }

  // 個別脅威の評価
  private assessThreat(target: TCASTarget): ThreatAssessment {
    // CPA（最接近点）とTCPA（最接近時間）の計算
    const cpaResult = this.calculateCPA(target);

    // 脅威判定基準
    const proximityThreshold = 6.0; // nautical miles
    const trafficAdvisoryThreshold = this.calculateTAThreshold(target.altitude);
    const resolutionAdvisoryThreshold = this.calculateRAThreshold(target.altitude);

    let threatLevel = ThreatLevel.NO_THREAT;
    let timeToThreat = Infinity;

    if (cpaResult.tcpa > 0 && cpaResult.tcpa < 120) { // 2分以内
      if (cpaResult.cpa < resolutionAdvisoryThreshold.horizontal && 
          Math.abs(cpaResult.verticalSeparation) < resolutionAdvisoryThreshold.vertical) {
        threatLevel = ThreatLevel.RESOLUTION_ADVISORY;
        timeToThreat = cpaResult.tcpa;
      } else if (cpaResult.cpa < trafficAdvisoryThreshold.horizontal && 
                 Math.abs(cpaResult.verticalSeparation) < trafficAdvisoryThreshold.vertical) {
        threatLevel = ThreatLevel.TRAFFIC_ADVISORY;
        timeToThreat = cpaResult.tcpa;
      } else if (cpaResult.cpa < proximityThreshold) {
        threatLevel = ThreatLevel.PROXIMITY_ADVISORY;
        timeToThreat = cpaResult.tcpa;
      }
    }

    return {
      threatLevel,
      timeToThreat,
      cpaResult,
      recommendedAction: this.determineRecommendedAction(threatLevel, cpaResult, target)
    };
  }

  // CPA（最接近点）計算
  private calculateCPA(target: TCASTarget): CPAResult {
    const ownVelocity = this.ownshipData.velocity;
    const targetVelocity = this.calculateTargetVelocity(target);

    // 相対位置・速度ベクトル
    const relativePosition = {
      x: target.relativePosition.distance * Math.cos(target.relativePosition.bearing * Math.PI / 180),
      y: target.relativePosition.distance * Math.sin(target.relativePosition.bearing * Math.PI / 180),
      z: target.relativePosition.relativeAltitude
    };

    const relativeVelocity = {
      x: targetVelocity.x - ownVelocity.x,
      y: targetVelocity.y - ownVelocity.y,
      z: target.verticalRate - this.ownshipData.verticalRate
    };

    // TCPA（最接近時間）計算
    const tcpa = -(relativePosition.x * relativeVelocity.x + 
                   relativePosition.y * relativeVelocity.y + 
                   relativePosition.z * relativeVelocity.z) /
                 (relativeVelocity.x * relativeVelocity.x + 
                  relativeVelocity.y * relativeVelocity.y + 
                  relativeVelocity.z * relativeVelocity.z);

    // CPA（最接近距離）計算
    const cpaPosition = {
      x: relativePosition.x + relativeVelocity.x * tcpa,
      y: relativePosition.y + relativeVelocity.y * tcpa,
      z: relativePosition.z + relativeVelocity.z * tcpa
    };

    const horizontalCPA = Math.sqrt(cpaPosition.x * cpaPosition.x + cpaPosition.y * cpaPosition.y);
    const verticalSeparation = Math.abs(cpaPosition.z);

    return {
      tcpa: tcpa,
      cpa: horizontalCPA,
      verticalSeparation: verticalSeparation,
      cpaPosition
    };
  }

  // 回避機動の決定
  generateResolutionAdvisory(threat: TCASTarget): ResolutionAdvisory {
    const cpaResult = this.calculateCPA(threat);
    const ownVerticalRate = this.ownshipData.verticalRate;
    const threatVerticalRate = threat.verticalRate;

    // 垂直方向の相対運動を分析
    const relativeVerticalVelocity = threatVerticalRate - ownVerticalRate;
    const verticalSeparation = threat.relativePosition.relativeAltitude;

    // 回避方向の決定ロジック
    let recommendedDirection: 'CLIMB' | 'DESCEND';
    let targetRate: number;
    let urgency: 'PREVENTIVE' | 'CORRECTIVE';

    // 高度差と相対速度に基づく判定
    if (verticalSeparation > 0) {
      // 脅威が上方にある場合
      if (relativeVerticalVelocity > 0) {
        // 脅威が上昇中または自機が降下中
        recommendedDirection = 'DESCEND';
        urgency = 'CORRECTIVE';
        targetRate = Math.max(-2500, ownVerticalRate - 1500); // 最低2500fpm降下
      } else {
        // 脅威が降下中または自機が上昇中
        recommendedDirection = 'CLIMB';
        urgency = 'PREVENTIVE';
        targetRate = Math.min(2500, ownVerticalRate + 1500); // 最大2500fpm上昇
      }
    } else {
      // 脅威が下方にある場合
      if (relativeVerticalVelocity < 0) {
        // 脅威が降下中または自機が上昇中
        recommendedDirection = 'CLIMB';
        urgency = 'CORRECTIVE';
        targetRate = Math.min(2500, ownVerticalRate + 1500);
      } else {
        // 脅威が上昇中または自機が降下中
        recommendedDirection = 'DESCEND';
        urgency = 'PREVENTIVE';
        targetRate = Math.max(-2500, ownVerticalRate - 1500);
      }
    }

    // 既存のRAとの整合性チェック
    if (this.activeRA) {
      const consistency = this.checkRAConsistency(recommendedDirection, this.activeRA);
      if (!consistency.consistent) {
        // 強化または反転が必要
        return this.modifyExistingRA(this.activeRA, consistency.modification);
      }
    }

    const raType = this.determineRAType(recommendedDirection, targetRate, ownVerticalRate);

    return {
      type: raType,
      direction: recommendedDirection,
      targetRate: targetRate,
      urgency: urgency,
      strengthening: false,
      reversal: false,
      message: this.generateRAMessage(raType, recommendedDirection, urgency)
    };
  }

