Global public health emergencies, particularly, infectious disease outbreaks, are occurring with increasing frequency and, imposing multifaceted challenges on healthcare systems, including staff-scheduling imbalances, elevated infection risks, and substantial uncertainty in patient demand. To address these challenges, a multi-objective robust optimization model for cross-regional allocation of healthcare staff is developed in this study. The model explicitly accounts for demand uncertainty and infection-risk constraints while optimizing rescue efficiency, fairness, and penalties for unmet demand. Our results demonstrate that (1) introducing cross-regional coordination markedly improves resource-allocation efficiency; (2) incorporating infection-risk constraints prevents overestimation of workforce availability and provides practical benefits; (3) scheduling policies must adapt dynamically to evolving epidemic phases and policy objectives; and (4) under high demand uncertainty, the robust model substantially improves schedule stability and reliability. By incorporating infection risk as a key decision constraint, our framework provides quantitative and actionable guidance for the adaptive scheduling of healthcare staff during complex, rapidly evolving infectious disease outbreaks.
The Baltic Capesize Index (BCI) reflects freight rate fluctuations of major dry bulk commodities and is highly sensitive to emergency-induced shocks. These emergencies differ markedly in impact intensity and duration, and even emergencies within the same category can generate heterogeneous BCI responses that cannot be adequately captured by category-based classification or frequency-based analysis alone. In this paper, emergencies are further classified into significant events and important events. We develop an Ensemble Empirical Mode Decomposition (EEMD)-based event-oriented framework that extends decomposition beyond frequency-based dynamics, complementing existing studies by explicitly integrating event-response characterization. By applying Fine-to-coarse Reconstruction, symbolic sequence procedures, and definite integrals to quantify cumulative effects, the approach enables the measurement of feedback direction, feedback time, individual fluctuation period, and fluctuation amplitude based on the classification of significant events and important events, and it reveals how route-level freight rate responses are aggregated into index-level volatility. The study demonstrates the following findings: (1) The fluctuation of the BCI is primarily composed of short-term market fluctuations, which result from supply-demand imbalances and the impact of important events, significant events, and the intrinsic long-term trend. (2) The BCI exhibits shorter feedback times to significant events and negative feedback events. (3) The individual fluctuation periods of important events are shorter, averaging 34 trading days. (4) The BCI fluctuates more in low-frequency modes under significant events. These findings assist firms and government departments in managing the risks arising from BCI volatility while enhancing the BCI’s predictive and early-warning capacity, thereby contributing to economic security.
In the digital era, firms implement advanced digital technologies to pursue enhanced performance. Drawing on resource orchestration theory, this study proposes two novel resource orchestration patterns: fit as moderation and fit as matching between supply-side digitalization and process integration. These patterns help us to understand the synergistic effects between the two factors, making the upstream digitalization of manufacturers valuable. Using survey data collected from 200 firms in China, this study employed stepwise multiple regression and group regression to test the proposed theoretical model. This study revealed that fit as moderation directly promotes supply-side resilience, whereas fit as matching harms supply-side resilience. Furthermore, we consider the moderating effects of supply-side relational ties and supply uncertainty. Supply-side relational ties weaken the effect of fit as moderation but mitigate the negative effect of fit as matching, whereas supply uncertainty strengthens the effect of fit as moderation but does not significantly moderate the effect of fit as matching. This study contributes to the literature by determining the synergistic effects of supply-side digitalization and process integration on supply-side resilience and how they vary across different relational and environmental conditions.
Online medical crowdfunding helps patients raise funds for medical expenses through small donations from a large number of donors thus it plays an important role in treating major diseases and alleviating poverty. However, limited attention has been paid to the poverty alleviation effect of online medical crowdfunding. Using a panel dataset of 301 administrative units at the prefecture level and above in China from 2016 to 2020, this study empirically investigates the direct impact of online medical crowdfunding on regional poverty rates. In addition, this study explores the moderating role of health poverty alleviation policies and regional economic development level. The findings reveal that online medical crowdfunding has a significant effect on alleviating poverty. The implementation of health poverty alleviation policies weakens the poverty alleviation effect, while regional economic development does not appear to moderate this effect. This study offers important theoretical contributions and practical implications for poverty alleviation and online crowdfunding.
