中国科学院数学与系统科学研究院期刊网

19 June 2025, Volume 46 Issue 6
    

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  • LIU Xinlong, YU Yang, YU Jinpeng, PEI Hailong
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1651-1666. https://doi.org/10.12341/jssms240251
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    In this paper, the linear water wave equation is used to describe the fluctuation of an ideal water body in a two-dimensional bounded rectangular region, and the Craig-Sulem transform is used to transform the water wave equation into a linear development equation with velocity potential function and wave height as state variables. In this paper, we assume that the wave height of water wave is the measured output of the system, and on this basis, we analyze the recognizability of the water depth and velocity potential function, and design a synchronous identification algorithm to estimate the water depth and potential function from the wave level. In this paper, a numerical identification algorithm based on adjoint method is designed, which can effectively estimate both water depth and potential function. Firstly, the traditional quadratic target functional is improved to target functional with system model constraints by introducing Lagrange multipliers. Secondly, the adjoint equation of the water wave equation is derived by the Lagrange functional differential formula, and the gradient of the target functional is obtained by solving the equation. Finally, the gradient descent method is used to estimate the water depth and velocity potential functions iteratively, and the effectiveness and accuracy of the proposed algorithm are verified by numerical simulations.
  • HU Qingying, HU Chunhong, ZHAO Ziwen, GU Zhenzhen, LIU Wenqiang
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1667-1686. https://doi.org/10.12341/jssms240568
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    For a class of uncertain multi-sensor systems with uncertain noise variance and one-step random observation delay, the augmentation method is applied, by using the extended noise technique, the system is transformed into a multi-model multi-sensor system with only uncertain noise variances. According to the minimax robust estimation method, the steady-state local robust Kalman predictor is proposed. Based on the steady-state local robust Kalman predictor, the steady-state local robust white noise deconvolution estimator is proposed. By using the matrix-weighed fusion algorithm, the robust matrix-weighted fused steady-state white noise deconvolution filter and smoother are proposed. By applying the Lyapunov equation method, and introducing several permutation matrices, the robustness of the proposed local and fused white noise estimators is proved. The precision relationship between the steady-state local and the fused robust white noise estimator is analyzed. The effectiveness and correctness of the proposed method are verified by applying it to a simulation example of a multi-sensor IS-136 signal system.
  • GUO Qinghui, LI Yuan, XING Zuoxia
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1687-1700. https://doi.org/10.12341/jssms240024
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    In order to improve the effect of signal noise reduction, this paper proposes a signal noise reduction method based on optimized variational mode decomposition combined with wavelet threshold. Firstly, the improved sparrow search algorithm is used to adaptively optimize the variational modal decomposition parameters to determine the optimal modal number $k$ and the quadratic penalty factor $\alpha $. Secondly, the improved wavelet threshold denoising method is used to denoise the noisy mode, and the effective mode and denoising mode are reconstructed to achieve signal denoising. Finally, compared with the traditional threshold denoising method, the results show that the signal-to-noise ratio of the proposed method is increased by 1.604, and the root mean square error is reduced by 0.015, which has a better noise reduction effect.
  • LIU Hui, LI Xiaohua, DING Lei
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1701-1719. https://doi.org/10.12341/jssms23691
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    A new full-state constraint event-triggered asymptotic tracking control strategy is proposed for a class of non-strict feedback nonlinear systems with unknown control direction. The strategy removes the dependence on the initial states of the system in the full-state constraint control based on a nonlinear mapping and a conversion method of system. Meanwhile, a new performance constraint function is designed so that the tracking error of the system converges into the neighborhood near the equilibrium point within a finite time. To avoid an overlarge initial value of the control input, the control mode with zero initial control input is considered in this paper. A new input adjustment function is designed, which is introduced into the event triggering mechanism to ensure that the event-triggered control input of the system has a zero initial value. The designed controller guarantees the full-state constraint performance and the asymptotic tracking performance of the system, and it can let the transform errors asymptotically converge to zero. The effectiveness and the superiority of the proposed scheme are verified by the simulation studies for a manipulator system.