  // RA実行・監視
  executeResolutionAdvisory(ra: ResolutionAdvisory): void {
    this.activeRA = ra;

    // パイロットへの即座の通知
    this.alertSubject.next({
      type: 'RESOLUTION_ADVISORY',
      severity: 'CRITICAL',
      message: ra.message,
      advisoryData: ra,
      timestamp: new Date()
    });

    // RA実行の監視
    this.monitorRACompliance(ra);

    // オートパイロットへの指示（統合システムの場合）
    if (this.ownshipData.autopilotEngaged) {
      this.sendAutopilotCommand(ra);
    }
  }

  // RA実行監視
  private monitorRACompliance(ra: ResolutionAdvisory): void {
    const monitoringSubscription = interval(500).pipe(
      map(() => this.checkRACompliance(ra)),
      takeUntil(this.alertSubject.pipe(filter(alert => alert.type === 'RA_CLEAR')))
    ).subscribe(compliance => {
      if (!compliance.compliant) {
        this.enhanceRA(ra, compliance.reason);
      } else if (compliance.threatCleared) {
        this.clearResolutionAdvisory();
      }
    });
  }

  // RA解除判定
  private clearResolutionAdvisory(): void {
    if (this.activeRA) {
      this.alertSubject.next({
        type: 'RA_CLEAR',
        severity: 'INFO',
        message: 'CLEAR OF CONFLICT',
        timestamp: new Date()
      });

      this.activeRA = undefined;
    }
  }

  // TCASアラートのストリーム
  getAlertStream(): Observable<TCASAlert> {
    return this.alertSubject.asObservable();
  }

  // システム統計情報
  getSystemStatistics(): TCASStatistics {
    return {
      activeTargets: this.targets.size,
      threatsDetected: Array.from(this.targets.values()).filter(t => t.threatLevel > 0).length,
      activeRA: this.activeRA !== undefined,
      systemStatus: this.getSystemStatus(),
      lastUpdate: new Date()
    };
  }
}

interface CPAResult {
  tcpa: number;           // Time to CPA (seconds)
  cpa: number;            // Closest Point of Approach (nautical miles)
  verticalSeparation: number; // feet
  cpaPosition: { x: number; y: number; z: number };
}

interface ThreatAssessment {
  threatLevel: ThreatLevel;
  timeToThreat: number;
  cpaResult: CPAResult;
  recommendedAction: string;
}

interface TCASAlert {
  type: 'PROXIMITY_ADVISORY' | 'TRAFFIC_ADVISORY' | 'RESOLUTION_ADVISORY' | 'RA_CLEAR';
  severity: 'INFO' | 'WARNING' | 'CRITICAL';
  message: string;
  advisoryData?: ResolutionAdvisory;
  timestamp: Date;
}

interface TCASStatistics {
  activeTargets: number;
  threatsDetected: number;
  activeRA: boolean;
  systemStatus: SystemStatus;
  lastUpdate: Date;
}

// フライト計画最適化システム
interface FlightPlan {
  flightId: string;
  origin: Airport;
  destination: Airport;
  aircraft: AircraftType;
  departureTime: Date;
  route: Waypoint[];
  altitude: number[];
  speed: number[];
  estimatedFuelConsumption: number;
  estimatedFlightTime: number;
  estimatedCost: number;
  weatherRisk: number;
  trafficComplexity: number;
}

interface OptimizationObjectives {
  minimizeFuelConsumption: { weight: number; priority: number };
  minimizeFlightTime: { weight: number; priority: number };
  minimizeCost: { weight: number; priority: number };
  minimizeWeatherRisk: { weight: number; priority: number };
  maximizeSafety: { weight: number; priority: number };
  minimizeEmissions: { weight: number; priority: number };
}

interface OptimizationConstraints {
  maxFlightTime?: number;
  maxFuelBudget?: number;
  maxCostBudget?: number;
  mandatoryWaypoints?: Waypoint[];
  avoidanceZones?: AvoidanceZone[];
  altitudeRestrictions?: AltitudeRestriction[];
  timeConstraints?: TimeConstraint[];
  weatherLimitations?: WeatherLimitation[];
}

class FlightPlanOptimizer {
  private geneticAlgorithm: GeneticAlgorithmEngine;
  private simulatedAnnealing: SimulatedAnnealingEngine;
  private weatherService: WeatherService;
  private trafficService: TrafficService;
  private performanceModels: Map<string, AircraftPerformanceModel>;

  constructor() {
    this.geneticAlgorithm = new GeneticAlgorithmEngine();
    this.simulatedAnnealing = new SimulatedAnnealingEngine();
    this.weatherService = new WeatherService();
    this.trafficService = new TrafficService();
    this.performanceModels = new Map();
    this.initializePerformanceModels();
  }

  // メインの最適化プロセス
  async optimizeFlightPlan(
    basicPlan: BasicFlightPlan,
    objectives: OptimizationObjectives,
    constraints: OptimizationConstraints
  ): Promise<OptimizedFlightPlan> {
    // 初期解の生成
    const initialSolutions = await this.generateInitialSolutions(basicPlan, constraints);

    // 並列最適化実行
    const optimizationResults = await Promise.all([
      this.runGeneticOptimization(initialSolutions, objectives, constraints),
      this.runSimulatedAnnealingOptimization(initialSolutions[0], objectives, constraints),
      this.runDeterministicOptimization(basicPlan, objectives, constraints)
    ]);

    // 最良解の選択
    const bestSolution = this.selectBestSolution(optimizationResults, objectives);

    // 解の検証・改良
    const validatedSolution = await this.validateAndRefine(bestSolution, constraints);

    // パフォーマンス予測
    const performancePrediction = await this.predictPerformance(validatedSolution);

    return {
      optimizedPlan: validatedSolution,
      performancePrediction,
      optimizationMetrics: this.calculateOptimizationMetrics(basicPlan, validatedSolution),
      alternativePlans: optimizationResults.slice(1, 4), // 上位代替案
      confidence: this.calculateConfidence(validatedSolution),
      sensitivityAnalysis: await this.performSensitivityAnalysis(validatedSolution)
    };
  }

