データ駆動型意思決定¶

// 統合分析エンジンによる意思決定支援システム
interface AnalysisRequest {
  problemType: 'descriptive' | 'predictive' | 'prescriptive';
  data: Dataset;
  objective: BusinessObjective;
  constraints?: Constraint[];
  timeframe: TimeConstraint;
  stakeholders: Stakeholder[];
}

interface AnalysisResult {
  statisticalInsights: StatisticalInsights;
  predictions?: PredictionResult;
  optimization?: OptimizationResult;
  recommendations: ActionRecommendation[];
  confidence: ConfidenceMetrics;
  methodology: MethodologyReport;
}

interface BusinessObjective {
  type: 'maximize' | 'minimize' | 'satisfice';
  target: string;
  success_metrics: string[];
  constraints: OperationalConstraint[];
}

class IntegratedDecisionAnalysisEngine {
  private statisticalAnalyzer: StatisticalAnalyzer;
  private machineLearningEngine: MachineLearningEngine;
  private optimizationSolver: OperationsResearchSolver;
  private methodSelector: AnalysisMethodSelector;
  private resultIntegrator: ResultIntegrator;

  constructor(config: EngineConfiguration) {
    this.initializeAnalysisEngines(config);
    this.methodSelector = new IntelligentMethodSelector();
    this.resultIntegrator = new MultiMethodResultIntegrator();
  }

  private initializeAnalysisEngines(config: EngineConfiguration): void {
    // 統計分析エンジン
    this.statisticalAnalyzer = new ComprehensiveStatisticalAnalyzer({
      methods: [
        'descriptive_statistics', 'hypothesis_testing', 'regression_analysis',
        'correlation_analysis', 'time_series_analysis', 'survival_analysis'
      ],
      significance_level: 0.05,
      correction_methods: ['bonferroni', 'fdr', 'holm']
    });

    // 機械学習エンジン
    this.machineLearningEngine = new AdvancedMLEngine({
      algorithms: {
        regression: ['linear', 'polynomial', 'ridge', 'lasso', 'elastic_net', 'random_forest'],
        classification: ['logistic', 'svm', 'random_forest', 'gradient_boosting', 'neural_network'],
        clustering: ['kmeans', 'hierarchical', 'dbscan', 'gaussian_mixture'],
        time_series: ['arima', 'lstm', 'prophet', 'transformer']
      },
      ensemble_methods: ['voting', 'bagging', 'boosting', 'stacking'],
      hyperparameter_tuning: 'bayesian_optimization'
    });

    // オペレーショナルリサーチソルバー
    this.optimizationSolver = new OROptimizationSolver({
      solvers: {
        linear_programming: 'simplex',
        integer_programming: 'branch_and_bound',
        nonlinear_programming: 'interior_point',
        network_optimization: 'min_cost_flow',
        dynamic_programming: 'bellman',
        metaheuristics: ['genetic_algorithm', 'simulated_annealing', 'particle_swarm']
      },
      tolerance: 1e-6,
      max_iterations: 10000
    });
  }

  // 統合分析の実行
  async performIntegratedAnalysis(
    request: AnalysisRequest
  ): Promise<AnalysisResult> {
    // 1. 問題分析と手法選択
    const methodPlan = await this.methodSelector.selectOptimalMethods(
      request.problemType,
      request.data,
      request.objective
    );

    // 2. 段階的分析実行
    const analysisResults = await this.executeAnalysisPipeline(
      request,
      methodPlan
    );

    // 3. 結果統合と推奨事項生成
    const integratedResult = await this.resultIntegrator.integrate(
      analysisResults,
      request.objective
    );

    return integratedResult;
  }

  // 分析パイプラインの実行
  private async executeAnalysisPipeline(
    request: AnalysisRequest,
    methodPlan: AnalysisMethodPlan
  ): Promise<PipelineResult> {
    const results: PipelineResult = {
      statistical: null,
      ml: null,
      optimization: null
    };

