With the rise of online shopping， shipping services provided by online retailers have become a key factor in consumers’ purchasing decisions. To accelerate the purchase of consumers， retailers often use contingent free-shipping offers as a marketing strategy. Under this model， consumers incur no shipping charge and enjoy free shipping as long as their order value exceeds a certain threshold； otherwise， they are charged a shipping fee. Consumers， being strategic， are motivated to buy additional items to qualify for free shipping. Therefore， it is crucial for retailers to determine the optimal online shipping strategy， considering the strategic behavior of consumers. Using a theoretical model between consumers and online retailers， both paid shipping services and contingent free-shipping services are analyzed， taking into account consumer behavior. How these two shipping options affect retailers’ decisions and identify the optimal shipping strategy is explored. The results show that retailers’ optimal shipping service depends not only on the fraction of consumers who have a high degree of recognition to merge but also on the price of the shipping service. To be specific， when there is a small fraction of consumers who have a high degree of recognition to merging， retailers will choose the contingent free-shipping service to consumers only if the price of the shipping service is high； otherwise， the paid shipping service. However， when there is a large fraction of consumers who have a high degree of recognition to merge， retailers will choose the contingent free-shipping service no matter how the price of the shipping service will be. Furthermore， retailers may set a higher threshold of free-shipping when the price of shipping service is lower. The results highlight that contingent free-shipping is not just a way for retailers to reduce the pressure of shipping expenses but a strategic tool that allows them to exploit consumer behavior to their advantage.

Gold， possessing the dual characteristics of a commodity and a monetary asset， has assumed an increasingly pivotal role in global financial markets. Its price dynamics are profoundly influenced by a wide array of macroeconomic indicators and geopolitical developments， rendering the gold futures time series highly volatile， nonlinear， and non-stationary. These intrinsic properties present significant challenges for accurate short-term forecasting. In response to these challenges， an advanced deep learning framework is proposed， termed KANsLTformer， aimed at effectively capturing the intricate temporal dynamics and multi-scale structures inherent in gold futures price series. The objective is to enhance both prediction accuracy and model robustness in the face of complex market behaviors. At the core of KANsLTformer lies a novel Temporal Convolutional Gated Linear Unit （TCGLU）， which combines temporal convolution with a gating mechanism to selectively extract and emphasize relevant short-term features. This design enhances the model's capacity to focus on local temporal patterns crucial for high-frequency market fluctuations. Building upon this foundation， the framework integrates a customized Transformer architecture that learns global dependencies and dynamic attention patterns across time， enabling the model to capture long-range interactions effectively. Furthermore， the inclusion of the Kolmogorov-Arnold Networks （KANs） module facilitates the modeling of nonlinear feature interactions through KANs， while Long Short-Term Memory （LSTM） units are employed to retain and utilize long-term historical information， further enriching the temporal representation. Together， these components form a synergistic hybrid architecture capable of modeling both high-frequency and low-frequency patterns and adapting to abrupt market changes. The model is empirically validated using real-world gold futures data， encompassing core trading variables such as open， high， low， and close prices， trading volume， and open interest. The evaluation framework encompasses diverse market conditions， including both stable and highly volatile regimes， and employs time-series-specific validation techniques along with robustness assessments under artificially introduced noise. Comparative experiments benchmark KANsLTformer against several comparative models， including ARIMA-GARCH， XGBOOST， LSTM， and CNN-Transformer. The proposed approach significantly outperforms baseline models， reducing Mean Absolute Error （MAE）， Symmetric Mean Absolute （SMAPE）， Root Mean Square Error （RMSE）， and Median Absolute Error （MedAE）. Compared with its strongest ablation variant， it lowers MAE by approximately 43.5% and achieves a Directional Accuracy （DA） of 63.69%， demonstrating the contribution of each module. The superiority of KANsLTformer is further validated through Diebold-Mariano （DM） tests， which confirm the statistical significance of its predictive improvements over competing models at the p<0.001 level under both noise-free and noisy conditions. Notably， under noisy conditions， the model maintains a DA of 58.03%， consistently outperforming benchmarks with statistically significant DM test values. Ablation studies underscore the indispensable contributions of the TCGLU and LSTM components in capturing short-term volatility and long-term dependencies， respectively. The complete configuration of KANsLTformer demonstrates superior stability， robustness， and generalization performance， particularly in volatile market scenarios. In conclusion， a robust and generalizable framework is introduced for financial time series forecasting， offering practical implications for investors， financial analysts， and policymakers engaged in gold futures trading. Beyond its application to the gold market， the methodological advancements presented herein contribute to the broader domain of deep learning-based time series modeling， opening new avenues for future research in financial prediction and decision support systems.