  // 遺伝的アルゴリズムによる最適化
  private async runGeneticOptimization(
    initialSolutions: FlightPlan[],
    objectives: OptimizationObjectives,
    constraints: OptimizationConstraints
  ): Promise<FlightPlan> {
    const gaConfig = {
      populationSize: 100,
      generations: 200,
      crossoverRate: 0.8,
      mutationRate: 0.02,
      eliteSize: 10,
      convergenceThreshold: 0.001
    };

    return new Promise((resolve) => {
      const population = this.initializePopulation(initialSolutions, gaConfig.populationSize);
      let bestSolution = population[0];
      let bestFitness = -Infinity;
      let convergenceCounter = 0;

      for (let generation = 0; generation < gaConfig.generations; generation++) {
        // 評価
        const evaluatedPopulation = population.map(individual => ({
          plan: individual,
          fitness: this.evaluateFitness(individual, objectives)
        }));

        // 選択
        evaluatedPopulation.sort((a, b) => b.fitness - a.fitness);
        const currentBest = evaluatedPopulation[0];

        if (currentBest.fitness > bestFitness) {
          bestFitness = currentBest.fitness;
          bestSolution = currentBest.plan;
          convergenceCounter = 0;
        } else {
          convergenceCounter++;
        }

        // 収束判定
        if (convergenceCounter > 20) break;

        // 次世代生成
        const nextGeneration: FlightPlan[] = [];

        // エリート保存
        for (let i = 0; i < gaConfig.eliteSize; i++) {
          nextGeneration.push(evaluatedPopulation[i].plan);
        }

        // 交叉・突然変異
        while (nextGeneration.length < gaConfig.populationSize) {
          const parent1 = this.selectParent(evaluatedPopulation);
          const parent2 = this.selectParent(evaluatedPopulation);

          if (Math.random() < gaConfig.crossoverRate) {
            const offspring = this.crossover(parent1, parent2);
            if (Math.random() < gaConfig.mutationRate) {
              this.mutate(offspring, constraints);
            }
            nextGeneration.push(offspring);
          }
        }

        population.splice(0, population.length, ...nextGeneration);
      }

      resolve(bestSolution);
    });
  }

  // 適応度評価関数
  private evaluateFitness(plan: FlightPlan, objectives: OptimizationObjectives): number {
    const metrics = this.calculatePlanMetrics(plan);
    let totalFitness = 0;

    // 各目標の正規化された値を計算
    const normalizedFuel = 1 - (metrics.fuelConsumption / metrics.maxExpectedFuel);
    const normalizedTime = 1 - (metrics.flightTime / metrics.maxExpectedTime);
    const normalizedCost = 1 - (metrics.totalCost / metrics.maxExpectedCost);
    const normalizedSafety = metrics.safetyScore; // 0-1 で既に正規化済み
    const normalizedWeatherRisk = 1 - metrics.weatherRisk; // 0-1 で既に正規化済み

    // 重み付き合計
    totalFitness += normalizedFuel * objectives.minimizeFuelConsumption.weight;
    totalFitness += normalizedTime * objectives.minimizeFlightTime.weight;
    totalFitness += normalizedCost * objectives.minimizeCost.weight;
    totalFitness += normalizedSafety * objectives.maximizeSafety.weight;
    totalFitness += normalizedWeatherRisk * objectives.minimizeWeatherRisk.weight;

    // 制約違反ペナルティ
    const constraintViolations = this.checkConstraintViolations(plan);
    const penaltyFactor = Math.exp(-constraintViolations.totalViolationScore);

    return totalFitness * penaltyFactor;
  }

  // リアルタイム再最適化
  async reoptimizeInFlight(
    currentPlan: FlightPlan,
    currentPosition: Position,
    updatedConditions: UpdatedConditions
  ): Promise<ReoptimizationResult> {
    // 現在位置からの残り航路を抽出
    const remainingRoute = this.extractRemainingRoute(currentPlan, currentPosition);

    // 更新された条件を反映
    const updatedObjectives = this.updateObjectives(currentPlan, updatedConditions);
    const updatedConstraints = this.updateConstraints(currentPlan, updatedConditions);

    // 高速再最適化（限定的探索）
    const reoptimizedRoute = await this.fastReoptimization(
      remainingRoute,
      updatedObjectives,
      updatedConstraints
    );

    // 変更の妥当性評価
    const changeAssessment = this.assessRouteChange(currentPlan, reoptimizedRoute);

    if (changeAssessment.beneficial && changeAssessment.feasible) {
      return {
        recommendedChange: true,
        newRoute: reoptimizedRoute,
        benefits: changeAssessment.benefits,
        implementationSteps: this.generateImplementationSteps(reoptimizedRoute),
        urgency: changeAssessment.urgency
      };
    } else {
      return {
        recommendedChange: false,
        reason: changeAssessment.reason,
        alternativeOptions: changeAssessment.alternatives
      };
    }
  }

  // 燃料最適化特化
  optimizeFuelEfficiency(plan: FlightPlan): Promise<FuelOptimizationResult> {
    return new Promise(async (resolve) => {
      // 高度プロファイル最適化
      const optimalAltitudes = await this.optimizeAltitudeProfile(plan);

      // 速度プロファイル最適化
      const optimalSpeeds = await this.optimizeSpeedProfile(plan, optimalAltitudes);

      // ルート微調整
      const optimalRoute = await this.optimizeRouteForFuel(plan, optimalAltitudes, optimalSpeeds);

      // 燃料節約量計算
      const fuelSavings = this.calculateFuelSavings(plan, {
        altitudes: optimalAltitudes,
        speeds: optimalSpeeds,
        route: optimalRoute
      });

      resolve({
        optimizedAltitudes: optimalAltitudes,
        optimizedSpeeds: optimalSpeeds,
        optimizedRoute: optimalRoute,
        fuelSavings: fuelSavings,
        implementationCost: this.calculateImplementationCost(plan, optimalRoute),
        environmentalBenefit: this.calculateEnvironmentalBenefit(fuelSavings)
      });
    });
  }

