Search Results (225)

A pursuer and an evader can exploit low-energy, non-Keplerian trajectories in cislunar space, making it difficult to obtain the saddle point for impulsive orbital pursuit–evasion games (OPEG). To address this problem, this paper first establishes a zero-sum differential game model based on the circular restricted three-body problem (CR3BP), where the terminal interception time is taken as the performance objective. The necessary optimality conditions for impulsive maneuvers are then derived using Pontryagin’s Maximum Principle (PMP), which transforms the optimal control problem into multipoint boundary value problems (MPBVPs). Subsequently, to overcome the high sensitivity of the MPBVPs to initial costate vectors in shooting methods, a two-layer hybrid initial-guess strategy combining a genetic algorithm with a time-domain coarse-grid search method is proposed for the single-impulse case. Furthermore, a receding-horizon strategy is introduced to generate the initial impulse sequence guess stage by stage for multiple-impulse cases. Finally, numerical simulations demonstrate that the proposed initial-guess strategy can effectively obtain the Stackelberg equilibrium solution for representative cislunar scenarios, including distant retrograde orbits (DROs) and Halo orbits. Meanwhile, the effects of observation delay and three-dimensional orbital characteristics on the game outcomes are also discussed based on dynamic game theory. Full article

Low seawater temperatures constrain the operation of open rack vaporizers (ORVs) and intermediate fluid vaporizers (IFVs), while also increasing pumping-related emissions at LNG terminals. This study establishes a carbon-oriented framework for an expanded ORV–IFV regasification system sharing a fixed-speed seawater pump network and evaluates thermal discharge from an adjacent power plant as a supplementary heat source. Using measured LNG composition, we developed an Aspen HYSYS model based on the Peng–Robinson equation of state and steady-state energy balances, which was validated against field data. Electricity-related CO₂ emissions from seawater pumps and auxiliaries were quantified using the regional grid emission factor, while pump scheduling was formulated as a mixed-integer nonlinear programming (MINLP) problem. Model predictions differed from measurements by approximately 2%. Lower seawater temperatures increased emissions and restricted maximum regasification capacity to 80% and 57% of the design value at 3–4 °C and 2–3 °C, respectively. For LNG throughputs of 300, 500, and 700 t/h, CO₂ reduction increased with warm-seawater flow and inlet temperature; maximum reductions reached approximately 50–55% under 3–7 °C ambient seawater conditions and 40% under 6–20 °C conditions, with a 95% confidence interval of ±3.9 percentage points. Monthly discharge data indicated reductions of approximately 20% in winter and 45% in summer. Integrating power-plant waste heat with load-dependent pump scheduling can improve the carbon performance of LNG regasification. Full article

A floating storage and regasification unit (FSRU) provides flexible infrastructure for liquefied natural gas (LNG) import, storage, regasification, and regional distribution. Its operation requires coordinated decisions on vessel deployment, sailing speed, loading and discharge timing, terminal inventory, berth use, and backup pipeline supply, while carbon taxation changes the relative cost of alternative vessel and operating choices. This paper formulates this operational planning problem as a maritime inventory routing problem (MIRP) for an LNG distribution network centered on an FSRU. The MIRP formulation integrates FSRU and terminal inventories, heterogeneous vessel capacities, berth constraints, discrete speed choices, pipeline backup, and direct operational carbon costs. To solve the resulting large model, we use a column generation framework with a labeling algorithm for the pricing subproblem, dual stabilization, and a diving heuristic for integer recovery. Each column represents a complete vessel schedule, and the restricted master problem coordinates schedule selection, task coverage, vessel use, backup supply, and aggregate berth capacity. Computational experiments are conducted on calibrated synthetic instances. A complete enumeration benchmark for a small instance provides a diagnostic check, while larger experiments examine computation time, feasible incumbent values, time discretization, instance size, and sensitivity to carbon taxes. The results show nonlinear responses in cost and emissions: low tax rates produce limited operational changes, intermediate rates induce clearer shifts toward vessels with lower emissions at moderate cost increases, and very high stress test rates yield diminishing marginal emission reductions. These findings show how integrated scheduling can support coordinated FSRU planning under carbon costs, while the observed transition points remain specific to the tested instances. Full article