The high volatility of Chinese stock market presents significant challenges to its sustainable development. Futures-spot hedging strategies are widely recognized as an effective solution for mitigating stock price risks. Therefore， it seeks to develop a novel and effective model for estimating futures-spot hedge ratios， thereby enabling investors to reduce risks and enhance returns. Considering the presence of substantial noise and multifractal characteristics in futures and spot markets， a multi-wavelet denoising and fractal scale-amplitude dual integration method is introduced for estimating stock futures hedge ratios. This method addresses market noise while fully leveraging the values of multiple time scales and fluctuation amplitudes. It begins by reducing data noise by a multi-wavelet approach and then employs the MF-DCCA （Multifractal Detrended Cross-Correlation Analysis） method to capture multifractal characteristics in futures-spot dependence structure. This process calculates hedging ratios across both volatility amplitudes and time scales. Using a swarm intelligence optimization algorithm， with return maximization and variance minimization as multi-objective functions， this method further integrates the hedging ratios across multiple scales and multiple fluctuation amplitudes sequentially to obtain the dual-integrated hedging ratio. Drawing on the prices from January 3， 2017， to June 14， 2024， totaling 1，808 trading days， this novel method is applied to estimate hedge ratios for the CSI 300 futures and spot markets. Empirical results indicate that， compared to single-wavelet methods， the proposed multi-wavelet denoising approach is more effective at reducing noise and demonstrates superior stability. The findings underscore the critical importance of addressing noise in hedging modeling process. Moreover， the CSI 300 futures and spot markets exhibit significantly multifractal characteristics， with notable variations across different time scales and fluctuation amplitudes. Additionally， the proposed model surpasses control methods in most cases， achieving higher returns， variance reduction ratios， and return-to-variance ratios， thereby delivering the optimal hedging effectiveness. The current paper provides a novel hedging theoretical methodology and offers a new risk-management insight.

Auction is a mechanism to allocate market resources and tap the value of commodities. With the development of the Internet and the gradual rise of e-commerce， online auctions have become an important part of auctions. With the rise of online auctions， a hybrid mechanism that combines auctions and sales has emerged on various auction sites， that is， buy-it-now auctions. There are three types of buy-it-now auctions： fixed buy-it-now auctions， temporary buy-it-now auctions and lasting buy-it-now auctions. The lasting buy-it-now auctions discussed in this article include auction and buy-it-now at the same time. Bidders can choose bidding option or execute buy-it-now. After bidders choose the auction， the buy-it-now option will not disappear. Subsequent bidders still have two options： auction and buy it now. The current discussion on buy-it-now auctions is aimed at single-item auctions， and does not take into account the situation of multiple items in a buy-it-now auction. Based on the independent private value model， the multi-unit Vickrey auction mechanism and the lasting buy-it-now auction are combined to establish a multi-item lasting buy-it-now auction model. By analyzing the expected returns of the bidder and the seller， the bidder's equilibrium bidding strategy and the seller's optimal buy-it-now price are obtained， and through numerical analysis， different strategies are given for both auction bidders and sellers to conduct multi-item lasting buy-it-now auctions online and offline on-site lasting buy-it-now auctions according to the number of items.The number of bidders in offline auctions is fixed， while the number of bidders in online auctions is not fixed. For this situation， numerical analysis is carried out on the number of auction items in the online auction situation and the offline auction situation respectively. The following conclusions can be drawn： With the increase of their own valuation， the ability of the bidder to accept the buy-it-now price increases， and the bidder has a higher willingness to accept the buy-it-now price option. As the reserve price set by the seller increases， the bid becomes less attractive to bidders who prefer to choose the buy-it-now option. As the commission rate set by the auction platform increases， the willingness of bidders to bid decreases， making it easier to choose a buy-it-now price. When the seller reasonably sets the buy-it-now price， it can improve its own income compared to when the buy-it-now price is not set. During offline auctions， the bidder chooses the lowest bid price， the best bid price set by the seller， and the seller's highest expected income per item all decrease with the increase in the number of items. In online auctions， the bidder chooses the lowest bid price， the best bid price set by the seller， and the seller's highest expected return per item all increase with the increase in the number of items.Based on the above conclusions， the following suggestions are put forward to the auction platform： The commission rate set by the auction platform for bidders should be reasonable. If the setting is too high， it will reduce the attractiveness of bidders for bidding， reduce the number of bidders’ participation， and make bidders’ bidding strategies more conservative and further reduce platform revenue. The commission rate set by the auction platform for bidders should be reasonable. If the setting is too high， it will reduce the attractiveness of bidders for bidding， reduce the number of bidders’ participation， and make bidders’ bidding strategies more conservative and further reduce platform revenue. The auction platform should make suggestions for online auctions or offline auctions according to the number of auction items of sellers， and increase the amount of commissions collected by the platform by increasing the expected income of the sellers. When the auction platform conducts offline auctions， it should limit the number of auction items of the sellers， and maintain the sellers' single-item income. When the auction platform conducts online auctions， it should make suggestions to the sellers to let them sell the items as much as possible at one time， so that the income of a single item can be as high as possible， so that the platform itself can get more commissions.It is assumed that the bidder only has unit demand， and the subsequent research can be conducted on the bidder's demand for multiple units. If the bidder has demand for multiple units， it needs to submit both price and quantity for auction. This situation is much more complicated.