  // 機械学習による予測改善
  async improveWithMachineLearning(
    historicalData: FlightPlanHistory[],
    actualPerformance: ActualPerformanceData[]
  ): Promise<ModelImprovement> {
    // フライト性能予測モデルの学習
    const performanceModel = await this.trainPerformanceModel(historicalData, actualPerformance);

    // 気象影響予測モデルの学習
    const weatherImpactModel = await this.trainWeatherImpactModel(historicalData, actualPerformance);

    // 最適化アルゴリズムのパラメータ調整
    const optimizedParameters = await this.optimizeAlgorithmParameters(historicalData);

    // モデル性能評価
    const modelValidation = await this.validateModels(performanceModel, weatherImpactModel);

    return {
      performanceModel,
      weatherImpactModel,
      optimizedParameters,
      validation: modelValidation,
      expectedImprovement: this.calculateExpectedImprovement(modelValidation),
      deploymentRecommendation: modelValidation.accuracy > 0.85
    };
  }

  // 運航実績フィードバック
  processFeedback(
    plannedRoute: FlightPlan,
    actualFlight: ActualFlightData
  ): Observable<OptimizationInsight> {
    return new Subject<OptimizationInsight>().pipe(
      map(() => {
        // 予測と実績の比較
        const deviationAnalysis = this.analyzeDeviations(plannedRoute, actualFlight);

        // 改善ポイントの特定
        const improvementAreas = this.identifyImprovementAreas(deviationAnalysis);

        // 次回最適化への反映事項
        const optimizationAdjustments = this.generateOptimizationAdjustments(improvementAreas);

        return {
          flightId: plannedRoute.flightId,
          deviationAnalysis,
          improvementAreas,
          optimizationAdjustments,
          modelUpdateRequired: deviationAnalysis.significantDeviation,
          learningPriority: this.calculateLearningPriority(deviationAnalysis)
        };
      })
    );
  }
}

interface OptimizedFlightPlan {
  optimizedPlan: FlightPlan;
  performancePrediction: PerformancePrediction;
  optimizationMetrics: OptimizationMetrics;
  alternativePlans: FlightPlan[];
  confidence: number;
  sensitivityAnalysis: SensitivityAnalysis;
}

interface ReoptimizationResult {
  recommendedChange: boolean;
  newRoute?: FlightPlan;
  benefits?: OptimizationBenefits;
  implementationSteps?: ImplementationStep[];
  urgency?: 'LOW' | 'MEDIUM' | 'HIGH' | 'CRITICAL';
  reason?: string;
  alternativeOptions?: FlightPlan[];
}

interface FuelOptimizationResult {
  optimizedAltitudes: number[];
  optimizedSpeeds: number[];
  optimizedRoute: Waypoint[];
  fuelSavings: FuelSavings;
  implementationCost: ImplementationCost;
  environmentalBenefit: EnvironmentalBenefit;
}

interface OptimizationInsight {
  flightId: string;
  deviationAnalysis: DeviationAnalysis;
  improvementAreas: ImprovementArea[];
  optimizationAdjustments: OptimizationAdjustment[];
  modelUpdateRequired: boolean;
  learningPriority: number;
}

// 統合運航管理システム
interface OperationalResource {
  id: string;
  type: ResourceType;
  availability: AvailabilityWindow[];
  capabilities: ResourceCapability[];
  location: Airport;
  status: ResourceStatus;
  constraints: ResourceConstraint[];
}

enum ResourceType {
  AIRCRAFT = 'aircraft',
  CREW = 'crew',
  GATE = 'gate',
  SLOT = 'slot',
  GROUND_EQUIPMENT = 'ground_equipment'
}

interface Flight {
  flightNumber: string;
  origin: Airport;
  destination: Airport;
  scheduledDeparture: Date;
  scheduledArrival: Date;
  aircraftType: string;
  passengerCount: number;
  requiredResources: ResourceRequirement[];
  status: FlightStatus;
  delays: Delay[];
  alternatives: AlternativeFlight[];
}

interface OperationalDisruption {
  id: string;
  type: DisruptionType;
  severity: DisruptionSeverity;
  affectedResources: string[];
  affectedFlights: string[];
  startTime: Date;
  estimatedEndTime?: Date;
  cause: string;
  impact: DisruptionImpact;
}

enum DisruptionType {
  WEATHER = 'weather',
  TECHNICAL = 'technical',
  CREW_SHORTAGE = 'crew_shortage',
  AIRPORT_CLOSURE = 'airport_closure',
  AIR_TRAFFIC_CONTROL = 'atc',
  STRIKE = 'strike'
}

class IntegratedOperationsCenter {
  private resourceManager: ResourceManager;
  private scheduleOptimizer: ScheduleOptimizer;
  private disruptionManager: DisruptionManager;
  private crewManager: CrewManager;
  private fleetManager: FleetManager;
  private alertSystem: OperationalAlertSystem;

  constructor() {
    this.resourceManager = new ResourceManager();
    this.scheduleOptimizer = new ScheduleOptimizer();
    this.disruptionManager = new DisruptionManager();
    this.crewManager = new CrewManager();
    this.fleetManager = new FleetManager();
    this.alertSystem = new OperationalAlertSystem();
    this.setupRealTimeMonitoring();
  }

  // リアルタイム運航監視
  private setupRealTimeMonitoring(): void {
    // フライト状況の監視
    interval(60000).pipe( // 1分間隔
      mergeMap(() => this.monitorAllFlights()),
      filter(issues => issues.length > 0)
    ).subscribe(issues => {
      this.handleOperationalIssues(issues);
    });

    // リソース状況の監視
    interval(30000).pipe( // 30秒間隔
      mergeMap(() => this.monitorAllResources()),
      filter(alerts => alerts.length > 0)
    ).subscribe(alerts => {
      this.processResourceAlerts(alerts);
    });

