Search Results (187)

Control-barrier-function-based safety filters are promising for autonomous driving, but most existing formulations treat obstacle perception as deterministic or account only for bounded ego state-estimation errors. This becomes limiting when obstacle existence, position, motion, and sensing quality vary online. We present a sensor-health- and belief-aware risk-adaptive high-order control barrier function (HOCBF) safety filter for dynamic obstacle avoidance. The method uses obstacle belief from a perception/tracking module, inflates residual obstacle uncertainty according to an object-wise sensor-health score, and converts upper-tail risk into adaptive HOCBF tightening through conditional value-at-risk (CVaR). Sensor health enters the controller through both covariance inflation and online CVaR confidence scheduling. The resulting quadratic program combines deterministic ego-error robustness with probabilistic perception uncertainty while minimally modifying the nominal control input. The zero-slack solution guarantees forward invariance of the risk-tightened safe set under the stated assumptions, whereas the slack-activated mode provides a quantified least-violation fallback rather than a strict safety guarantee. Simulations on a nonlinear 3-DOF bicycle model evaluate critical cut-in, sudden perception degradation, merge-bottleneck, fixed-CVaR, sensitivity, runtime-scaling, heterogeneous multi-obstacle, and heavy-tailed uncertainty cases. Full article

Startup investment decisions are always accompanied by high uncertainty, limited historical data, and the need to simultaneously consider financial performance, sustainability, and innovation. With the rapid expansion of financial technologies, the use of digital decision-support tools to manage this complex environment has become increasingly important. This study presents a multi-objective optimization framework for startup portfolio selection that simultaneously maximizes expected returns, minimizes downside risk using the Conditional Value-at-Risk (CVaR) measure, improves sustainability performance based on ESG indicators, and considers liquidity constraints. The main innovation of this study is the simultaneous integration of financial and non-financial criteria alongside a set of realistic structural constraints, including budget constraints, the number of options available, the concentration ceiling, and the minimum required levels for ESG, innovation, and liquidity. The results show that the proposed model is able to create a transparent balance between return, risk, sustainability, and investment horizon, and by changing the parameters related to risk and sustainability, it can target capital flows towards more innovative startups with higher ESG scores. This framework can be used as a practical tool for investors, digital investment platforms, and policymakers in responsible and data-driven capital allocation. Full article

Bond ETF rebalancing is difficult to describe with return and risk objectives alone, because a portfolio that looks attractive on paper may still be impractical if it requires large and unstable trades. This paper proposes a topology-aware multi-objective particle swarm optimization framework for bond ETF allocation under credit-risk-related constraints. The method jointly considers annualized return, CVaR, and diversification, while enforcing long-only, exposure, and hard maximum-step turnover constraints. The central idea is to treat the swarm as a communication graph: particles exchange information through an explicit topology, and this topology affects how feasible regions are explored and how leaders are selected. When a candidate portfolio update violates the turnover budget, it is repaired toward the feasible set before evaluation, so that the search remains tied to tradable rebalancing decisions. We test the framework in a walk-forward out-of-sample backtest on U.S. bond ETFs from 2008 to 2024. The empirical analysis compares stronger classical and evolutionary baselines, four communication topologies, hard-versus-soft turnover control, stress-period behavior, and a synthetic scalability proxy. The results suggest that hard turnover repair is effective in truncating extreme rebalancing events, while communication topology changes the return–risk–turnover profile. In our experiments, the ring topology gives the most stable default behavior. Overall, the evidence suggests that topology is not just an implementation detail in swarm-based portfolio search, but a design choice that affects constrained multi-objective allocation. Full article