Business-to-consumer (B2C) online retailing, including omni-channel models, has become a dominant retail format, and effective order fulfillment has emerged as a key determinant of service quality, operational efficiency, and customer satisfaction. This study comprehensively reviews order fulfillment operations in B2C online retailing, emphasizing the unique logistical characteristics of this context: large assortments, multi-location fulfillment networks, personalized multi-item orders, and tight delivery time windows. We develop a practice-oriented taxonomy of operational decisions that mirrors the end-to-end order fulfillment process, covering order assignments, warehouse, shipping, and return operations. By synthesizing findings across these decision areas, this review reveals how recent advances in optimization and real-time decision-making address the complexity of modern e-commerce fulfillment. Based on these findings, this study highlights practice-driven challenges in B2C order fulfillment, examines how emerging data- and AI-driven methodologies can address them, and synthesizes a domain-specific research agenda across major operational decision areas, thereby providing a coherent roadmap for future research.
We propose a newHigh-Frequency-Based Volatility (HEAVY) Generalized Autoregressive Score (GAS) model with Autoregressive Conditional Density (ARCD) estimation for examining the joint dynamics of fat-tailed and skewed daily returns and realized covariance matrix observations. The proposed HEAVY GAS-ARCD models extends the classical (HEAVY) models. The score dynamics for the unobserved true covariance matrix and shape parameters are specified and formulated by assuming a flexible density (Wishart or matrix-F distribution) for the realized covariance measures and a multivariate skewed Student’s t distribution for the daily returns. The class of score models encompasses many realized volatility score models. The proposed models are estimated by using the maximum likelihood estimation (MLE) method. Furthermore, the parameter restrictions for positive definiteness are formulated, conditions of consistency and asymptotic normality are presented, and Monte Carlo simulation experiments are used to study the precision of MLE. In the application, two group data have been used from the portfolio of U.S. financial assets. The performances of the HEAVY GAS-ARCD models are compared with many realized volatility score benchmarks. Both in-sample and out-of-sample forecasting results support the use of the proposed HEAVY GAS-ARCD models.
In this paper, we investigate parking permit management in a many-to-one electric-vehicle (EV) corridor network in which each corridor is composed of a road-bottleneck path, a charging-bottleneck path and a parallel transit path. First, since the parking lot is available for multiple paths within a corridor, we show that the parking ending time for EV drivers on each path is a strictly increasing function of the number of parking spaces allocated to them under endogenous travel demand, which allows us to successfully analyze the user equilibrium in the one-to-one EV corridor. The problem is then formulated as a mixed-integer linear program (MILP). Second, the traffic pattern in the many-to-one corridor network considering parking space constraint is obtained, and a new bisection method to compute the equilibrium is proposed. We also theoretically analyze and numerically demonstrate the impact of network size on the computational efficiency of the algorithm. Third, we propose and compare the efficiencies of various schemes to eliminate both intra-corridor and inter-corridor competition for parking, such as system optimum parking permit allocation, Pareto improving parking permit allocation and tradable parking permit. Analytical and numerical examples are carried out to present that parking permits are extremely effective in traffic management of EVs.
Achieving equitable health resource allocation under Universal Health Coverage (UHC) requires reconciling complex trade-offs between healthcare cost containment, clinical efficacy, and patient financial protection. To address this challenge, we propose a dual-perspective framework integrating societal and household-level disease burden optimization, with depression—a leading global disability cause—as the case study. Our bi-objective model prioritizes evidence-based antidepressant therapies for inclusion in national health benefit packages, thereby guiding insurance reimbursement policies and clinical practices to optimize resource allocation across macroeconomic and microeconomic dimensions. Given the model’s high-dimensional non-convex characteristics, we developed the Triple-Population Cooperative Particle Swarm Optimization for BDD (TPCPSO_BD), an advanced swarm intelligence-based computational tool integrating two key methodological advancements (triple-population partitioning for cooperative optimization and an adaptive particle correction mechanism that enhances convergence efficiency while maintaining solution diversity). Validated using real-world data from City S, China, TPCPSO_BD demonstrates superior performance in accuracy, efficiency, and policy impact (48.97% reduction in household out-of-pocket expenditures through optimized reimbursement schedules). This methodology establishes a transferable decision-support system for mental health financing in low- and middle-income countries, advancing Sustainable Development Goal 3 through technical innovation in handling non-linear health-economic interactions, dynamic prioritization of cost-effective interventions, and explicit incorporation of household financial risk protection, thereby providing a robust mechanism to promote sustainable healthcare equity.