    // 予測的問題検知
    interval(300000).pipe( // 5分間隔
      mergeMap(() => this.predictOperationalProblems())
    ).subscribe(predictions => {
      this.handlePredictiveAlerts(predictions);
    });
  }

  // 運航スケジュール最適化
  async optimizeOperationalSchedule(
    timeWindow: TimeWindow,
    constraints: OperationalConstraints
  ): Promise<OptimizedSchedule> {
    // 現在のスケジュール状況を取得
    const currentSchedule = await this.getCurrentSchedule(timeWindow);

    // 利用可能リソースの分析
    const resourceAvailability = await this.analyzeResourceAvailability(timeWindow);

    // 制約条件の処理
    const processedConstraints = this.processConstraints(constraints, resourceAvailability);

    // 最適化問題として定式化
    const optimizationProblem = this.formulateOptimizationProblem(
      currentSchedule,
      resourceAvailability,
      processedConstraints
    );

    // 最適化実行
    const optimizationResult = await this.solveOptimizationProblem(optimizationProblem);

    // 実行可能性検証
    const feasibilityCheck = await this.validateScheduleFeasibility(optimizationResult);

    if (feasibilityCheck.feasible) {
      return {
        optimizedSchedule: optimizationResult.schedule,
        resourceAllocation: optimizationResult.allocation,
        improvementMetrics: this.calculateImprovements(currentSchedule, optimizationResult.schedule),
        implementationPlan: this.createImplementationPlan(optimizationResult),
        riskAssessment: this.assessImplementationRisks(optimizationResult)
      };
    } else {
      // 実行不可能な場合の代替案生成
      return this.generateFallbackSchedule(currentSchedule, feasibilityCheck.issues);
    }
  }

  // 運航障害対応
  async handleOperationalDisruption(disruption: OperationalDisruption): Promise<DisruptionResponse> {
    // 影響分析
    const impactAnalysis = await this.analyzeDisruptionImpact(disruption);

    // 対応策の生成
    const responseOptions = await this.generateResponseOptions(disruption, impactAnalysis);

    // 最適な対応策の選択
    const optimalResponse = this.selectOptimalResponse(responseOptions, impactAnalysis);

    // 実行計画の作成
    const executionPlan = await this.createExecutionPlan(optimalResponse);

    // ステークホルダーへの通知
    const notifications = this.generateNotifications(disruption, optimalResponse);

    // 実行開始
    const execution = await this.executeResponse(executionPlan);

    return {
      disruption,
      impactAnalysis,
      selectedResponse: optimalResponse,
      executionPlan,
      notifications,
      execution,
      followUpActions: this.defineFollowUpActions(disruption, optimalResponse)
    };
  }

  // 乗務員スケジューling最適化
  optimizeCrewScheduling(
    flights: Flight[],
    crewAvailability: CrewAvailability[],
    regulations: CrewRegulations
  ): Promise<OptimizedCrewSchedule> {
    return new Promise(async (resolve) => {
      // 乗務員要件の分析
      const crewRequirements = this.analyzeCrewRequirements(flights);

      // 法的制約の処理
      const legalConstraints = this.processCrewRegulations(regulations);

      // 最適配置アルゴリズム
      const assignment = await this.runCrewAssignmentOptimization({
        requirements: crewRequirements,
        availability: crewAvailability,
        constraints: legalConstraints
      });

      // 配置の検証
      const validation = this.validateCrewAssignment(assignment, legalConstraints);

      if (validation.valid) {
        resolve({
          assignment: assignment.schedule,
          coverage: assignment.coverage,
          efficiency: this.calculateCrewEfficiency(assignment),
          complianceStatus: validation.compliance,
          costAnalysis: this.calculateCrewCosts(assignment),
          riskFactors: this.identifyCrewRisks(assignment)
        });
      } else {
        // 制約違反の解決
        const adjustedAssignment = await this.resolveCrewConstraintViolations(assignment, validation);
        resolve(adjustedAssignment);
      }
    });
  }

  // 航空機配置最適化
  async optimizeAircraftAssignment(): Promise<FleetOptimizationResult> {
    // 現在の航空機状況
    const fleetStatus = await this.fleetManager.getCurrentFleetStatus();

    // フライトスケジュール要件
    const flightRequirements = await this.getFlightRequirements();

    // メンテナンス計画
    const maintenanceSchedule = await this.fleetManager.getMaintenanceSchedule();

    // 最適配置計算
    const optimization = await this.runFleetOptimization({
      fleetStatus,
      flightRequirements,
      maintenanceSchedule,
      operationalConstraints: this.getFleetConstraints()
    });

    // 配置変更の実行可能性評価
    const feasibility = this.evaluateFleetChangeFeasibility(optimization);

    return {
      optimalAssignment: optimization.assignment,
      utilizationImprovement: optimization.utilizationGain,
      costSavings: optimization.costReduction,
      feasibility,
      implementationTimeline: this.createFleetChangeTimeline(optimization),
      riskMitigation: this.planFleetChangeRisks(optimization)
    };
  }

  // 予測的運航分析
  async predictOperationalPerformance(
    forecastHorizon: number // hours
  ): Promise<OperationalForecast> {
    // 気象予測の取得
    const weatherForecast = await this.getWeatherForecast(forecastHorizon);

    // トラフィック予測
    const trafficForecast = await this.getTrafficForecast(forecastHorizon);

    // リソース状況予測
    const resourceForecast = await this.forecastResourceAvailability(forecastHorizon);

    // 運航パフォーマンス予測
    const performancePrediction = await this.predictPerformanceMetrics({
      weather: weatherForecast,
      traffic: trafficForecast,
      resources: resourceForecast
    });

    // リスク要因の特定
    const riskFactors = this.identifyForecastRisks(performancePrediction);

    // 予防的措置の提案
    const preventiveMeasures = this.suggestPreventiveMeasures(riskFactors);

    return {
      forecastPeriod: { start: new Date(), hours: forecastHorizon },
      performancePrediction,
      riskFactors,
      preventiveMeasures,
      confidence: this.calculateForecastConfidence(performancePrediction),
      recommendations: this.generateOperationalRecommendations(performancePrediction, riskFactors)
    };
  }