The transition toward sustainable and low-carbon power systems increasingly depends on the efficient accommodation of high shares of renewable energy while maintaining secure and reliable grid operation. In interconnected power systems, this challenge is intensified by strong cross-regional coupling, tie-line flow violation risks, and the high computational burden of fully coupled probabilistic assessments. To support the sustainable operation of renewable-rich interconnected systems, this paper proposes a probabilistic production simulation method that incorporates risk-aware tie-line exchange optimization. Sequential random sample paths are constructed by considering load fluctuations, renewable energy output uncertainty, and random outages of conventional units. Using cross-regional exchange power as coupling variables, a conditional value-at-risk (CVaR)-based pre-scheduling model is established to control tie-line and interface flow tail risks. Given the scheduled exchange power, cross-regional exchanges are transformed into regional boundary power injections, enabling decoupled sequential probabilistic production simulation for each region. The exchange schedule is then iteratively updated through marginal-value feedback. A four-region interconnected system is used for case-study validation. Results show that the proposed method improves renewable energy accommodation, reduces renewable curtailment, suppresses tie-line flow violation risk, and maintains high reliability assessment accuracy. Compared with the region-decoupled benchmark with fixed exchange power, the proposed method increases the renewable energy accommodation rate from 93.82% to 95.41% and reduces renewable curtailment from 312,162 MWh to 231,284 MWh, while also lowering expected energy not served and loss of load expectation. In addition, under the reported case-study setting, the proposed RC-IEF-PPS reduces the computation time from 5216.24 s for Full-PPS to 4074.63 s, i.e., by 21.9%, while maintaining comparable reliability assessment accuracy. These results indicate that the proposed framework can support the sustainable integration of high-penetration renewable energy by improving clean-energy utilization, operational reliability, and computational tractability in interconnected power systems. Full article

The Brazilian craft beer market has experienced continuous growth, increasing operational challenges for small- and medium-sized breweries that frequently rely on empirical and spreadsheet-based production routines. These practices often lead to inefficient resource allocation, production instability, and sustainability concerns. This study proposes an integrated analytical framework combining Machine Learning (ML) and Operations Research (OR) to improve demand forecasting and production planning. The methodology is based on a synthetic dataset calibrated to the operational conditions of a Brasília-based craft brewery, incorporating realistic demand patterns such as seasonality, trend, and intermittency across multiple SKUs over an 18-month horizon. Forecasting models—including Moving Average, Single Exponential Smoothing, and a global ML-based proxy—were evaluated using rolling-origin validation. The resulting probabilistic forecasts were integrated into a capacity-constrained optimization model based on linear programming, extended with risk-aware decision-making using Conditional Value-at-Risk (CVaR). The results indicate that the ML-based approach achieved competitive forecasting performance (sMAPE = 5.83% and MAE = 11.76) while enabling the generation of capacity-feasible and risk-aware production plans aligned with service-level targets. The integration of probabilistic forecasts into the optimization model allowed explicit trade-offs between cost, service level, and resource utilization. The main contribution of this study lies in demonstrating how the integration of predictive and prescriptive analytics can support more sustainable production planning in resource-constrained manufacturing environments. By replacing ad hoc spreadsheet routines with a closed-loop decision-support system, the proposed framework advances the literature on data-driven PPC and provides practical guidance for SMEs operating under uncertainty. Full article

Strategic bidding for wind–battery hybrid systems is increasingly critical as electricity spot markets transition toward market-oriented mechanisms, particularly in Chinese pilot regions. However, dual uncertainties—wind generation variability and volatile locational marginal prices (LMPs)—expose market participants to significant financial tail risk. This study develops a risk-constrained reinforcement learning framework for optimal bidding of wind–storage hybrid systems. We employ soft actor–critic (SAC) for continuous action control and integrate conditional value-at-risk (CVaR) into reward design to explicitly penalize low-probability, high-loss outcomes. The framework incorporates realistic operational constraints, including linearized battery degradation costs and a market-compatible single-bid abstraction for hourly settlement. Using one-year historical operational data from a 150 MW wind farm (with a 91-day test period), we find that storage integration increases annual profit by 108.4–114.2% relative to wind-only operation. Critically, the SAC–CVaR policy (η = 0.35) preserves 97.3% of risk-neutral profit ($7.71 M vs. $7.93 M) while substantially mitigating downside risk: CVaR@95% improves by 42.4% (−$549 vs. −$952) and VaR@95% improves by 30.1% (−$275 vs. −$393). The trained policy achieves sub-millisecond inference (0.262 ms per decision, ~3820 decisions/s), corresponding to a 3.8 × 10⁴–5.7 × 10⁴× speedup over optimization-based solvers (10–15 s per decision), enabling real-time deployment. Behavioral analysis reveals that the agent learns adaptive, forecast-normalized bidding strategies with more conservative reporting in high-price regimes and counter-cyclical battery dispatch patterns, demonstrating effective coordination between profitability and risk control under volatile market conditions. Full article