    // Phase 1: 統計分析（現状理解）
    if (methodPlan.includeStatistical) {
      results.statistical = await this.executeStatisticalAnalysis(
        request.data,
        methodPlan.statisticalMethods
      );
    }

    // Phase 2: 機械学習（予測・分類）
    if (methodPlan.includeMachineLearning) {
      results.ml = await this.executeMachineLearningAnalysis(
        request.data,
        methodPlan.mlMethods,
        results.statistical // 統計分析結果を活用
      );
    }

    // Phase 3: 最適化（意思決定）
    if (methodPlan.includeOptimization) {
      results.optimization = await this.executeOptimizationAnalysis(
        request,
        results.statistical,
        results.ml
      );
    }

    return results;
  }

  // 統計分析の実行
  private async executeStatisticalAnalysis(
    data: Dataset,
    methods: StatisticalMethod[]
  ): Promise<StatisticalResults> {
    const results: StatisticalResults = {};

    // 記述統計
    if (methods.includes('descriptive')) {
      results.descriptive = await this.statisticalAnalyzer.computeDescriptiveStats(data);
    }

    // 相関分析
    if (methods.includes('correlation')) {
      results.correlation = await this.statisticalAnalyzer.analyzeCorrelations(data);
    }

    // 回帰分析
    if (methods.includes('regression')) {
      results.regression = await this.statisticalAnalyzer.performRegression(data);
    }

    // 仮説検定
    if (methods.includes('hypothesis_testing')) {
      results.hypothesis_tests = await this.statisticalAnalyzer.conductHypothesisTests(data);
    }

    // 因果推論
    if (methods.includes('causal_inference')) {
      results.causal_analysis = await this.statisticalAnalyzer.performCausalInference(data);
    }

    return results;
  }

  // 最適化分析の実行
  private async executeOptimizationAnalysis(
    request: AnalysisRequest,
    statisticalResults: StatisticalResults,
    mlResults: MachineLearningResults
  ): Promise<OptimizationResults> {
    // 最適化問題の定式化
    const optimizationProblem = this.formulateOptimizationProblem(
      request.objective,
      request.constraints,
      statisticalResults,
      mlResults
    );

    // 適切なソルバーの選択
    const solver = this.selectOptimalSolver(optimizationProblem);

    // 最適化実行
    const solution = await solver.solve(optimizationProblem);

    // 感度分析
    const sensitivityAnalysis = await this.performSensitivityAnalysis(
      optimizationProblem,
      solution
    );

    // シナリオ分析
    const scenarioAnalysis = await this.performScenarioAnalysis(
      optimizationProblem,
      solution
    );

    return {
      optimal_solution: solution,
      sensitivity_analysis: sensitivityAnalysis,
      scenario_analysis: scenarioAnalysis,
      robustness_analysis: await this.assessRobustness(solution)
    };
  }

  // 具体的業務問題への適用
  async solveBusiness Problem(
    problemDefinition: BusinessProblemDefinition
  ): Promise<BusinessSolution> {
    switch (problemDefinition.category) {
      case 'resource_allocation':
        return await this.solveResourceAllocation(problemDefinition);

      case 'demand_forecasting':
        return await this.solveDemandForecasting(problemDefinition);

      case 'price_optimization':
        return await this.solvePriceOptimization(problemDefinition);

      case 'supply_chain_optimization':
        return await this.solveSupplyChainOptimization(problemDefinition);

      case 'customer_segmentation':
        return await this.solveCustomerSegmentation(problemDefinition);

      default:
        return await this.solveGenericProblem(problemDefinition);
    }
  }

  // リソース配分問題の解決
  private async solveResourceAllocation(
    problem: ResourceAllocationProblem
  ): Promise<ResourceAllocationSolution> {
    // 1. 統計分析：過去のリソース利用効率分析
    const utilizationAnalysis = await this.statisticalAnalyzer.analyzeResourceUtilization(
      problem.historical_data
    );