  • CHEN Qi, WU Libing
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1720-1733. https://doi.org/10.12341/jssms240022
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    This paper investigates the fixed-time adaptive fault-tolerant control problem for a class of nonlinear systems with actuator faults and full-state constraints. Firstly, by utilizing back-stepping control and fixed-time stability theory, a fixed-time adaptive fault-tolerant controller and parameter updated laws are designed to compensate for actuator faults and parameter uncertainties; Then, the barrier Lyapunov function is used to handle the full-state constrained problem of the system, so that the states of the system do not violate the predetermined constraint conditions; Furthermore, the proposed control scheme can ensure that all signals of the closed-loop system are bounded, the tracking error converges to the neighborhood of the origin within a fixed time and the upper bound of the convergence time is independent of the initial state of the system; Finally, the simulation example is shown to verify the efficiency of the presented strategy.
  • LI Minshuo, LIU Ao, WANG Keyao, LIU Bo
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1734-1751. https://doi.org/10.12341/jssms240247
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    To enhance supply chain efficiency, it has become a standard production mode for geographically diverse factories to collaborate in completing production tasks. Constructing scheduling models that reflecting real-world conditions and designing simple yet effective optimization algorithms are key to achieving efficient collaboration. Distributed flexible job shop scheduling problem has emerged as a promising tool for modeling and optimizing such problems. However, existing researches rarely account for sequence-dependent setup time for machines, instead simplifying the time as constant. This approximation can lead to inferior schedules, thereby impeding system efficiency. This paper establishes a mixed-integer programming model to describe the distributed flexible job shop scheduling problem with sequence-dependent setup time. A Q-learning based iterated greedy algorithm is proposed to solve it, wherein the Q-learning mechanism is utilized to dynamically select the appropriate perturbation magnitude, effectively overcoming the decline in search performance caused by unreasonable perturbation in conventional iterative greedy algorithms. By introducing the correlation between machines' setup time and operation sequences into benchmark instances for distributed flexible job shop scheduling with machine eligibility constraints, 207 instances are constructed. The proposed algorithm is compared with three iterative greedy algorithms with fixed perturbation magnitudes, simulated annealing, scatter search, backtrack search-based hyper-heuristic and random permutation descent-based hyper-heuristic. Experimental results demonstrate that the proposed Q-learning based iterative greedy algorithm achieves higher search quality and faster convergence speed.
  • LIU Aijun, XIONG Jiamin, CHAI Jian, LI Zengxian, LI Jiaxin, ZHANG Yan
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1752-1771. https://doi.org/10.12341/jssms23890
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    While the franchise-based express delivery industry has developed rapidly, there are also issues of unstable cooperation caused by conflicting interests and low service quality, which makes it difficult to satisfy the increasing demand for high-quality and high-service from consumers. To this end, this paper uses the method of evolutionary game to dynamically analyze the evolutionary stability of the courier company's regulatory strategy, the production behavior of terminal franchisees and the government's regulatory rewards and punishments strategy, and reveals the impact of different decision parameters on evolutionary stability, demonstrating the conditions for evolutionary stability. The numerical analysis results indicate that when the risk cost and profit-sharing ratio are in different threshold intervals, the game system between express delivery companies and franchisees presents four different evolutionary stability results. In addition, when formulating reward and punishment policies, the government should ensure that the sum of rewards and punishments for all parties is greater than their speculative gains, in order to ensure the standardized operation and cooperative stability of express service enterprises. The results of this paper are of great significance to the establishment of a suitable default punishment system, risk identification and early warning mechanisms, and enhancing the government's regulatory functions, while creating a favorable market operating environment.
  • CHEN Yufeng, YANG Shuo, WANG Chuwen
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1772-1793. https://doi.org/10.12341/jssms240110
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    This paper measures multiple price bubbles in the global iron ore futures market, China's stock market and industry stock markets from 2013 to 2021, and examines the risk spillover effects among markets based on bubble dates and causal relationships, in order to reveal the guiding role of industry market bubbles and the financialization characteristics of the global iron ore market. The results show that: 1) There was a continuous capital rotation relationship between iron ore and Chinese stock market in the initial stage of iron ore futures market, which has evolved into the linkage effect in recent years; 2) There is a bidirectional bubble contagion relationship between iron ore and China's stock market in the post-epidemic era, which confirms the financialization of iron ore market; 3) The bubble infection relationship is complex and dynamic, and the outburst of comprehensive market bubble often evolves from the industry stock market bubble. Once there is a bubble in some sectors, market investors tend to buy stocks that have risen relatively slowly guided by loss aversion bias, and the behavior encourages bubbles to spread from sectors that rose ahead of the market to the general market. It is of great significance to evaluate and avoid the financialization of iron ore market to maintain the stability of financial market and reduce the systemic financial risk.