This paper examines the market maturation hypothesis in cryptocurrency markets through a three-stage analysis of the evolution of tail risk in Bitcoin (BTC) and Ethereum (ETH). Using daily closing prices from January 2015 to February 2026 for BTC (n = 4058) and November 2017 to February 2026 for ETH (n = 3015), we employ 365-day rolling windows—reflecting the continuous 24/7 operation of cryptocurrency markets—to trace the temporal dynamics of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and Maximum Drawdown (MDD). The empirical strategy combines (i) Newey–West trend tests on rolling risk metrics, (ii) regime-conditional analysis across market states (Bull, Bear, or Neutral) and volatility regimes (high/low uncertainty), and (iii) exceedance correlation analysis to capture asymmetric BTC–ETH tail dependence. The results are consistent with the market maturation hypothesis: all ten trend coefficients across both assets are statistically significant (p < 0.001), with linear time trends explaining up to 46.8% (BTC VaR₁%) and 67.5% (ETH VaR₁%) of variation in rolling tail risk. Sub-period comparisons confirm economically meaningful declines—BTC VaR₁% fell by 22.0% and ETH VaR₁% by 26.6% between the early and late subsamples. However, maturation is markedly asymmetric across uncertainty regimes: tail-risk reductions concentrate in low-uncertainty periods, whereas BTC MDD in high-uncertainty regimes shows no significant improvement (+1.0%, p = 0.176). Excess correlation analysis reveals a persistent and widening downside asymmetry (ρ⁻ = 0.847 vs. ρ⁺ = 0.246 at the 90th percentile), with late-period upper-tail correlation turning negative (ρ⁺ = −0.175 at the 95th percentile), implying that portfolio diversification within the cryptocurrency asset class remains illusory during market stress. These findings carry direct implications for institutional risk management, stress-testing frameworks, and prudential regulation of digital assets. Full article

With the rapid development of smart cities, coordinating diverse distributed energy resources through storage-centric shared management has become a critical challenge. This paper proposes a bi-level energy management framework to support peer-to-peer energy trading among multiple virtual power plants (VPPs) under multidimensional uncertainties. The interaction is modeled as a Stackelberg–Nash equilibrium framework, in which OK, we will make the necessary revisions as per the requirements.a public energy storage operator and a natural gas company act as leaders to maximize social welfare and design differentiated trading strategies for VPPs. The VPPs act as followers and participate in cooperative energy trading based on a generalized Nash equilibrium scheme, sharing surplus energy and allocating cooperative benefits according to their contributions. To address uncertainty, Conditional Value at Risk (CVaR) is adopted to quantify the expected loss of the upper-level decision makers. The lower-level VPP problem is formulated as a three-stage stochastic robust optimization model considering renewable generation uncertainty. To solve the resulting nonlinear bi-level problem, a two-stage solution approach combining particle swarm optimization and KKT-based reformulation is developed to transform it into a tractable mixed-integer linear programming model. Numerical case studies verify the effectiveness of the proposed framework. Full article