The increasing globalization of financial markets has amplified the destructive power and contagion effects of financial crises， posing significant threats to national financial stability and economic development. Regulators face the critical challenge of establishing effective early warning signals. Financial stress indices， which represent price pressure in financial markets， and systemic risk indicators based on financial institution measures are both used as metrics for financial crises， but the relationship between them is unclear.The relationship between macro-level financial market pressures， as represented by FSIs， and micro-level systemic risk， as measured by the capital shortfalls of financial institutions （SRISK） is investigated. Specifically， it examines whether the aggregate capital shortages of individual financial institutions can explain the observed price pressures in the broader financial market. Furthermore， it is explored which form of standardized SRISK （relative to GDP， market value， or prudential capital requirements） provides the most robust explanation for financial market stress， thereby shedding light on the underlying mechanisms linking micro-level vulnerabilities to macro-level crises. Finally， it aims to develop internally consistent indicators for measuring financial crisis probability and systemic risk tolerance， ultimately leading to a practical early warning indicator.To address these questions， a multi-faceted approach is adopted. First， a comprehensive FSI for China is constructed using nine representative indicators from the money， stock， bond， and foreign exchange markets. Second， the SRISK for both Chinese and major global economies is estimated， representing the expected capital shortfall of financial institutions under a systemic event. The SRISK is then standardized in three ways： SRISK/GDP， SRISK/MV， and SRISK/（TA*k）. Third， both domestic and global regression models are established to empirically test the relationship between the FSI and the standardized SRISK measures. Fourth， based on the regression results， two internally consistent financial crisis metrics are derived： （1） the probability of a financial crisis， defined as the probability that the FSI exceeds a predefined threshold （mean plus one standard deviation）； and （2） SRISK capacity， defined as the maximum tolerable level of SRISK for a given probability of crisis （e.g.， 50%）. Finally， an early warning indicator is developed by comparing the actual SRISK to the estimated SRISK capacity.The empirical results demonstrate that micro-level systemic risk measures， particularly SRISK standardized by prudential capital requirements （SRISK/（TA*k））， can effectively explain macro-level price pressures in the financial market. The SRISK/（TA*k） variable consistently exhibits the strongest and most significant positive relationship with the FSI across different model specifications， suggesting that financial crises are closely associated with deleveraging behavior through asset fire sales to mitigate capital shortfalls. The estimated probability of a financial crisis in China aligns well with historical periods of heightened systemic risk. The global model tends to capture the impact of external shocks earlier than the domestic model， consistent with the nature of cross-border risk contagion. The analysis of SRISK capacity provides a dynamic measure of the system’s resilience to systemic risk. Valuable insights into the interconnectedness of macro and micro dimensions of financial crises are provided. By demonstrating that macro price pressures can be explained by micro-level capital shortfalls， a critical gap in the literature is bridged. The finding that SRISK standardized by prudential capital requirements is the most informative indicator highlights the importance of regulatory capital standards in mitigating systemic risk. The proposed early warning indicator， based on the ratio of SRISK to SRISK capacity， offers a practical tool for regulators to monitor systemic risk and implement timely interventions. The results also highlight the growing systemic importance of Chinese financial institutions in the global context. Overall， it contributes to a deeper understanding of financial crisis dynamics and provides valuable guidance for policymakers seeking to enhance financial stability.

The challenge of accurately assessing credit risk has grown with the complexity of financial markets and the availability of big data. Traditional models often fall short of capturing dynamic risk factors. The problem is addressed by proposing the Factor-Enhanced Additive Neural Network （FAAN） model， combining big data dimensionality reduction techniques with deep learning approximation to improve accuracy and interpretability in credit risk measurement.The main research question is designing an interpretable deep learning model that outperforms existing methods in measuring corporate credit risk. The FAAN model tackles this by integrating factor-based dimensionality reduction to remove noise and deep learning to capture complex non-linear relationships. It is more effective than traditional models like linear regression and popular interpretable machine learning methods （e.g.， GAM， EBM）.Empirical analysis using real-world data shows that the FAAN model： （1） consistently outperforms external credit ratings and other models across various metrics， demonstrating superior generalization； （2） highlights price indicators as crucial dynamic factors that enhance credit risk detection； and （3） confirms that combining dimensionality reduction with deep learning significantly improves credit rating quality.It contributes to research on interpretable deep learning and corporate credit risk， offering a powerful tool for dynamic risk assessment and credit rating practices， advancing both academic understanding and practical applications.