  • ZHUANG Delin, WU Duyun, WANG Shuai
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1794-1812. https://doi.org/10.12341/jssms240054
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    Digital finance can provide significant financial resources for the development of the real economy, and it has a far-reaching impact on the investment decisions of enterprises. This paper aims to explore the impact of digital finance on the investment structure bias of enterprises and clarify its influence mechanism. To achieve this, we expand the research perspective to investigate how digital finance affects the relationship between enterprises' fixed asset investment and financial investment. The study, based on a sample of 22,242 data points from 3641 A-share listed companies between 2011 and 2021, concludes that the growth of digital finance has worsened the financial investment bias of these companies. This conclusion remains valid even after conducting several robustness tests. Regarding the impact mechanism, digital finance enhances the convenience of financial investment by improving the external financial environment of firms. However, it also exacerbates the financial investment bias of firms by stimulating their speculative motives to hold financial assets. Additional analysis shows that industry-level cohort effects act as a catalyst in the relationship between digital finance and financial investment bias. This paper confirms that digital finance affects the investment structure of enterprises and provides empirical evidence for the rationalization of their investment structure.
  • CHEN Shengli, LI Xinru, LUO Menghua
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1813-1831. https://doi.org/10.12341/jssms240755
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    As an important component of the modern economic system, digital finance has a crucial influence on the development of new quality productivity. Based on the panel data of 30 provinces and municipalities selected in China from 2013 to 2022, this paper uses the entropy weight-TOPSIS method to measure the development level of new quality productivity at the provincial level, and analyzes the impact effect and mechanism of digital finance on new quality productivity through the two-way fixed effect model and the mediation effect model. The research finds that digital finance significantly promotes the development of new quality productivity, and this conclusion has passed the robustness test and endogeneity treatment. In the heterogeneity analysis, it is found that this promoting effect shows differences in different regions, different innovation capabilities and different degrees of enterprise agglomeration, presenting a pattern of “Central > Northeast > East > West”, “high innovation capability > low innovation capability”, and “high degree of enterprise agglomeration > low degree of enterprise agglomeration”. The mechanism test finds that digital finance promotes new quality productivity through the positive effects of promoting the level of science and technology, improving the efficiency of resource allocation and optimizing the upgrading of industrial structure. The threshold effect analysis find that when the level of innovation output crosses the threshold value in the process of digital finance influencing new quality productivity, the promoting effect of digital finance on new quality productivity weakens, and there is a marginal diminishing effect. Therefore, this paper discusses relevant policy suggestions, providing useful ideas for the formulation of policies on promoting the development of new quality productivity by digital finance.
  • CHANG Jiajia, YANG Huawei, SHU Yadong, YU Xiaojun, HU Zhijun
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1832-1855. https://doi.org/10.12341/jssms23693
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    The paper investigates the stocking problem of multiple retailers in a competitive environment. Applying criterions of minimax absolute ex-post regret and maximin relative ex-post regret, two stocking games of multiple competing retailers are established respectively where the distribution function of the market demand is unknown and the information that each retailer holds regarding the market demand support is asymmetric. We establish the existence of the Nash equilibrium of the robust optimization, and derive an explicit closed-form expression under specific conditions. Compared with the optimal stocking strategy under the traditional maximization expected profit criterion, the performance of the two robust optimization criteria is analyzed. Results show that, when there is no customer transfer, that is, there is no market demand competition among retailers, the minimax absolute ex-post regret criterion is always superior to the maximin relative ex-post regret criterion; when there exists customer transfer, the market demand faced by each retailer interacts, the two criteria present “mixed” excellent performance. Particularly, when the information asymmetry degree is high, there is a possibility that the maximin relative ex-post regret criterion is better than the minimax absolute ex-post regret criterion.
  • YU Li'e, CHEN Jingxian
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1856-1876. https://doi.org/10.12341/jssms23854
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    Taking the supply chain system composed of a manufacturer, a foundry and a retailer as the research object, and considering the difference of consumers' quality perception, two kinds of supply chain game models are constructed to study the quality decision of retailer's private brand, which are without developing private brand and developing private brand. The results show that: developing private brand is the optimal strategy for retailer which encroaches on the profit of manufacturer; The equilibrium solution of the supply chain game model is not unique when retailer develops private brand, which depends on the proportion of high-quality perceived consumers and consumers' brand awareness; The private brand product is not “low quality and low price”, but “high quality and low price” product when the proportion of high-quality perceived consumers is large and consumers' brand awareness is strong; increasing the proportion of high-quality perceived consumers is conducive to improving the qualities of manufacturer's brand product and retailer's private brand product; when consumers have strong brand awareness, increasing the proportion of high-quality perceived consumers is beneficial to all enterprises in the supply chain.