High-rise elevator group control systems operate under pronounced nonstationarity during commuting peaks, post-event surges, and capacity degradation, where the waiting time distribution becomes right-tail heavy and stresses service-level agreements (SLAs) defined by coverage and high-quantile targets. At the same time, the time-of-use tariffs and carbon constraints sharpen the tension between peak-power control, energy savings, and service capacity. This paper proposes a two-layer resilience scheduling framework that integrates queueing-based planning with safe reinforcement learning (RL) fine-tuning. In the planning layer, parsimonious queueing approximations and scenario-based evaluation construct a finite set of implementable mode cards and emergency switching cards; Sample Average Approximation (SAA) combined with Conditional Value-at-Risk (CVaR) constraints filter candidates to enforce tail-risk-aware service limits while keeping power demand within a prescribed envelope. In the execution layer, online dispatch is formulated as a constrained Markov decision process; within the planning layer limits, action masking and Lagrangian safe RL learn small adaptive adjustments to suppress tail-waiting risk and improve recovery dynamics without increasing peak-power commitments. The experiments under morning peaks and post-event surges confirm tail risk reduction and accelerated recovery. For partial outages, the framework prioritizes SLA coverage and recovery speed, accepting a bounded increase in tail risk as a manageable trade-off. Throughout all tests, peak power remains within the prescribed limits. Improvements persist across random seeds and demand fluctuations, indicating distributional robustness and cross-scenario generalization. Ablation studies further reveal complementary roles: removing the planning layer CVaR screening worsens tail performance, while removing the execution layer action masking increases constraint violations and destabilizes recovery. Full article

During pandemic emergencies, demand for relief supplies in affected areas surges abruptly and evolves randomly and dynamically, resulting in highly asymmetric supply and demand. Ensuring timely and reliable supply requires robust decision-making under risk. This study addresses a stochastic multi-objective location-routing problem (LRP) that simultaneously considers demand uncertainty and travel time variability. A multi-scenario stochastic programming model is developed with three objectives: minimizing total system cost, minimizing total waiting time, and minimizing the composite conditional value at risk (CVaR–Rcomp) to capture tail risks under extreme scenarios. A novel regret-based risk mechanism is introduced to unify temporal and cost dimensions, enabling joint evaluation of uncertainties within a single framework. To solve this challenging high-dimensional problem, a reinforcement learning-enhanced NSGA-III (RL-NSGAIII) is proposed. Specifically, Q-learning generates high-quality initial solutions, which accelerate convergence and improve population diversity for NSGA-III. Case studies demonstrate that the proposed method outperforms traditional evolutionary algorithms in convergence efficiency and Pareto solution quality, while effectively revealing potential risk blind spots. The results provide quantitative decision support and robust optimization insights for emergency logistics networks operating under uncertain conditions. Full article

To mitigate the compliance deviation risk induced by wind power output fluctuations, this paper proposes a two-stage joint bidding model for cascaded hydropower–wind systems within the electricity spot market framework from a price-taker perspective, explicitly accounting for the decision maker’s risk preferences. To capture the impacts of hydropower vibration zones on joint bidding decisions, the feasible output range of hydropower units is divided into multiple safe operating sub-intervals, and vibration zone avoidance is modeled using binary decision variables; meanwhile, penalty terms are incorporated into the objective function to suppress vibration zone crossing behaviors. From a risk-aware decision-making perspective, Conditional Value-at-Risk (CVaR) is adopted to quantify the downside tail risk of bidding revenues, and a risk factor is introduced to flexibly adjust the decision maker’s risk attitude. Finally, a case study based on a cascaded hydropower system and an associated wind farm in Southwest China is conducted to demonstrate the effectiveness of the proposed joint bidding strategy and to examine the impacts of risk preferences and vibration zone considerations on joint bidding outcomes. Full article

Aiming at the poor robustness of maintenance schemes in industrial equipment maintenance projects, which arises from uncertain factors including fault degree, maintenance time, and resource availability, this paper proposes a synergistic cost-risk optimization method that integrates Distributionally Robust Optimization (DRO) and Conditional Value-at-Risk (CVaR). First, the paper analyzes the uncertainty characteristics of such projects and constructs a distribution ambiguity set based on the Wasserstein distance to depict unknown probability distributions. Second, a two-stage DRO-CVaR optimization model is established: the first stage formulates a pre-optimization scheme to minimize maintenance costs, and the second stage introduces CVaR for extreme risk measurement, thus achieving optimal decision-making under the worst-case scenario. Finally, a nested Column-and-Constraint Generation (C&CG) algorithm is designed to solve the proposed model. A numerical example is conducted for verification, and results show that compared with traditional stochastic programming and pure DRO methods, the proposed method reduces the total cost by 10.4%, the worst-case scenario loss by 28.9%, and the CVaR value by 32.0%. It thus exhibits superior economic efficiency and risk resistance in uncertain environments. Full article