Improving the innovation capability of new product development (NPD) projects is crucial to ensuring their competitiveness. Although the roles of internal and external supply chain integration (SCI) have been identified and empirically examined, how SCI influences innovation capability needs further exploration, especially considering organizational complexity (OC), a prominent feature of NPD projects. This study reveals the mechanism through which three types of integration influence two types of innovation capability under the boundary condition of OC. Drawing on dynamic capability theory, we empirically examine the proposed moderated mediation research model using data from 239 NPD project leaders. The results show that all three types of integration contribute to both types of innovation capability through dynamic capability. Moreover, OC strengthens the indirect effects of the three types of integration on incremental innovation capability (IIC) via dynamic capability. Contrary to our hypotheses, OC has nonsignificant moderating effects on the indirect effects of the three types of integration on radical innovation capability (RIC) via dynamic capability. Theoretical and practical implications are discussed.
Frame-structure prefabricated underground stations (FPUSs) are becoming increasingly prevalent in low-carbon urban development. However, existing decision-making methods for FPUS construction modules still lack diverse evaluation approaches and fail to balance structural performance with construction time effectively. This study investigates assembly-module selection for FPUSs in Shenzhen Metro Phase V and proposes a performance–time dual-control assembly-module decision-making framework (PTADF). First, various assembly-module schemes were developed and assessed using finite element analysis (FEA) to evaluate their influence on FPUS performance. Subsequently, the PTADF was established to analyze construction time consumption and variations in structural performance during FPUS construction. Finally, the performance–time combined indicator was applied to quantitatively evaluate the alternative schemes and determine the optimal assembly-module scheme. The results indicate that: (1) The beam-slab module enables efficient construction and effective deformation control of the FPUS enclosure structure. The precast walls provide the primary lateral resistance, whereas the frame system contributes to structural flexibility. The use of hinged joints for assembly does not compromise FPUS performance. (2) The beam-slab module scheme significantly reduces the total construction time by 50%–60% compared with the other schemes. (3) The total construction time of the beam-slab module with hinged joints is approximately 75% of that required by the rigid- and semi-rigid-joint schemes. This study provides a valuable reference for applying FPUSs in low-carbon metro construction in Shenzhen and offers practical support for the broader adoption of FPUS technology.
The dynamic pricing mechanisms of firms and search techniques for historical prices have spurred strategic behaviors and price expectations among customers. These customers predict future markdowns and delay their purchases based on price expectations (i.e., reference prices), where joint pricing and ordering decisions should be considered carefully to counteract or soften the negative impact of customers’ strategic behaviors and their reference prices on the seller’s profitability. Moreover, relevant literature neglects general and important features in the actual market (e.g., nonzero price thresholds and randomness in the formation of reference prices). In this study, we establish a Markov game between the retailer and strategic customers over a multi-period horizon, and set the reference price as a three-regime linear piecewise model with loss and gain thresholds. Then, we propose an adaptive multi-agent deep deterministic policy gradient (MA2DDPG) algorithm containing the experience replay buffer separation mechanism and delayed actor updates. Experimental results with synthetic and real-world datasets reveal that our algorithm converges to optimal policies in terms of convergence and rationality, and that it vastly outperforms the benchmark algorithms. Moreover, valuable managerial insights are obtained through an extensive numerical analysis for practitioners, particularly when they must consider the complicated characteristics of heterogeneous customer populations, such as disappointment behaviors, price thresholds, and strategic customer proportions. Our results pave the way for future research aimed at using multi-agent reinforcement learning to improve operations management with behavioral factors.
This study identifies a novel form of the crowding-out effect. While real estate investment has long been considered a major contributor to local tax revenue, many rapidly growing economies, such as China, rely heavily on it. This research reveals new findings with both theoretical and practical significance. Using a series of general equilibrium models, we demonstrate a significant relationship between real estate investment and local tax revenue. Specifically, the ratio of real estate investment to local GDP shows a negative correlation with both the ratio of local tax revenue to GDP and their respective growth rates. These results confirm that real estate investment crowds out local tax revenue relative to GDP.