Accurately predicting future stock returns can effectively enhance the out-of-sample performance of a investment portfolio， yet a single prediction model may struggle to robustly forecast a vast number of stock assets， each with distinct data characteristics. To address this issue， a mixed ensemble algorithm based on machine learning and ensemble learning is proposed to predict a wide variety of stock assets. Specifically， the proposed mixed ensemble algorithm is based on a mix of five single models and five ensemble models： including Multiple Linear Regression （MLR）， Support Vector Regression （SVR）， Generalized Regression Neural Network （GRNN）， Convolutional Neural Network （CNN）， Long Short-Term Memory （LSTM）， Simple Average， Linear Weighted， Random Forest （RF）， Gradient Boosting Decision Tree （GBDT）， and Entropy Weighting. By generating investor views through the proposed mixed ensemble algorithm and pre-selecting a portion of high-quality stocks， the Black-Litterman （BL） model is further incorporated to construct a more effective portfolio strategy. In the empirical section of this study， 105 constituent stocks from the CSI 300 index were selected as research samples， covering 242 trading days from January 1， 2022， to December 30， 2022， with daily data frequency. Empirical results show that， the mixed ensemble algorithm significantly reduces the forecasting error of future stock returns compared to benchmark models. After stock pre-selection the out-of-sample performance of all portfolio strategies significantly surpasses the CSI 300 Index. Notably， the out-of-sample performance of the constructed portfolio strategy by using the mixed ensemble forecasting algorithm and BL model markedly is superior to traditional portfolio strategies such as minimum variance， maximum Sharpe ratio， maximum expected return， equal weight， and equal risk weight， as well as other benchmark forecasting algorithms and BL model-constructed portfolio strategies. Finally， a robustness test is conducted by varying the rolling window's length， which further verifies the robustness of this approach. The proposed portfolio optimization model can significantly improve the out-of-sample performance of a vast number of stocks and provide theoretical guidance for stock investment in the real market.

Industry alliances serve as a potent conduit for enhancing innovation outputs among firms. However， the dissolution of such alliances is frequently precipitated by inequitable innovation revenue distribution mechanisms. The phenomenon of re-alliance among enterprises within an industry alliance is addressed， and the cooperative game with coalition structure is introduced to delineate the existence of preferential alliances among enterprises. Subsequently， the classic axiom of consistency is extended to cooperative games with coalition structures， axiomatizing the Banzhaf value with coalition structure within this framework. The consistency axiom underscores the intrinsic stability of allocation rules， stipulating that when a subset of participants departs with their allocated gains， the remaining participants’ shares in the resultant reduced game should align with their shares in the original game. Consequently， an allocation rule that satisfies the consistency ensures that agreements formulated under this rule remain unaltered irrespective of participant count fluctuations. Then， a three-stage extended game model is constructed for the distribution of innovation revenues within industry alliances， examining the operational mechanism of the Banzhaf value with coalition structure. Finally， three novel innovation revenue distribution schemes are proposed， predicated on the Banzhaf value with coalition structure， and an empirical analysis is conducted using the intelligent voice intellectual property industry alliance as a case study.The findings indicate　（1） The Banzhaf value with coalition structure possess inherent stability and is uniquely characterized by consistency within the priori union and standardization. （2） Within the three-stage extended game model for the distribution of innovation revenues in industry alliances， the Banzhaf value with coalition structure constitutes the subgame perfect equilibrium for the initial two stages. （3） Scheme one， predicated on the Banzhaf value with coalition structure， not only fosters the development of dominant enterprises but also safeguards the stability of the industry alliance. Scheme two enhances solidarity among enterprises within the industry alliance， facilitating the flow of innovation elements. Scheme three is more apt for industry alliances constituted by risk-neutral enterprises.

The emerging incidents of human flesh search threatened personal privacy. Privacy leakage in the process of human flesh search has become an important factor that inhibits the development of the Internet. Using a 2×2×2 multi-factor mixed control experiments， how decision contexts， for example herd behaviors and network anonymity， influence the relationship between the intention and the actual behaviors of disclosing others’ privacy is examined. It is found that privacy leakage intention positively predicts corresponding behaviors. Herd behaviors strengthens the relationship between privacy leakage intention and corresponding behavior， while network anonymity weakens that relationship. As network anonymity increases， the relationship between privacy leakage intention and corresponding behavior turns to negative. Theory is enriched by clarifying the motivations and decision mechanism of disclosing others’ privacy. Operable suggestions for both the government and Internet firms are offered to attenuate the motivations and actual behaviors of disclosing others’ privacy.

As public concern about the quality of large language models （LLMs） continues to grow， vendors are increasingly adopting open-source strategy as an indirect means to signal quality， aiming to enhance consumers’ perception of model performance and alleviate security concerns. A signaling game model is constructed in which the open-source degree of LLMs serves as a quality signal， and how a vendor’s quality disclosure strategy affects the open-source degree， market demand， and profits is examined. It is found that when the quality difference between the vendor’s potential high and low-type states is small， the vendor conceals its quality type under a non-disclosure strategy （pooling equilibrium）. Compared with active disclosure， in this case a high-type vendor chooses a lower open-source degree and experiences lower first-period market demand but higher second-period demand， while the opposite pattern holds for a low-type vendor. When the quality difference is large， the vendor reveals its type under non-disclosure （separating equilibrium）， where a high-type vendor adopts a higher open-source degree and achieves higher first-period demand but lower second-period demand compared to active disclosure， while a low-type vendor’s open-source degree and market demand remain unaffected. Active quality disclosure always enhances the profit of a high-type vendor， but reduces the profit of a low-type vendor when the quality difference is substantial.