  • ZENG Yinlian, CHEN Xue, ZENG Huantao, LI Jun, LUO Qin
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1877-1892. https://doi.org/10.12341/jssms240602
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    With the development of intelligent mobility and sharing economy, Mobility -as-a-Service (MaaS) platforms have become a key component of modern transportation systems. The success of MaaS platforms relies on data sharing between platform operators and transportation service providers. However, balancing the degree and quality of data sharing and coordinating the interests of all parties remain significant challenges. This paper aims to analyze the data sharing issues in the MaaS model, focusing on the impact of both the degree and quality of data sharing on system efficiency. To achieve this, a transportation system consisting of a MaaS platform operator and a transportation service provider is constructed, and differential game theory is used to analyze the optimal strategies and payoffs of participants in three different game scenarios. The results show that when the income distribution coefficient meets certain conditions, a Pareto improvement is achieved for both participants and the whole transport system from the Nash non-cooperative game to the Stackelberg leader-follower game, and further to the cooperative game. Furthermore, subsidy incentives and cooperative mechanisms can effectively improve the data sharing efficiency between MaaS platform operators and transportation service providers, thereby increasing the payoffs of both parties and the overall system.
  • YANG Peng
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1893-1912. https://doi.org/10.12341/jssms240370
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    This paper studies the reinsurance contract formulation problem under the game between two competitive reinsurers and one insurer. The insurer is engaged in two kinds of dependent insurance business, and the dependence is characterized by the number of claims and the amount of claims. The insurer signs reinsurance contracts with different reinsurers for the two kinds of insurance business. For these two kinds of insurance business, we don't restriction the reinsurance type, and the specific form of the reinsurance is determined by solving the stochastic optimization problem considered by the insurer and two reinsurers. Through relative performance, we quantify the competition between the two reinsurers, and establish a non-zero-sum stochastic differential game between them, and then establish a leader-follower stochastic differential game between the insurer and the two reinsurers. Under the mean-variance criterion, the explicit optimal reinsurance contract, i.e., the insurer's optimal claim risk sharing strategy and the two reinsurers' optimal reinsurance pricing strategy, is obtained by using stochastic analysis and stochastic control technology. Finally, the influence of model parameters on the optimal reinsurance contract is explored through numerical experiments.
  • YUAN Xiaoyong, MA Ruyu
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1913-1928. https://doi.org/10.12341/jssms240041
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    The rapid development of e-commerce in recent years has provided opportunities for agricultural development, not only widening the marketing channels for farmers, but also providing new financing channels for farmers. This paper considers an agricultural product supply chain composed of a farmer with limited funds and e-commerce platform. The farmer sells his agricultural products through e-commerce platform, and the platform acts as an intermediary to collect commission and implement promotion means to attract consumers. Based on bank financing and platform financing, this paper explores the optimal decisions and profits of the farmer and the platform under the Stackelberg game and Nash game, and conducts comparative analysis. It is found that factors such as loan interest rates and output risk have a significant impact on the choice of financing models for supply chain members. When certain conditions are met, the production input of farmers under platform financing is higher and supply chain members can obtain more profits. Meanwhile, compared to the Nash game, the profits of supply chain members under the Stackelberg game will be higher.
  • SHAO Feng, CHEN Zibo, SHAO Hu, LIU Pengjie
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1929-1945. https://doi.org/10.12341/jssms240192
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    In this study, we employ a hybrid self-adaptive technique to introduce a new Dai-Liao type conjugate parameter for solving unconstrained optimization problems. Subsequently, we incorporate an adjustable parameter as the spectral parameter to construct an effective search direction and suggest a Dai-Liao hybrid conjugate gradient method. Its search direction satisfies the sufficient descent property regardless of the line search. Under some fundamental assumptions and weak Wolfe line search, we establish the strong convergence for the uniformly convex function and global convergence for the general function. Moreover, under Armijo line search, for the uniformly convex function, we investigate the complexity analysis of the proposed method. In attaining a point with a gradient norm below $\epsilon$, it requires at most $O(\epsilon^{- 2})$ function evaluations. Finally, some numerical experiments show that our method outperforms some existing methods on solving unconstrained optimization problems and image restoration problems.