This study proposes a hierarchical signal-to-policy learning framework for risk-aware portfolio optimization that integrates model-based return forecasting, explainable machine learning, and deep reinforcement learning (DRL) within a unified architecture. In the first stage, next-period returns are estimated using gradient-boosted tree models, and SHAP-based feature attributions are extracted to provide transparent, factor-level explanations of the predictive signals. In the second stage, a Proximal Policy Optimization (PPO) agent incorporates both predictive forecasts and explanatory signals into its state representation and learns dynamic allocation policies under a mean–CVaR reward function that explicitly penalizes tail risk while controlling trading frictions. By separating signal extraction from policy learning, the proposed architecture allows the use of economically interpretable predictive signals to incorporate into the policy’s state representation while preserving the flexibility and adaptability of reinforcement learning. Empirical evaluations on U.S. sector ETFs and Dow Jones Industrial Average constituents show that the hierarchical framework delivers higher and stable out-of-sample risk-adjusted returns relative to both a single-layer DRL agent trained solely on technical indicators, a mean–CVaR optimized portfolio using the same parameters used in the proposed hierarchical model and standard equal weight as well as index-based benchmarks. These results demonstrate that integrating explainable predictive signals with risk-sensitive reinforcement learning improves the robustness and stability of data-driven portfolio strategies. Full article

To enhance the low-carbon operational performance of integrated energy systems (IESs) under multi-source uncertainties, this study proposes a bilevel stochastic optimization framework incorporating a dynamic mean–CVaR risk model and a tiered carbon pricing mechanism. The upper level adopts an improved NSGA-II to jointly optimize economic cost, carbon emissions, and system flexibility through capacity planning decisions. The lower level performs scenario-based operation evaluation with a time-varying risk aversion coefficient, enabling differentiated risk responses across operating periods. A stepwise carbon price function and a capped carbon revenue mechanism are introduced to represent real carbon market regulations and avoid excessive emission reduction benefits. Multidimensional uncertainty scenarios—covering renewable variability, load fluctuations, and market price disturbances—are generated for risk-aware evaluation. Simulation results show that the proposed approach effectively reduces cost and emission volatility and achieves a more balanced trade-off between economy and low-carbon performance compared with conventional static-risk models. Sensitivity analyses further reveal that increased risk aversion shifts system operation strategies from economy-oriented to robustness-oriented modes, highlighting the importance of dynamic risk modeling and carbon policy design for future low-carbon multi-energy systems. Full article

Cryptocurrency markets are characterized by extreme volatility, fat-tailed return distributions, and frequent regime shifts, challenging traditional mean–variance portfolio optimization. In such environments, downside risk management becomes central, and tail-sensitive measures such as Conditional Value-at-Risk (CVaR) are increasingly adopted. However, empirical evidence remains mixed regarding whether CVaR-based strategies provide consistent protection across market regimes and tail depths. This study conducts a comprehensive empirical evaluation of tail-risk-based portfolio strategies using cryptocurrency data from 2018 to 2025. A rolling-window back-testing framework with weekly rebalancing is employed. We compare traditional benchmarks, moment-based and robust CVaR strategies, regime-dependent CVaR optimization, regression-enhanced ES–CVaR hybrids, and reinforcement learning-based CVaR policies. Performance is evaluated using mean return, volatility, CVaR at multiple confidence levels (90%, 95%, and 99%), and maximum drawdown. Market regimes are identified through volatility-based rules, and robustness is assessed via sensitivity analysis and block-bootstrap confidence intervals. The results show that no single strategy dominates across all conditions. Hybrid ES–Reg–CVaR strategies provide stable protection under moderate tail risk, reinforcement learning-based CVaR strategies adapt better to extreme tails, and regime-based CVaR optimization consistently limits drawdowns during stress periods. These findings demonstrate that effective CVaR-based portfolio management in cryptocurrency markets requires a regime- and tail-depth-dependent approach rather than a universal optimization rule. Full article