In the context of a growing global focus on quality， luxury goods have evolved to provide consumers not only with functional utility but also with status utility， which reflects social prestige and wealth. However， the true quality of luxury products remains a private piece of information held by companies， leading to an information asymmetry between producers and consumers. This information gap poses a critical challenge for luxury brands： whether to disclose product quality to reduce information asymmetry and build trust with consumers， or to withhold it to maintain the product's exclusivity and mystique. The core research question of this paper is how luxury brands can optimally balance the disclosure of quality information while maintaining their status value in various supply chain structures.To address this problem， a game-theoretic model is built to analyze luxury brands' quality disclosure strategies in both centralized （direct-operated） and decentralized （multi-brand or buyer-operated） supply chain structures. The model assesses how differences in supply chain management affect the firms' willingness to disclose quality information. It also examines the contrast between luxury goods and ordinary products in terms of quality disclosure and explores how the sensitivity of consumers to status utility influences the decision-making process for luxury brands. The problem is approached through several key research methods， including theoretical modeling and comparative analysis. The game theory-based model simulates the behavior of luxury firms under different market conditions， including various levels of consumer status sensitivity， supply chain structures， and competitive environments. It identifies the conditions under which firms will choose to disclose or hide product quality and the resultant effects on pricing， consumer perception， and firm profitability.The main findings of the research are as follows　（a） Luxury brands should selectively disclose quality information to balance the product's perceived excellence with its mystique. The optimal strategy involves revealing quality information only when the product quality exceeds a certain threshold， enhancing consumer trust while preserving exclusivity. （b） Compared to ordinary products， luxury goods have a higher threshold for quality disclosure. This higher threshold reflects the luxury brands' desire to protect the status utility provided by their products. （c） Luxury brands operating in centralized supply chains （e.g.， direct-operated stores） have a higher tendency to disclose quality information than those in decentralized supply chains （e.g.， multi-brand or buyer stores）， as centralized supply chains allow greater control over brand perception and consumer interaction. （d） As consumer sensitivity to status utility increases， luxury brands become more inclined to withhold quality information， even when the disclosure cost is low. However， this selective withholding leads to higher product pricing and enhanced profitability for the brand.

It focuses on the construction of strategic delivery force in this paper， aiming to specify the current construction level by using the grey cluster evaluation. The result can be used for providing support for decisions about the formulation of future target improvement measures. Firstly， a three-layer index system is proposed to describe the characteristics of the strategic delivery force about composition structure， the delivery process and sources of the materials which are going to be delivered. Secondly， the traditional possibility functions employed in the grey clustering evaluation have been modified to overcome the drawback that they are not able to express the uncertain factors which origin from multiple sources. Then， the detailed implementation steps of the newly developed improved possibility function based grey clustering evaluation of strategic delivery force are presented. At last， the practicability and effectiveness of the proposed method are demonstrated through a numerical analysis with regard to a key element of the index system. Finally， the practicability and effectiveness of the proposed method are verified by numerical analysis of a core component of the index system.

In this paper， the decision problem of equipment replacement in technology upgrading of manufacturing enterprises is studied. In the context of the uncertainty of technology development and market demand， decision makers need to make a choice between continuing to produce with “high cost of old technology” or using “low cost of new technology” in order to minimize the sum of equipment replacement cost and total operating cost. In the case of unknown product market demand time T， decision makers need to decide the specific time sequence t_{\mathrm{1}},t_{\mathrm{2}},\cdots ,t_k of the future k replacement only with the information of the per-unit running costs c_{\mathrm{1}},c_{\mathrm{2}},\cdots ,c_{k+\mathrm{1}} and replacement costs \mathrm{\Delta }{c}_{\mathrm{0}},\mathrm{\Delta }{c}_{\mathrm{1}},\cdots ,\mathrm{\Delta }{c}_k， so as to minimize the total cost while controlling the uncertainty risk. The competitive analysis is used to design an online strategy， so that the gap between the solution of the online strategy and the optimal solution is controlled within a certain ratio， so that even in the worst case， the enterprise can get a relatively satisfactory result.Firstly， the k-equipment replacement model is established in the paper. Then， an online strategy CSER， whose replacement time sequence and the corresponding competitive ratio is obtained by solving a non-linear programming problem NLP. And the optimality of the strategy CSER is further proved. Moreover， the discussion of the special case when k=\mathrm{1} shows that the k-equipment replacement model is more general than the previous research. Then， in order to solve the non-linear problem NLP， a numerical solution algorithm CBSM is designed by using the idea of binary search， where the solution of NLP is transformed into checking the feasible domains of a series of linear programmings. Finally， the effectiveness of the strategy CSER is verified by numerical simulations， and some general suggestions are also presented for decision makers.The k-equipment replacement model in this paper allows arbitrary replacement times and variation of replacement costs. So it is a quite general model and may have strong practical significance. In the future， factors such as the experience of decision makers， risk appetite and compatibility of old and new technologies can be incorporated into the research of the online replacement decision making.