  // KPI監視・報告
  generateOperationalKPIs(): Observable<OperationalKPIs> {
    return interval(3600000).pipe( // 1時間間隔
      map(() => {
        const currentTime = new Date();
        const last24Hours = new Date(currentTime.getTime() - 24 * 60 * 60 * 1000);

        return {
          timestamp: currentTime,
          onTimePerformance: this.calculateOnTimePerformance(last24Hours, currentTime),
          utilizationRates: this.calculateUtilizationRates(last24Hours, currentTime),
          costEfficiency: this.calculateCostEfficiency(last24Hours, currentTime),
          customerSatisfaction: this.estimateCustomerSatisfaction(last24Hours, currentTime),
          resourceEfficiency: this.calculateResourceEfficiency(last24Hours, currentTime),
          environmentalImpact: this.calculateEnvironmentalImpact(last24Hours, currentTime),
          safetyMetrics: this.calculateSafetyMetrics(last24Hours, currentTime),
          profitability: this.calculateProfitability(last24Hours, currentTime)
        };
      })
    );
  }

  // 意思決定支援ダッシュボード
  getDecisionSupportData(): Observable<DecisionSupportData> {
    return combineLatest([
      this.getCurrentOperationalStatus(),
      this.getActiveDisruptions(),
      this.getPendingDecisions(),
      this.getResourceConstraints(),
      this.getPerformanceAlerts()
    ]).pipe(
      map(([status, disruptions, decisions, constraints, alerts]) => ({
        operationalStatus: status,
        activeDisruptions: disruptions,
        pendingDecisions: decisions,
        resourceConstraints: constraints,
        performanceAlerts: alerts,
        decisionRecommendations: this.generateDecisionRecommendations({
          status, disruptions, decisions, constraints, alerts
        }),
        priorityActions: this.identifyPriorityActions({
          status, disruptions, decisions, constraints, alerts
        })
      }))
    );
  }
}

interface OptimizedSchedule {
  optimizedSchedule: Flight[];
  resourceAllocation: ResourceAllocation[];
  improvementMetrics: ImprovementMetrics;
  implementationPlan: ImplementationPlan;
  riskAssessment: RiskAssessment;
}

interface DisruptionResponse {
  disruption: OperationalDisruption;
  impactAnalysis: ImpactAnalysis;
  selectedResponse: ResponseOption;
  executionPlan: ExecutionPlan;
  notifications: Notification[];
  execution: ExecutionStatus;
  followUpActions: FollowUpAction[];
}

interface OptimizedCrewSchedule {
  assignment: CrewAssignment[];
  coverage: CoverageAnalysis;
  efficiency: EfficiencyMetrics;
  complianceStatus: ComplianceStatus;
  costAnalysis: CostAnalysis;
  riskFactors: RiskFactor[];
}

interface OperationalForecast {
  forecastPeriod: { start: Date; hours: number };
  performancePrediction: PerformancePrediction;
  riskFactors: RiskFactor[];
  preventiveMeasures: PreventiveMeasure[];
  confidence: number;
  recommendations: OperationalRecommendation[];
}

interface OperationalKPIs {
  timestamp: Date;
  onTimePerformance: number;
  utilizationRates: UtilizationRates;
  costEfficiency: CostEfficiencyMetrics;
  customerSatisfaction: number;
  resourceEfficiency: ResourceEfficiencyMetrics;
  environmentalImpact: EnvironmentalMetrics;
  safetyMetrics: SafetyMetrics;
  profitability: ProfitabilityMetrics;
}

// 気象意思決定支援システム
interface WeatherCondition {
  type: WeatherType;
  severity: SeverityLevel;
  location: GeographicArea;
  timeRange: TimeRange;
  movement: WeatherMovement;
  confidence: number;
  impact: WeatherImpact;
}

enum WeatherType {
  THUNDERSTORM = 'thunderstorm',
  TURBULENCE = 'turbulence',
  ICING = 'icing',
  LOW_VISIBILITY = 'low_visibility',
  STRONG_WINDS = 'strong_winds',
  VOLCANIC_ASH = 'volcanic_ash',
  SANDSTORM = 'sandstorm'
}

interface WeatherDecision {
  decisionType: WeatherDecisionType;
  recommendation: string;
  rationale: string;
  alternatives: WeatherAlternative[];
  confidence: number;
  timeConstraint: number; // minutes until decision must be made
  cost: DecisionCost;
  risk: RiskAssessment;
}

enum WeatherDecisionType {
  CONTINUE_AS_PLANNED = 'continue',
  ROUTE_DEVIATION = 'deviation',
  ALTITUDE_CHANGE = 'altitude_change',
  DELAY_DEPARTURE = 'delay',
  DIVERT_TO_ALTERNATE = 'divert',
  RETURN_TO_ORIGIN = 'return'
}

class WeatherDecisionSupportSystem {
  private weatherAnalyzer: WeatherAnalyzer;
  private flightTracker: FlightTracker;
  private routeOptimizer: RouteOptimizer;
  private riskAssessor: WeatherRiskAssessor;
  private decisionEngine: DecisionEngine;

  constructor() {
    this.weatherAnalyzer = new WeatherAnalyzer();
    this.flightTracker = new FlightTracker();
    this.routeOptimizer = new RouteOptimizer();
    this.riskAssessor = new WeatherRiskAssessor();
    this.decisionEngine = new DecisionEngine();
    this.setupWeatherMonitoring();
  }

  // 気象監視・意思決定の自動化
  private setupWeatherMonitoring(): void {
    // 気象データの継続監視
    interval(300000).pipe( // 5分間隔
      mergeMap(() => this.getLatestWeatherData()),
      distinctUntilChanged((prev, curr) => this.compareWeatherConditions(prev, curr)),
      filter(conditions => this.hasSignificantWeatherChanges(conditions))
    ).subscribe(conditions => {
      this.processWeatherUpdate(conditions);
    });