This paper investigates the trajectory tracking control problem for an unmanned surface vehicle (USV) subject to unknown time-varying environmental disturbances and the challenge of ensuring fast convergence while avoiding singularities. A novel fixed-time nonsingular fast terminal sliding mode (NFTSM) control scheme, integrated with a fixed-time disturbance observer (DOB), is proposed to achieve high-precision and robust tracking. The control architecture consists of three key components: (i) a fixed-time virtual velocity control law designed to ensure that position errors converge to zero within a fixed time even when velocity errors vanish, thereby preventing slow convergence; (ii) a nonsingular fast terminal sliding surface that eliminates the singularity issue inherent in traditional terminal sliding mode and guarantees that the USV state converges to the desired trajectory within a fixed time independent of initial conditions; and (iii) a fixed-time DOB that accurately estimates and compensates for external disturbances, with estimation errors proven to converge to zero in a fixed time. The stability and fixed-time convergence of the closed-loop system are rigorously established using Lyapunov theory. Comparative simulations, conducted under external disturbances on the Cybership II model, demonstrate that the proposed NFTSMC strategy significantly outperforms conventional sliding mode control (SMC) and nonsingular terminal sliding mode control (NTSMC). Specifically, the proposed scheme reduces the integral of absolute error (IAE) for position tracking by over 60% and the integral of time-weighted absolute error (ITAE) by more than 30% compared to SMC, while achieving smoother control inputs and stronger disturbance rejection. These results highlight the superior convergence speed, tracking accuracy, and robustness of the proposed controller, underscoring its originality and practical value for USV autonomous navigation. Full article

A new class of smooth exact penalty functions, constructed using the hyperbolic tangent (tanh) function, is proposed for solving constrained global optimization problems. The tanh function is chosen because of its unique mathematical properties: it is monotonic, bounded, infinitely differentiable, and provides a uniform approximation of the absolute value function with an explicit error bound of $O(1/(\gamma e\left)\right)$. These properties make it particularly suitable for constructing smooth penalty functions that preserve exactness. The proposed penalty function exhibits both smoothness and exactness: it is continuously differentiable, and for a sufficiently large penalty parameter, its local minimizers coincide exactly with those of the original constrained problem. In addition, by integrating a filling term, a novel filled penalty function is constructed that enables the algorithm to escape from a current local minimizer and locate a better one. Leveraging this filled penalty function, a global optimization algorithm is designed that performs local minimization and filling stages alternately. The convergence properties of the algorithm are rigorously established; it is shown that the sequence of objective function values is strictly decreasing and that the termination point constitutes a global approximate optimal solution. Finally, numerical experiments on 18 benchmark problems, along with statistical significance tests and performance profiles, confirm the effectiveness and competitiveness of the proposed approach against traditional quadratic penalty methods and modern solvers such as IPOPT and ALM. Full article

Toll-road operators must coordinate pavement rehabilitation and toll pricing because surface condition and tolls jointly affect route choice, realized demand, deterioration, and long-term revenue. Motivated by infrastructure REIT asset-operation requirements, this study develops a stochastic bi-level multi-period model for joint pavement maintenance and toll pricing under demand feedback. The upper level selects annual tolls and rehabilitation intensities for tolled links subject to budget and service constraints. The lower level solves an elastic-demand user equilibrium based on generalized travel disutility. The operator objective extends discounted-profit maximization by adding revenue coefficient of variation, maximum drawdown, and terminal pavement value. Budget availability and deterioration uncertainty are represented by scenario multipliers, and the model is solved by a real-coded genetic algorithm coupled with the method of successive averages (GA-MSA). Experiments on the Li-Sheng benchmark and a semi-empirical Nanjing toll-road REIT corridor show that stochastic coordinated decisions retain more than 97% of the NPV achieved by the GA-MSA profit-oriented benchmark while improving revenue stability and limiting downside risk. Supplementary comparisons with PSO-MSA and DE-MSA show that alternative upper-level search rules identify different points on the normalized risk–return surface. The findings support treating maintenance and pricing as an integrated asset-operation problem for long-horizon toll-road assets. Full article

This paper investigates a stochastic differential portfolio game between two competing investors with relative wealth preferences. The financial market consists of one risk-free asset and one risky asset, whose price dynamics follow the CEV model. We formulate this game as two utility maximization problems, where each investor aims to maximize their relative utility defined as the weighted average of the ratio between their terminal wealth and the competitor’s terminal wealth. Firstly, we derive the Hamilton–Jacobi–Bellman (HJB) equations and corresponding value functions through the dynamic programming principle. Next, we obtain the explicit solutions to equilibrium investment strategies and value functions for the non-zero-sum game under the CRRA utility framework. Finally, we conducted numerical simulations to analyze the impacts of model parameters on equilibrium strategies and provide relevant economic explanations. Full article