Aiming at the vessel navigation congestion problem caused by the bottleneck effect of stepped locks， congestion charging and differential dam-over subsidy mechanism are introduced to enhance the shipping efficiency. Firstly， based on the dynamics of ships crossing the locks in a cycle， and the difference of the ideal time interval between two locks in a row， a double bottleneck model without charging is established， and the total cost of the system is derived under different scenarios； and then， through the establishment of the optimal charging equilibrium model， the congestion charge is taken as the subsidy for dam overturning， and the two-line linkage mechanism of ‘crossing the locks and overturning the dams’ is put forward. Then， by establishing the optimal toll equilibrium model and taking the congestion fee as the dam-over subsidy， a two-lane linkage mechanism of ‘gate crossing + dam-over’ is proposed to realise the synergy of navigable vessels crossing dams. It is shown that： toll management can effectively reduce the social cost of ship navigation and alleviate the congestion problem in front of the dam， and the dam-over subsidy can effectively promote the transfer of the dam-over mode； navigation managers can regulate the capacity of the downstream bottleneck when the upstream and downstream ships are not balanced to avoid the backlog of ships between the locks of the ladder level， so as to make effective use of the capacity of the dam-over； at the same time， by giving the larger tonnage ships a higher priority for the lock-over system， the function of the dam-over system can be brought into full play， and the total number of ships crossing the dams can be increased. At the same time， giving higher priority to larger tonnage ships can give full play to the function of the over-dam system and increase the total number of ships crossing the dam.

In logistics supply chain management， the warehouse as the storage place of goods， its storage optimization has been the focus of research by warehouse managers and researchers. The allocation of storage positions and goods picking are two important aspects of inventory management. If the space allocation strategy is appropriate， the efficiency of goods picking can be effectively improved. The ABC allocation strategy commonly used in warehouse storage classifies goods by category and turnover. This allocation method does not consider the same kind of goods for split placement. However， when demand is uncertain， considering the need of safe stock， the selection of a few items in the replenishment cycle of a single goods is a low probability event. The ABC allocation strategy will make the items with a low probability of being selected in the goods with a high total turnover frequency also occupy the position closer to I/O point. The low turnover frequency leads to an increase in total picking distance and a decrease in picking efficiency.In this paper， a new allocation strategy based on uncertain demand is proposed. By considering the different demand probability of different quantity items in a single goods， the strategy divides the same goods into different areas. The low-probability demand portion of the goods will be stored away from the I/O point. In this way， the total demand probability near the I/O point is increased， thus making the storage and picking process more efficient. For the convenience of description and understanding， the goods location allocation strategy proposed in this paper is defined as the Probability-of-Retrieval（POR） goods location allocation strategy. The ABC location allocation strategy sorts goods according to turnover rate， but the POR strategy allocates the same goods according to the different demand probability under the different quantity in the replenishment period， and determines a probability boundary as the partition boundary， all kinds of goods are stored randomly within the same boundary.The average one-way picking distance is taken as the optimization goal. Firstly， the optimal order quantity of each goods is gotten by combining EOQ replenishment strategy. Secondly， according to the assumption of normal distribution and the POR location allocation strategy， the expressions of storage space， weighted turnover frequency and picking distance of goods in different subareas and the expressions of picking distance of ABC location allocation under uncertain demand are derived. Finally， Matlab 2018A is used to carry out numerical experiments， and the optimized results are compared under the different variable parameters such as demand curve， standard deviation， service level and so on， the influence of the change of the probability partition on the total picking distance is discussed.The experimental results show that under different parameters， the POR location allocation strategy is better than the traditional ABC location allocation strategy to varying degrees. The smoother the demand curve is， the smaller the demand difference of various goods is. The model based on demand probability proposed in this paper can save the total travel distance more than the traditional model， but at the same time， the increase of optimization degree is also more and more gentle； Under the POR strategy， too high service level is not appropriate； The smaller the standard deviation of goods demand distribution， the higher the degree of optimization； The more probability zones， the smaller the travel distance. Under the common ABC slope curve of 20% goods contributing 80% demand， in each probability partition strategy， when the cumulative demand probability of goods in the first partition （the area closest to the I/0 point） is about 20%， the total picking distance is relatively shortest.In the actual storage， the warehouse manager can estimate the demand distribution according to the previous orders， get the average value and standard deviation of different kinds of goods， and arrange the goods distribution better under the partition storage allocation strategy based on the demand probability.