    // 進行中フライトへの影響評価
    interval(180000).pipe( // 3分間隔
      mergeMap(() => this.evaluateActiveFlights())
    ).subscribe(assessments => {
      assessments.forEach(assessment => {
        if (assessment.requiresDecision) {
          this.generateWeatherDecision(assessment);
        }
      });
    });
  }

  // 天候に基づく運航判断
  async makeWeatherDecision(
    flight: FlightInfo,
    weatherConditions: WeatherCondition[],
    operationalConstraints: OperationalConstraints
  ): Promise<WeatherDecision> {
    // 気象影響分析
    const weatherImpact = await this.analyzeWeatherImpact(flight, weatherConditions);

    // リスク評価
    const riskAssessment = await this.assessWeatherRisk(flight, weatherImpact);

    // 実行可能な選択肢の生成
    const alternatives = await this.generateWeatherAlternatives(flight, weatherConditions, operationalConstraints);

    // 各選択肢の評価
    const evaluatedAlternatives = await this.evaluateAlternatives(alternatives, riskAssessment);

    // 最適な決定の選択
    const optimalDecision = this.selectOptimalDecision(evaluatedAlternatives);

    // 決定の妥当性検証
    const validation = await this.validateDecision(optimalDecision, flight, operationalConstraints);

    if (validation.valid) {
      return optimalDecision;
    } else {
      // 修正された決定を生成
      return this.generateModifiedDecision(optimalDecision, validation.issues);
    }
  }

  // 雷雨回避システム
  async generateThunderstormAvoidance(
    flight: FlightInfo,
    thunderstorms: ThunderstormData[]
  ): Promise<AvoidanceRoute> {
    // 雷雨セルの将来位置予測
    const predictedStorms = await this.predictThunderstormMovement(thunderstorms, flight.estimatedRoute);

    // 回避ルートの候補生成
    const avoidanceRoutes = this.generateAvoidanceRoutes(flight.currentRoute, predictedStorms);

    // 各ルートの評価
    const routeEvaluations = await Promise.all(
      avoidanceRoutes.map(route => this.evaluateAvoidanceRoute(route, flight, predictedStorms))
    );

    // 最適回避ルートの選択
    const optimalRoute = routeEvaluations.reduce((best, current) => 
      current.score > best.score ? current : best
    );

    // 実行可能性と安全性の最終チェック
    const safetyCheck = await this.validateAvoidanceRoute(optimalRoute.route, flight);

    return {
      originalRoute: flight.currentRoute,
      avoidanceRoute: optimalRoute.route,
      avoidanceReason: this.generateAvoidanceReason(predictedStorms),
      fuelImpact: optimalRoute.fuelImpact,
      timeImpact: optimalRoute.timeImpact,
      safetyMargin: safetyCheck.safetyMargin,
      alternativeRoutes: routeEvaluations.slice(1, 3).map(r => r.route),
      updateRequired: this.calculateRouteUpdateTiming(optimalRoute.route, flight)
    };
  }

  // 乱気流回避・対処システム
  async handleTurbulenceDecision(
    flight: FlightInfo,
    turbulenceData: TurbulenceData
  ): Promise<TurbulenceResponse> {
    // 乱気流の強度・範囲分析
    const turbulenceAnalysis = this.analyzeTurbulenceIntensity(turbulenceData);

    // 航空機の耐性評価
    const aircraftCapability = this.evaluateAircraftTurbulenceCapability(flight.aircraftType);

    // 対処法の決定
    let response: TurbulenceResponse;

    if (turbulenceAnalysis.severity === 'LIGHT' && aircraftCapability.canHandle) {
      // 軽微な乱気流：継続飛行
      response = {
        action: 'CONTINUE_WITH_CAUTION',
        recommendedActions: [
          'シートベルト着用サイン点灯',
          '客室乗務員への注意喚起',
          '速度調整（推奨速度への変更）'
        ],
        speedAdjustment: this.calculateTurbulenceSpeed(flight.aircraftType, turbulenceAnalysis),
        monitoring: 'CONTINUOUS'
      };
    } else if (turbulenceAnalysis.severity === 'MODERATE') {
      // 中程度の乱気流：高度変更検討
      const altitudeOptions = await this.findSmoothAltitudes(flight.currentPosition, turbulenceData);

      if (altitudeOptions.length > 0) {
        response = {
          action: 'CHANGE_ALTITUDE',
          recommendedAltitude: altitudeOptions[0].altitude,
          rationale: `より滑らかな飛行のため${altitudeOptions[0].altitude}ftへの変更を推奨`,
          fuelImpact: altitudeOptions[0].fuelImpact,
          timeImpact: altitudeOptions[0].timeImpact,
          alternativeAltitudes: altitudeOptions.slice(1)
        };
      } else {
        response = {
          action: 'CONTINUE_WITH_ENHANCED_PRECAUTIONS',
          recommendedActions: [
            '速度を乱気流貫通速度に調整',
            '客室サービス中断',
            '頻繁な状況評価'
          ]
        };
      }
    } else {
      // 強い乱気流：迂回必須
      const deviationRoute = await this.generateTurbulenceDeviation(flight, turbulenceData);
      response = {
        action: 'DEVIATE_ROUTE',
        deviationRoute: deviationRoute.route,
        deviationReason: '強い乱気流回避のため',
        estimatedDelay: deviationRoute.delay,
        fuelPenalty: deviationRoute.fuelCost,
        safetyBenefit: deviationRoute.safetyImprovement
      };
    }

    return response;
  }

  // 着氷リスク管理
  async manageIcingRisk(
    flight: FlightInfo,
    icingConditions: IcingConditions
  ): Promise<IcingDecision> {
    // 着氷の程度と危険性評価
    const icingRisk = this.assessIcingRisk(icingConditions, flight.aircraftType);