This study examines the impact of credit-based financial policy on corporate technological innovation. The 2007 corporate bond issuance policy of the Chinese government allowed firms with higher market credit ratings to raise funds through bond issuance. We adopt a multi-period difference-in-differences (DID) identification strategy and find robust evidence that enabling firms to access bond financing significantly enhances their innovation performance. The mechanism operates by improving firms’ debt maturity structure and alleviating short-term financing pressure. Additionally, this study confirms that bond issuance can enhance external monitoring and decrease information asymmetry between external investors and issuing firms, thereby promoting corporate innovation. Furthermore, the effect of bond issuance is more pronounced for firms with higher dependence on external financing, and it significantly improves the innovation performance of non-state-owned enterprises (non-SOEs). Overall, this study enriches the literature on credit-based bond issuance policies and technological innovation, and provides policy implications for improving China’s corporate bond market to support innovation-driven development.
This paper investigates the problem of multi-skilled worker assignment and production scheduling in seru production systems with precedence constraints among product operations, with the aim of satisfying customer demand. To address this problem, we first formulate a mixed-integer programming model that minimizes the total completion time of all orders, in line with the quick-response objective of seru production systems. Given the inherent complexity of this problem, we further develop a two-stage heuristic algorithm based on simulated annealing, referred to as SABH. To evaluate the performance of the proposed algorithm, we conduct a series of computational experiments on randomly generated instances with varying levels of worker heterogeneity. The computational results demonstrate that SABH is capable of producing high-quality solutions. In addition, sensitivity analyses on worker configurations are carried out to verify the practical applicability of the proposed model.
The growing prevalence of deceptive low-quality products exacerbated by the expansion of online commerce has prompted high-quality manufacturers to reconsider their counterstrategies. This study examines a supply chain with a high-quality manufacturer and a retailer that sells a mixture of high-quality and low-quality products, falsely labeling them as high-quality. Game-theoretic models are developed to explore the effectiveness of adopting the manufacturer’s official livestream channel as a countermeasure against deceptive low-quality products. Then, how adopting an official livestream channel impacts stakeholders’ payoffs is examined. Our key finding indicates that the manufacturer can effectively resist deceptive low-quality products by adopting an official livestream channel, provided that the manufacturer focuses on educating consumers while avoiding excessive information disclosure in livestreams. It can also boost the manufacturer’s profit if the marginal cost of adopting a livestream channel is low and the level of information enhancement is sufficiently low or sufficiently high. Contrary to common belief, we find that adopting the manufacturer’s official livestream channel does not necessarily disadvantage the retailer. Under certain conditions, livestream channel adoption creates a win-win-win scenario for the high-quality manufacturer, the retailer, and the consumers.
This paper investigates the strategic interaction between platform pricing and inter-manufacturer capacity sharing in a platform-based supply chain, where two competing manufacturers with asymmetric capacities use a third-party platform to facilitate capacity transactions. This study examines how platform pricing shapes manufacturers' capacity sharing strategies, downstream competition, and equilibrium outcomes. Although capacity-sharing platforms are rapidly proliferating in manufacturing, prior literature largely overlooks the strategic interplay between platform governance and manufacturers' competitive behavior. This research fills the gap by providing a systematic theoretical account of how platforms can proactively design pricing to steer co-opetitive manufacturers toward distinct equilibrium regimes, offering actionable insights for platform managers, manufacturers, and policymakers. We develop a game-theoretic model involving a capacity-sharing platform, two competing manufacturers with asymmetric capacity constraints, and a downstream retailer. Using backward induction and Karush-Kuhn-Tucker (KKT) conditions, we analyze four scenarios defined by the binding status of each manufacturer's capacity constraint, with extensions to profit-sharing contracts and alternative retail structures. The analysis reveals two distinct equilibria: one where only the capacity demander's constraint binds, and another where both manufacturers' constraints bind, with the transition governed by the capacity-sufficient manufacturer's capacity level relative to a threshold. We uncover a non-monotonic relationship between platform service fees and market competition-higher fees alleviate competition in the first regime but intensify it in the second. Under profit-sharing contracts, the platform can transform into a two-sided market by charging asymmetrically differentiated fees, thereby enhancing profitability. Our findings offer regime-dependent pricing guidance for platform managers, highlighting that effective platform governance requires diagnosing the underlying capacity structure before setting prices. Manufacturers gain insights into how platform fees affect their competitive positions, while policymakers are reminded that platform intermediation does not automatically resolve capacity allocation inefficiencies; its effectiveness depends on aligning platform incentives with operational constraints. (c) 2026 Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