Buffer monitoring is a core content of critical chain project management， which can control the project progress by monitoring the project buffer. In the research of critical chain project buffer monitoring， most of them monitor the project in units of activities， stages or projects， and the measurement of project progress is not comprehensive. Earned value management and schedule risk analysis theory analyze and control the project schedule from the whole and part， respectively， but there are still some limitations in measuring the activity and project schedule risk when applied to buffer monitoring research. In order to comprehensively analyze the overall progress and internal activity information of a project， and provide more effective early warnings of project execution during the buffer monitoring process， a new sensitivity index and monitoring threshold are established based on the schedule risk analysis and the earned duration theory， and an all-round buffer monitoring model is proposed in this paper. First， considering the network and resource characteristics of activities， as well as the impact of the mean and standard deviation of the duration on project progress， an integrated sensitivity index based on simulation is established to measure activity risks and allocate the buffer. Second， the monitoring threshold is determined according to the earned duration method incorporated by the sensitivity index， and then differential deviation corrective measures are taken for activities with different risks to form a comprehensive buffer monitoring system. The new sensitivity index reflects the impact of activity risk on the project from bottom to top， and the improved monitoring threshold provides overall top-down control from a project perspective， thus forming a comprehensive buffer monitoring system. Finally， the proposed method， static buffer monitoring method and relative buffer monitoring method are applied to a typical tailings pond construction project to carry out Monte Carlo simulation. The results show that compared with classical methods， the proposed method has double optimization effects in shortening the duration and reducing the cost， which verifies the feasibility and effectiveness of the proposed method. It can help project managers to comprehensively analyze the progress of the project from the perspective of project and activity， and provide a certain reference value and practical significance for the decision-making of project progress control.

In the replenishment phase of mobile robot fulfillment systems， constantly changing consumer demand necessitates timely decisions regarding the allocation of replenished items to storage locations and the adjustment of pod positions within the storage area. A well-organized pod layout is crucial to mitigate congestion in the aisles where robots operate， which can significantly impact the efficiency of picking operations. The joint optimization of item and pod storage assignment problems is addressed during the replenishment phase， with a focus on achieving workload balance across picking aisles. The research problem originates from the need to enhance operational efficiency in robotic warehouses， where dynamic consumer demands and spatial constraints present significant challenges. Accurately assigning items to pods and optimally positioning these pods within the warehouse can lead to improved picking efficiency and reduced robot congestion. The problem is formulated to consider both the internal composition of pods and their spatial distribution in the warehouse. A bi-objective mixed-integer programming model is developed to maximize the total product correlation on pods and the matching degree between pods and storage locations. The product correlation objective aims to group items that are frequently ordered together onto the same pod， thereby reducing the number of pod visits required during order fulfillment. The matching degree objective ensures that pods are stored in locations that minimize travel distances. To solve this complex bi-objective optimization problem， an improved non-dominated sorting genetic algorithm II （NSGA-II） is designed. The algorithm is enhanced by incorporating a decentralized pod storage assignment strategy to facilitate balanced aisle workload distribution， ensuring efficiency and reducing congestion. Numerical experiments are conducted using data that reflect typical warehouse scenarios to validate the proposed algorithm. The results demonstrated the accuracy and effectiveness of our approach through comparisons with exact solutions obtained by Gurobi， a state-of-the-art optimization solver. Furthermore， advantages of the joint optimization model over a two-stage heuristic algorithm are highlighted in terms of achieving a better balance between product correlation and pod-storage matching. Sensitivity analysis is performed， and several key insights are obtained： increasing the weight coefficient for aisle congestion is found to alleviate congestion but decrease the matching degree between pods and storage locations. Conversely， higher replenishment rates are shown to enhance product correlation on pods and the matching degree but increase pod transportation distances. Additionally， it is observed that a warehouse length-to-width ratio closer to 1 and lower product correlation favor overall product correlation on pods and matching degree. It contributes to the field by offering a novel approach to managing the joint storage allocation and pod positioning in the replenishment phase. By balancing efficiency and operational constraints while addressing fluctuating consumer demands， the proposed model and algorithm provide valuable insights for optimizing warehouse operations. The findings can significantly aid related research in warehouse management and logistics optimization， offering strategies that enhance the responsiveness and efficiency of fulfillment systems in the face of dynamic market conditions.

With the increasingly fierce international competition， company competition is gradually changing to supply chain competition. E-commerce platforms are actively strengthening deep cooperation within the supply chain to enhance overall competitiveness， such as equity investments. Especially in a market with huge growth potential， e-commerce platforms are trying to find supply chain equity partners to expand the market. In recent years， however， the rise of private labels has provided them with new options， which has made equity partnerships increasingly complex. More and more e-commerce platforms are considering introducing private labels to seize the market alone or in partnership with manufacturers. In other words， e-commerce platforms need to weigh the gains from equity investment in market expansion against the loss of private label market share due to increased competition. Therefore， based on the market competition brought by private labels， a supply chain composed of a manufacturer and an e-commerce platform is established. By comparing the change of the e-commerce platform's profit with private label introduction， whether the e-commerce platform will invest in upstream manufacturers after private label introduction is studied. Further， the e-commerce platform's private label introduction strategy is studied and the impact of private label introduction on the e-commerce platform's equity investment strategy is analyzed. It is found that the e-commerce platform always tends to introduce private label， which increases the likelihood of adopting equity investment strategy. Further analysis shows that the e-commerce platforms's equity investment strategy is affected by investment efficiency， shareholding ratio and brand advantage. When the investment efficiency is low， the e-commerce platform tends to take equity investment with lower shareholding ratio. However， when the shareholding ratio is high， the e-commerce platform's equity investment strategy depends on two brand advantages. Specifically， in the case of large advantages of national brand and small gap between the two brands， the e-commerce platform adopts equity investment strategy. Or， in the case of significant advantages of private label， the e-commerce platform adopts equity investment strategy. These conclusions provide a possible explanation for the significant differences in equity investment strategy of e-commerce platforms after private label introduction.