    // 防氷システムの能力評価
    const deicingCapability = this.evaluateDeicingCapability(flight.aircraftType, icingConditions);

    let decision: IcingDecision;

    if (icingRisk.severity === 'TRACE' && deicingCapability.adequate) {
      // 軽微な着氷：システム作動で継続可能
      decision = {
        action: 'ACTIVATE_DEICING_SYSTEMS',
        systemsToActivate: deicingCapability.recommendedSystems,
        monitoringFrequency: 'EVERY_2_MINUTES',
        fuelImpact: deicingCapability.fuelConsumption,
        continueThreshold: icingRisk.maxAcceptableAccumulation
      };
    } else if (icingRisk.severity === 'LIGHT_TO_MODERATE') {
      // 中程度の着氷：高度変更または迂回
      const escapeOptions = await this.findIcingEscapeRoutes(flight, icingConditions);

      decision = {
        action: 'ESCAPE_ICING_CONDITIONS',
        escapeOptions: escapeOptions,
        recommendedOption: escapeOptions[0],
        urgency: 'MODERATE',
        timeConstraint: this.calculateIcingTimeConstraint(icingRisk)
      };
    } else {
      // 重度の着氷：即座の回避必須
      const emergencyEscape = await this.generateEmergencyIcingEscape(flight, icingConditions);

      decision = {
        action: 'IMMEDIATE_ESCAPE',
        emergencyRoute: emergencyEscape.route,
        urgency: 'HIGH',
        rationale: '重度着氷条件のため即座の回避が必要',
        nearestAlternateAirport: emergencyEscape.nearestAlternate,
        declareEmergency: icingRisk.severity === 'SEVERE'
      };
    }

    return decision;
  }

  // 総合的な気象意思決定
  async comprehensiveWeatherDecision(
    flight: FlightInfo,
    allWeatherData: ComprehensiveWeatherData
  ): Promise<ComprehensiveWeatherDecision> {
    // 複数の気象要因の統合評価
    const integratedAssessment = await this.integrateWeatherFactors(allWeatherData, flight);

    // 優先度付きリスク評価
    const prioritizedRisks = this.prioritizeWeatherRisks(integratedAssessment);

    // 包括的な対処戦略
    const strategy = await this.developComprehensiveStrategy(prioritizedRisks, flight);

    // 段階的実行計画
    const executionPlan = this.createWeatherExecutionPlan(strategy);

    // コンティンジェンシープラン
    const contingencyPlans = await this.developContingencyPlans(strategy, flight);

    return {
      primaryStrategy: strategy,
      executionPlan: executionPlan,
      contingencyPlans: contingencyPlans,
      riskMitigation: this.planRiskMitigation(prioritizedRisks),
      decisionConfidence: this.calculateDecisionConfidence(integratedAssessment),
      reviewPoints: this.defineReviewPoints(strategy, flight),
      communicationPlan: this.createCommunicationPlan(strategy, flight)
    };
  }

  // 気象学習・改善システム
  improveWeatherDecisions(
    historicalDecisions: WeatherDecisionHistory[],
    outcomes: DecisionOutcome[]
  ): Observable<WeatherDecisionImprovement> {
    return new Subject<WeatherDecisionImprovement>().pipe(
      map(() => {
        // 意思決定の精度分析
        const accuracyAnalysis = this.analyzeDecisionAccuracy(historicalDecisions, outcomes);

        // パターン学習
        const learnedPatterns = this.extractDecisionPatterns(historicalDecisions, outcomes);

        // モデル改善提案
        const modelImprovements = this.suggestModelImprovements(accuracyAnalysis, learnedPatterns);

        // 閾値調整
        const thresholdOptimization = this.optimizeDecisionThresholds(historicalDecisions, outcomes);

        return {
          accuracyMetrics: accuracyAnalysis,
          learnedPatterns: learnedPatterns,
          modelImprovements: modelImprovements,
          optimizedThresholds: thresholdOptimization,
          implementationRecommendations: this.generateImplementationRecommendations(modelImprovements)
        };
      })
    );
  }
}

interface AvoidanceRoute {
  originalRoute: FlightRoute;
  avoidanceRoute: FlightRoute;
  avoidanceReason: string;
  fuelImpact: FuelImpact;
  timeImpact: TimeImpact;
  safetyMargin: SafetyMargin;
  alternativeRoutes: FlightRoute[];
  updateRequired: RouteUpdateTiming;
}

interface TurbulenceResponse {
  action: 'CONTINUE_WITH_CAUTION' | 'CHANGE_ALTITUDE' | 'CONTINUE_WITH_ENHANCED_PRECAUTIONS' | 'DEVIATE_ROUTE';
  recommendedActions?: string[];
  speedAdjustment?: SpeedAdjustment;
  monitoring?: 'CONTINUOUS' | 'FREQUENT' | 'STANDARD';
  recommendedAltitude?: number;
  rationale?: string;
  fuelImpact?: FuelImpact;
  timeImpact?: TimeImpact;
  alternativeAltitudes?: AltitudeOption[];
  deviationRoute?: FlightRoute;
  deviationReason?: string;
  estimatedDelay?: number;
  fuelPenalty?: number;
  safetyBenefit?: SafetyBenefit;
}

interface IcingDecision {
  action: 'ACTIVATE_DEICING_SYSTEMS' | 'ESCAPE_ICING_CONDITIONS' | 'IMMEDIATE_ESCAPE';
  systemsToActivate?: DeicingSystem[];
  monitoringFrequency?: string;
  fuelImpact?: number;
  continueThreshold?: number;
  escapeOptions?: EscapeOption[];
  recommendedOption?: EscapeOption;
  urgency?: 'LOW' | 'MODERATE' | 'HIGH' | 'CRITICAL';
  timeConstraint?: number;
  emergencyRoute?: FlightRoute;
  rationale?: string;
  nearestAlternateAirport?: Airport;
  declareEmergency?: boolean;
}

interface ComprehensiveWeatherDecision {
  primaryStrategy: WeatherStrategy;
  executionPlan: ExecutionPlan;
  contingencyPlans: ContingencyPlan[];
  riskMitigation: RiskMitigationPlan;
  decisionConfidence: number;
  reviewPoints: ReviewPoint[];
  communicationPlan: CommunicationPlan;
}