    // 2. 機械学習：需要予測
    const demandForecast = await this.machineLearningEngine.forecastDemand(
      problem.demand_data,
      problem.forecast_horizon
    );

    // 3. 最適化：制約下でのリソース配分最適化
    const allocationProblem = new LinearProgrammingProblem({
      objective: 'maximize_efficiency',
      variables: problem.resources,
      constraints: [
        ...problem.capacity_constraints,
        ...problem.demand_constraints,
        ...this.generateForecastConstraints(demandForecast)
      ]
    });

    const optimalAllocation = await this.optimizationSolver.solve(allocationProblem);

    return {
      allocation: optimalAllocation.solution,
      expected_efficiency: optimalAllocation.objective_value,
      utilization_insights: utilizationAnalysis,
      demand_forecast: demandForecast,
      sensitivity_analysis: optimalAllocation.sensitivity,
      implementation_plan: this.generateImplementationPlan(optimalAllocation)
    };
  }

  // 価格最適化問題の解決
  private async solvePriceOptimization(
    problem: PriceOptimizationProblem
  ): Promise<PriceOptimizationSolution> {
    // 1. 統計分析：価格弾性の推定
    const elasticityAnalysis = await this.statisticalAnalyzer.estimatePriceElasticity(
      problem.sales_data,
      problem.price_data
    );

    // 2. 機械学習：需要予測モデル
    const demandModel = await this.machineLearningEngine.buildDemandModel(
      problem.features,
      problem.target_demand
    );

    // 3. 最適化：利益最大化価格の決定
    const pricingProblem = new NonlinearProgrammingProblem({
      objective: 'maximize_profit',
      demand_function: demandModel,
      cost_function: problem.cost_function,
      price_bounds: problem.price_bounds,
      elasticity_constraints: elasticityAnalysis.constraints
    });

    const optimalPricing = await this.optimizationSolver.solve(pricingProblem);

    return {
      optimal_prices: optimalPricing.solution,
      expected_profit: optimalPricing.objective_value,
      elasticity_insights: elasticityAnalysis,
      demand_sensitivity: this.analyzeDemandSensitivity(demandModel),
      competitive_analysis: await this.performCompetitiveAnalysis(optimalPricing),
      implementation_strategy: this.designPricingStrategy(optimalPricing)
    };
  }

  // リアルタイム意思決定支援
  createRealTimeDecisionSupport(
    context: DecisionContext
  ): Observable<RealTimeDecisionSupport> {
    return interval(context.refresh_interval).pipe(
      mergeMap(async () => {
        // データ収集
        const currentData = await this.collectRealTimeData(context);

        // 迅速分析（軽量版）
        const quickAnalysis = await this.performQuickAnalysis(currentData, context);

        // 閾値チェック
        const alerts = this.checkDecisionThresholds(quickAnalysis, context);

        // 推奨アクション生成
        const recommendations = await this.generateRealTimeRecommendations(
          quickAnalysis,
          alerts,
          context
        );

        return {
          timestamp: new Date(),
          analysis: quickAnalysis,
          alerts: alerts,
          recommendations: recommendations,
          confidence: this.calculateRealTimeConfidence(quickAnalysis)
        };
      }),

      // 重要度フィルタリング
      filter(result => result.alerts.length > 0 || result.recommendations.length > 0),

      share()
    );
  }
}

// 結果インターフェース
interface PipelineResult {
  statistical: StatisticalResults | null;
  ml: MachineLearningResults | null;
  optimization: OptimizationResults | null;
}

interface BusinessSolution {
  solution_type: string;
  primary_recommendations: ActionRecommendation[];
  supporting_analysis: AnalysisEvidence;
  implementation_plan: ImplementationPlan;
  success_metrics: SuccessMetric[];
  risk_assessment: RiskAssessment;
}