  • XU Leiyan, MENG Zhiqing, WANG Hui
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1946-1965. https://doi.org/10.12341/jssms23904
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    The distributed robust multi-objective bilevel programming based on worst-case conditional risk value (WCVaR) is a bilevel decision model consisting of multiple loss objective functions for upper and lower level decisions. Firstly, the concave probability density function clusters formed by multiple perturbation convex sets of upper level and lower level decisions are defined separately. Given the multiple loss objective functions corresponding to upper level and lower level decisions, robust VaR values and robust WCVaR values corresponding to the entire disturbance set are defined. Then, a distributed robust multi-objective bilevel programming based on WCVaR with concave probability density function clusters is constructed under certain conditions, it has been proven that the model of distributed robust multi-objective bilevel programming based on WCVaR is equivalent to distributed robust multi-objective bilevel programming with perturbed parameters. Furthermore, in the case where both the upper and lower layers are finite mixed probability density function clusters, it is proved that the corresponding WCVaR based distributed robust multi-objective bilevel programming is equivalent to another distributed robust multi-objective bilevel programming with finite multiple constraints. In this case, when all objective and constraint functions are linear functions, the equivalent programming can be transformed into an approximate bilevel linear programming problem. Finally, a WCVaR based double-layer robust decision model for multi product production and ordering under periodic fluctuations with upper and lower level decisions is established. Numerical experiments have shown that this model can obtain robust production ordering strategies for both upper level and lower level decisions, avoiding losses caused by overproduction or underproduction in the supply chain.
  • WANG Yulong, GUO Xueying, ZHANG Xingong, CHEN Xin
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1966-1975. https://doi.org/10.12341/jssms240154
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    This paper considers a single-machine scheduling problem with slack due date assignment, where the objective function is to minimize an objective function consisting of penalties for both early work and late work, we prove the $NP$-hardness of this problem and provide a pseudo-polynomial time algorithm with a time complexity of $O(n^2P(P-p_{\max}))$. Furthermore, we also consider two special cases of minimizing total late work under slack due dates: Identical processing times and identical penalty coefficients. For these two problems, we propose polynomial time algorithms with a time complexity of $O(n\log n)$, respectively.
  • LIU Yun, LIU Yong, WANG Zhucheng, ZHAO Huanhuan
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1976-1991. https://doi.org/10.12341/jssms23809
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    The research and development of complex equipment core technology is a multi-agent collaborative innovation activity. However, innovation subjects often invest less effort and cooperation than the requirements of the research and development system due to different purposes, which directly affects the progress and efficiency of complex equipment core technology research and development. In order to mobilize the enthusiasm and initiative of the innovation subject, it is urgent to build an effective incentive mechanism. In view of this, considering that the degree of effort, the degree of cooperation and the level of core technology are important factors that affect the research and development of core technology of complex equipment, from the perspective of cost sharing, using game analysis technology, this paper constructs the interest model of core technology research and development of complex equipment, analyzes the optimal decision-making under the cooperative, non-cooperative and incentive situations, and then designs the incentive mechanism of core technology research and development. The validity and rationality of the model are verified by an actual case. This research has found that the cost sharing incentive mechanism designed in this paper can effectively solve the incentive problem of the research and development system, and the improvement of the core technology level of both the main mechanism and suppliers can directly increase their own profits, further increase the cost sharing ratio of the main manufacturers, and promote the development of the research and development system.
  • LU Jincheng, ZHUANG Hua, ZHOU Qin
    Journal of Systems Science and Mathematical Sciences. 2025, 46(6): 1992-2010. https://doi.org/10.12341/jssms241072
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    The inverse data envelopment analysis (inverse DEA) method under the variable frontier assumption can effectively analyze the resource allocation and output optimization issues of decision-making units (DMUs) in future production periods, considering the impact of technological changes on the frontier. However, the existing methods only discuss the single case of frontier changes, ignoring the complexity of technological changes, which may lead to the diversity of frontier changes. To solve this problem, the paper first reveals the reasons for the problems of the existing methods through in-depth analysis. Then, it proposes a variable frontier inverse DEA method under the assumption of technological diversity changes, and discusses the different scenarios of future production period frontier changes reflected by the proposed method and their related properties. Finally, through comparative analysis, the advantages of this method are demonstrated, and it is applied to the research on the planning of development goals for China's high-tech industries. The results show that the proposed method can effectively reflect the diversity of frontier changes and the potential changing trends of the frontier, and can provide reference for decision-makers to formulate relevant development plans.