We study dynamic hedging in an incomplete market where the underlying asset follows a stochastic-volatility process and the hedger trades only the stock and the money-market account. The hedging problem is formulated as a multi-stage stochastic control problem with a quadratic terminal-loss objective and is solved through a Hamilton–Jacobi–Bellman framework. For the Heston model, the resulting mean-variance hedge specializes to the Galtchouk–Kunita–Watanabe projection and can be written as the sum of the spot delta and a volatility-risk correction term. We emphasize that this representation is used in the paper as an implementation theorem for our setting, rather than as a new general result. On the numerical side, we compare a finite-difference alternating-direction implicit solver with a Deep Galerkin Method, providing full implementation details for both. The finite-difference solver is the preferred method for the two-state Heston problem because it is faster and more accurate on low-dimensional grids, whereas the neural solver becomes attractive only for higher-dimensional extensions where mesh-based methods become computationally burdensome. In backtests across major S&P 500 market regimes from 2006 to 2022, the stochastic-volatility-aware hedge modestly improves on Black–Scholes hedging during stress episodes, while differences are negligible in calm markets. Across the reported experiments, the PDE-optimal mean-variance hedge is numerically indistinguishable from the recalibrated Heston hedge, indicating that the main value of the framework is theoretical unification and implementation guidance rather than a materially different trading rule in the tested setting. Fixed worst-case robust hedging is overly conservative in the historical sample, although adaptive robustness remains a promising conceptual extension. The main contribution of the paper is therefore a rigorous and implementable unification of multi-stage PDE optimization with stochastic-volatility-aware hedging, together with evidence that the economic value of model sophistication is concentrated in stressed markets. Full article

Accidents in process industries continue to cause severe casualties, and a small number of events account for a large share of fatalities. This study proposes a Topic–Hierarchy Coincidence Analysis (T-H CNA) framework to identify condition combinations associated with high-fatality outcomes by integrating BERTopic, Human Factors Analysis and Classification System (HFACS), and Coincidence Analysis (CNA). The framework is applied to 121 Chinese investigation reports of serious-or-above chemical accidents from 2015 to 2025. BERTopic is used to extract 25 causal semantic themes from accident-cause texts, which are then mapped by expert classification onto eight second-level HFACS categories (Fleiss’κ = 0.7118). On this basis, CNA identifies minimally sufficient configurations (MSCs) and traces their cross-level transmission pathways. Six three-condition MSCs are obtained, with consistency values ranging from 0.750 to 0.923; the broadest pathway covers 28.3% of high-fatality cases. Deficient organizational climate and failure to correct known problems recur as the main upstream endpoints and remain stable under stricter fatality thresholds. Although operational errors appear in 88.43% of cases, they do not enter any sufficient configuration. The results indicate that high-fatality outcomes are more closely associated with coupled upstream organizational and supervisory failures than with terminal errors alone, supporting upstream-oriented process safety governance. Full article

This study addresses a modified continuous multi-reservoir scheduling problem characterized by a high-dimensional continuous decision space, strong time-varying storage constraints, strict terminal storage closure requirements, and a highly nonconvex composite objective. To solve this challenging problem, a multi-stage collaborative memetic algorithm based on CVT-MAP-Elites and clustering gradient (MCMA-CCG) is proposed. The framework consists of three tightly coupled stages: an exploration stage based on CVT-MAP-Elites to preserve diverse high-potential elites, a clustering stage using DBSCAN with a customized noise-retention strategy, and a refinement stage that combines DE with gradient-enhanced SLSQP to perform accurate exploitation. Under a unified experimental setting, MCMA-CCG was evaluated against several representative optimization algorithms, including DE, GA, SAPHTLR, HBMO, MFA, and MBWOHHO, over 30 independent runs. The updated results show that MCMA-CCG consistently achieves the best overall performance in both the four-cycle and five-cycle reservoir scheduling scenarios while also exhibiting superior empirical runtime and feasibility behavior. In the four-cycle case, it attained a best value of 6.08 × 10³, an average of 5.97 × 10³, and a standard deviation of 4.62 × 10¹; meanwhile, it produced feasible solutions in 26 of 30 runs, achieved a mean feasibility distance of 1.20 × 10⁻³, and required only 54.91 s on average under the 30,000-function-evaluation budget. In the more challenging five-cycle case, it attained a best value of 7.60 × 10³, an average of 7.44 × 10³, and a standard deviation of 7.55 × 10¹; it still generated feasible solutions in 19 of 30 runs, with a mean feasibility distance of 4.84 × 10⁻³ and an average runtime of 93.80 s under the 50,000-function-evaluation budget. By contrast, all baseline algorithms produced no fully feasible runs under the same feasibility criterion and generally required longer wall-clock time. Ablation studies further demonstrate that the superior performance of MCMA-CCG does not arise from any single module, but from the effective synergy among quality-diversity exploration, cluster-guided seed extraction, and gradient-assisted local refinement. These results confirm both the numerical superiority and the physical interpretability of the proposed framework for complex continuous multi-reservoir scheduling problems. Full article