// インテリジェント分析手法選択システム
class IntelligentMethodSelector {
  private problemClassifier: ProblemClassifier;
  private methodDatabase: AnalysisMethodDatabase;
  private performancePredictor: MethodPerformancePredictor;

  constructor() {
    this.problemClassifier = new MLProblemClassifier();
    this.methodDatabase = new ComprehensiveMethodDatabase();
    this.performancePredictor = new EnsemblePerformancePredictor();
  }

  // 最適分析手法の選択
  async selectOptimalMethods(
    problemType: ProblemType,
    data: Dataset,
    objective: BusinessObjective
  ): Promise<AnalysisMethodPlan> {
    // 1. 問題特性の分析
    const problemCharacteristics = await this.analyzeProblemCharacteristics(
      problemType, data, objective
    );

    // 2. データ特性の分析
    const dataCharacteristics = this.analyzeDataCharacteristics(data);

    // 3. 候補手法の生成
    const candidateMethods = this.methodDatabase.getCandidateMethods(
      problemCharacteristics,
      dataCharacteristics
    );

    // 4. 手法性能の予測
    const methodPerformances = await Promise.all(
      candidateMethods.map(method => 
        this.performancePredictor.predict(method, problemCharacteristics, dataCharacteristics)
      )
    );

    // 5. 最適組み合わせの選択
    const optimalCombination = this.selectOptimalCombination(
      candidateMethods,
      methodPerformances,
      objective
    );

    return {
      includeStatistical: optimalCombination.statistical !== null,
      includeMachineLearning: optimalCombination.ml !== null,
      includeOptimization: optimalCombination.optimization !== null,
      statisticalMethods: optimalCombination.statistical?.methods || [],
      mlMethods: optimalCombination.ml?.methods || [],
      optimizationMethods: optimalCombination.optimization?.methods || [],
      expectedPerformance: optimalCombination.expectedPerformance,
      reasoning: optimalCombination.reasoning
    };
  }

  // 問題特性の分析
  private async analyzeProblemCharacteristics(
    problemType: ProblemType,
    data: Dataset,
    objective: BusinessObjective
  ): Promise<ProblemCharacteristics> {
    return {
      complexity: this.assessProblemComplexity(problemType, data),
      structure: this.analyzeProblemStructure(objective),
      constraints: this.identifyConstraintTypes(objective.constraints),
      uncertainty: this.assessUncertaintyLevel(data),
      timeframe: this.analyzeTimeConstraints(objective),
      stakeholder_complexity: this.assessStakeholderComplexity(objective)
    };
  }

  // データ特性の分析
  private analyzeDataCharacteristics(data: Dataset): DataCharacteristics {
    return {
      size: this.assessDataSize(data),
      dimensionality: this.calculateDimensionality(data),
      quality: this.assessDataQuality(data),
      distribution: this.analyzeDistribution(data),
      temporal_nature: this.analyzeTemporalNature(data),
      missing_patterns: this.analyzeMissingPatterns(data),
      correlation_structure: this.analyzeCorrelationStructure(data)
    };
  }

  // 手法組み合わせの評価
  private selectOptimalCombination(
    candidates: CandidateMethod[],
    performances: MethodPerformance[],
    objective: BusinessObjective
  ): OptimalMethodCombination {
    // 多目的最適化による手法選択
    const optimizer = new MultiObjectiveOptimizer({
      objectives: [
        'accuracy',
        'interpretability',
        'computational_efficiency',
        'robustness'
      ],
      weights: this.determineObjectiveWeights(objective)
    });

    const combinations = this.generateValidCombinations(candidates);
    const evaluatedCombinations = combinations.map(combination => ({
      combination,
      score: optimizer.evaluate(combination, performances)
    }));

    const optimal = evaluatedCombinations.reduce((best, current) => 
      current.score > best.score ? current : best
    );

    return {
      ...optimal.combination,
      expectedPerformance: optimal.score,
      reasoning: this.generateSelectionReasoning(optimal.combination, performances)
    };
  }
}