We consider dynamic competition between two platforms in a market with network externalities. In a framework with exogenous connectivity, there exists a unique symmetric equilibrium point when the strength of network externalities is lower than a threshold. When the strength exceeds an upper threshold, one platform captures almost the entire market at the equilibrium state. Furthermore, we analyze the role of endogenous connectivity: We find that the market share of any platform will not exceed the golden ratio52 , and social welfare is improved when connectivity cost is low. (c) 2026 China Science Publishing & Media Ltd. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
This study investigates how ambiguity surrounding correlation coefficients and drift rates influences individual trading behavior in a continuous-time model. We derive decision-making processes under both risk and ambiguity, demonstrating that non-participation in the market arises from the rational choices of ambiguity-averse na & iuml;ve investors. The degree of correlation ambiguity between two risky assets determines the equilibrium investment policies of na & iuml;ve investors. In equilibrium, when the correlation coefficient is sufficiently high, sophisticated investors optimally short the asset with the lower Sharpe ratio while taking a long position in the other. Drift rate ambiguity exerts a downward effect on equilibrium prices. However, its impact on equilibrium positions depends on the relationship between the correlation coefficient and the ratio of the Sharpe ratios of the two risky assets. (c) 2026 China Science Publishing & Media Ltd. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Carbon leakage traditionally refers to the relocation of production to less-regulated regions to reduce carbon costs, which has prompted the adoption of additional carbon regulations such as carbon tariffs and carbon allowances. However, this policy portfolio may also trigger reverse carbon leakage, a relocation behavior in which firms move production to regulated regions to exploit policy arbitrage. We develop a game-theoretic model with dynamic carbon allowances to analyze the interaction among firms' location choices, technology improvement, and regulatory policies. The results show that although a high carbon tariff induces reverse carbon leakage, total emissions increase because production expansion outweighs the reduction in emission intensity. Furthermore, technology improvement unexpectedly intensifies the increase in total emissions through a free-ride effect under the dynamic allowance mechanism, which lowers the tariff threshold for reverse leakage. Additionally, although reverse carbon leakage improves consumer surplus, it may reduce overall social welfare when environmental damage exceeds economic gains. Therefore, regulators should mitigate reverse carbon leakage by setting relatively low carbon tariffs and carbon allowances simultaneously, particularly in industries where technology improvement is possible. (c) 2026 China Science Publishing & Media Ltd. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Climate change poses significant challenges to ecosystems, economic development, and human health, positioning adaptation as a critical component of global climate risk management. Here we provide a systematic review on the economics of climate adaptation, synthesizing advances, frontier debates, and research priorities. Through a bibliometric analysis of 6248 publications from 1978 to 2025, we trace the evolution of research trends and knowledge structures. We then evaluate core methodological approaches, highlighting their strengths in capturing behavioral responses, estimating causal effects, and modeling dynamic optimization, while noting a shift from static frameworks to integrated, multi-method analyses. Empirical findings reveal diverse adaptation strategies: households adapt through consumption changes, agricultural practices, and migration, whereas firms respond via product diversification, relocation, supply chain reconfiguration, and technological innovation—responses mediated by resource endowments, institutional frameworks, and behavioral constraints. Theoretical progress clarifies the interplay between adaptation and mitigation, the role of adaptation assistance in fostering global climate cooperation, and the welfare effects of market-driven strategies. Future research should prioritize the empirical identification of heterogeneous adaptation behaviors; focus on vulnerable regions; and long-term dynamic modeling, alongside theoretical progress in damage function specification, uncertainty modeling, the integration of social and political economy, network-based approaches, and methodological innovation.