Agricultural cold chain logistics is characterized by inherent challenges—product perishability, high carbon emissions, and stringent time windows—which are further exacerbated by dynamic disruptions. Existing methods suffer from slow adaptability, unstable multi-objective convergence, and severe cold-start issues. This work falls within the broad scope of biomimetics—the science of emulating nature’s time-tested strategies to solve complex engineering problems—and bio-inspired data-driven methods and their applications in engineering control, optimization, and artificial intelligence. The proposed H-MODRL framework embodies core biomimetic principles: the Genetic Algorithm (GA) mimics Darwinian natural selection and genetic inheritance, the Sparrow Search Algorithm (SSA) abstracts the cooperative foraging and anti-predation behaviors of sparrow populations in nature, and the Arrhenius-based freshness-decay model captures the biochemical kinetics governing perishable biological products. By synergistically integrating these biological evolution principles, swarm intelligence, and deep learning, the framework tackles real-world logistics complexity in a manner directly inspired by living systems. This study presents a well-organized hybrid optimization framework (H-MODRL) that couples a three-stage hybrid evolutionary mechanism, synergistically integrating heuristic warm-start, evolutionary policy guidance, and deep reinforcement learning decision-making. First, an improved genetic algorithm combined with the earliest deadline first strategy constructs a feasible initial population satisfying hard time-window constraints. Second, a large neighborhood search-enhanced chaotic sparrow search algorithm builds a high-quality elite guidance set for policy learning. Third, a physics-based multi-objective proximal policy optimization model embedded with Arrhenius equation-derived freshness-decay kinetics performs online decision-making. Experiments demonstrate that pre-computed all-pairs shortest paths and an O(1) hash-based dynamic-disruption indexing mechanism support fast online replanning. On heterogeneous simulated terrains based on real Chinese geospatial data, H-MODRL outperforms state-of-the-art algorithms across four objectives—logistics cost, carbon emissions, terminal freshness, and delivery time—while exhibiting compact, low-variance performance distributions, thereby validating its engineering robustness and practical value in complex agricultural cold chain environments. Full article

The Colonel Blotto game is a classical model of competitive resource allocation, but equilibrium computation becomes difficult in heterogeneous and asymmetric instances. In this research, we study a finite-horizon dynamic reformulation in which players allocate resources sequentially over publicly observed stages, while the payoff depends only on terminal cumulative allocations. The purpose of the reformulation is not to change the primitive objective, but to represent the same terminal-payoff problem as a zero-sum Markov game. We first show that the dynamic formulation admits a pathwise payoff-equivalent Markov representation through telescoping rewards. Under a known finite horizon, costless carryover, and terminal-only payoff evaluation, the dynamic game and the corresponding static Blotto game have the same minimax value at every reachable continuation state. This is a value-equivalence result; it does not imply a one-to-one correspondence between static and dynamic equilibrium strategy sets. The proof is based on terminal-deferral upper and lower bounds for the two players. We also study action-independent geometric termination, for which the discounted telescoping return coincides exactly with the expected stopped terminal payoff, and we provide a probability-controlled mismatch bound for truncated stopping rules. Numerical finite-grid experiments illustrate the value identity and report residual diagnostics. The results clarify when sequential Markov representations preserve the original Blotto objective and when additional primitives, such as carryover depreciation or primitive flow payoffs, require separate analysis. Full article

This paper presents a theoretically grounded integration of deterministic Q-learning with relational game theory (QLRG) for efficiently identifying minimal winning coalitions in Online Social Networks (OSNs). We address the fundamental challenge that coalition formation is NP-hard under traditional approaches by leveraging structural properties of relational dependencies and Armstrong’s axioms to transform the problem into one solvable in polynomial time. Our framework reduces the state space from exponential O(2ⁿ) to O(n²) through a sufficient statistic representation based on coalition size, follower reach, and terminal status, while achieving O(n⁴) time complexity under deterministic, static, and sufficiently symmetric influence structures. The QLRG framework introduces three critical innovations: (1) a principled agent selection mechanism derived directly from the Q-function that eliminates heuristic weight tuning; (2) a formal Boost action defined through temporal closure operators that captures influence spread dynamics; and (3) a constrained MDP formulation that enforces relational consistency through action elimination rather than penalty terms. We prove that the Bellman optimality operator forms a contraction mapping, guaranteeing deterministic convergence to optimal policies with established rates of O(1/√k) for decreasing learning rates or linear convergence up to bias for constant rates. To bridge the gap between this idealized model and the asymmetry inherent in real OSNs, we further develop a cluster-based sufficient statistics approach. By partitioning the network into communities with bounded internal variation, we relax the global symmetry requirement while preserving polynomial state space complexity, and obtaining a single within-community swap changes the optimal Q-value by at most ${\displaystyle \frac{{\epsilon }_i}{1-\gamma }}$, which is a local Lipschitz continuity result. The implications of this are both theoretical and practical, and they form the bedrock for relaxing the global symmetry assumption in the QLRG framework. Empirical validation on synthetic networks satisfying the symmetry assumption demonstrates that QLRG consistently identifies minimal winning coalitions matching the optimal solutions found by exhaustive search, while operating with polynomial-time complexity. Unlike conventional approaches, our framework simultaneously satisfies four critical properties: deterministic convergence, policy optimality, minimal coalition identification, and computational tractability. The work bridges computational social science and operations research, providing a mathematically rigorous foundation for strategic decision-making in influencer marketing and coalition formation. While the framework requires symmetry assumptions that may only hold approximately in real-world OSNs, it establishes an idealized baseline for future extensions addressing stochasticity, dynamics, and partial observability. This research represents a paradigm shift from empirical improvements to theoretically grounded convergence guarantees for coalition formation problems, demonstrating how structural mathematical insights can transform intractable problems into efficiently solvable ones without sacrificing solution quality. Full article

This study investigates the Container Consolidation Problem (CCP), a critical operational challenge in container terminals where containers with specific attributes must be relocated during yard crane idle periods. The primary objective is to maximize yard space availability for incoming vessels by strategically grouping containers, thereby alleviating storage pressure and enhancing throughput. A mixed-integer programming model is formulated to minimize the total handling time, incorporating complex constraints related to crane availability, relocation sequencing, and slot assignment. Due to the combinatorial complexity inherent in large-scale yard operations, a comprehensive optimization framework is proposed. This framework balances computational efficiency with solution quality, offering a robust approach to solve large-scale instances within practical time limits. Computational experiments demonstrate that the proposed methodology consistently yields high-quality solutions, effectively resolving the trade-off between solution speed and optimality. The research provides not only a novel methodological perspective for solving this NP-hard problem but also offers significant practical value. By optimizing crane scheduling, the model directly contributes to reducing operational costs, improving the turnover rate of yard space, and strengthening the overall efficiency of the maritime supply chain. Full article

Rapid and robust trajectory planning for the Terminal Area Energy Management (TAEM) phase of horizontal-landing Reusable Launch Vehicles (RLVs) is critical but challenging due to large initial deviations, stringent terminal constraints, and strong model nonlinearities. To address the limitations of existing methods in convergence reliability and computational speed, this paper proposes a novel online trajectory optimization framework based on analytical lateral planning and equivalent dynamic decoupling. First, a cubic Bézier curve is employed to parameterize the lateral ground track, enabling the rapid generation of analytical expressions for the lateral states that strictly satisfy boundary constraints. Leveraging these analytical solutions, the original six-degree-of-freedom dynamics are exactly decoupled and reduced to a lower-dimensional model governing only the longitudinal motion. To further mitigate nonlinearity, the third derivative of height with respect to range is introduced as a virtual control variable, transforming the problem into a smoother form. The resulting equivalent longitudinal optimization problem is then efficiently solved using the Gauss Pseudospectral Method. Numerical simulations demonstrate that the proposed method significantly outperforms traditional approaches in computational efficiency: it generates feasible trajectories satisfying all constraints within 0.26 s (3$\sigma$ value). Furthermore, the method exhibits remarkable insensitivity to initial guesses, achieving stable convergence even with simple linear initialization. This approach provides a robust and real-time capable solution for complex TAEM trajectory optimization problems characterized by high nonlinearity and multiple constraints. Full article