Collaborating with local enterprises to reserve emergency supplies can help local governments quickly meet the emergency material demand in post-disaster phase. In order to meet the surging demand and avoid supply disruption， the government authority needs to cooperate with multiple suppliers. In this situation， the enterprises will make the cooperation decision based on whether the reserved materials can be sold when disaster happens and how much the government authority will pay for it. In this regard， emergency material supply agreement cooperation based on quantity flexibility contract is built among one government authority and multiple local enterprises with considering the quantity of sold materials being determined by the reliability of the local enterprises and the government’s procurement sequence. The impact of the government’s procurement pricing decisions on the feasibility and cost-effectiveness of the government-enterprise agreement cooperation are analyzed.

Based on the analytical results and numerical simulation results， it is found that when the reliability of the enterprises are the same， it is better for the government authority to equally assign the emergency material procurement quantity. When the reliability are different， with the increase of the government’s regular procurement quantity， the government’s flexibility procurement pricing level will increase and the quantity of the materials reserved by each agreement enterprise decreases. However， the benefits of the agreement enterprises will show diverse trends. It theoretically enriches the research studies on emergency material procurement management problem in this paper. Practically， managerial insights and decision reference are provided for the government authority to cooperate with local enterprises to improve the material supply capability and emergency response capability.

Considering cross-side externality between self-scheduling supply and demand in the ride-hailing platform， to match the two sides and optimize profit， subscription pricing is introduced， where if customers choose to subscribe， then they can receive services at a discounted price during the contract period after paying the subscription fee at the beginning； Otherwise， they will receive services at regular prices during the contract period. For the platform， subscription services lock up some revenue from the customers who pay before they observe their perceived value； But at the same time， this part of consumers request services at low prices during the contract period， which will also bring losses to the platform. The platform needs to weigh the pros and cons of introducing subscription services. A model is built and solved that considers endogenous supply capacity and allows customers to choose whether to subscribe. And the firm determines optimal subscription plan as well as the wage. It is found that the optimal strategy balances expected supply and demand at a higher level and the optimal pricing achieves higher profits. It is believed that the reason for this is from two aspects： on the one hand， some passengers choose to purchase subscription services in advance， which allows the platform to take advantage of the passengers' ex-ante homogeneity in their perceived value； on the other hand， the heterogeneity in the number of rides leads different passengers to make different choices， from the platform's perspective， which is equivalent to differential pricing on the dimension of ride frequency. A sensitivity analysis of the impact of relevant market parameters on the optimal decisions is conducted. When the scale of supply （relative to demand） grows， due to the fact that resources are no longer scarce， in order to achieve supply-demand balance， the platform needs to decrease the price of each ride to increase passengers' willingness to request the service， and reduce the driver's wage to control effective supply. At the same time， in order to ensure the optimal number of subscribers， the platform needs to increase the discount. Finally， in order to make up for the loss of revenue caused by the decrease of pay-per-ride and discount rate， the platform needs to increase subscription fees. Numerical experiments show that these conclusions still hold when there are multiple scenarios. These findings are helpful for ride-hailing platforms to determine their pricing strategies.

As the aging population increases， the treatment demand from chronic disease patients is rising， posing unprecedented challenges to traditional healthcare systems. Chronic disease patients typically require long-term and regular consultations， significantly increasing the operational burden of healthcare systems. In this context， internet hospitals， as an emerging healthcare service model， provide a new solution to alleviate this issue. Particularly in the special situation in China， where patients often remain during doctors' lunch breaks， the integration of online and offline outpatient processes in internet hospitals shows great potential. By increasing the proportion of online consultations in the current process and optimizing the allocation of medical resources， overall efficiency can be improved， and costs can be reduced. An optimization plan based on a Simulink simulation model is proposed， aiming to explore the optimization of integrated online and offline outpatient processes in internet hospitals. Through the construction of a Simulink simulation model， detailed comparisons of the processes before and after optimization are conducted， analyzing the effects of the optimization plan on cost reduction and resource utilization. The research data is sourced from daily outpatient registration numbers from chronic disease departments in top-tier hospitals， statistical data from the National Health Commission， survey data on the willingness to use online medical services， and relevant literature， which provide reliable bases for model parameter allocation. Factors such as patient demand， the proportion of online and offline services， and doctors' workload are incorporated into the Simulink model to ensure that the optimization plan fits the actual situation. The results show that the optimized process can not only improve the efficiency of medical resource utilization but also effectively reduce the operational pressure on hospitals. Particularly when facing a large number of chronic disease patients， the proportion of online services plays a crucial role in enhancing efficiency. Additionally， sensitivity analysis of the optimization plan was conducted， and the results indicate that the proportion of online and offline services needs to be dynamically adjusted with changes in patient demand. When medical resources are not saturated， more patients should be encouraged to choose online follow-up consultations to improve overall efficiency. However， during peak patient flow periods， excessive reliance on online services should be avoided to prevent overcrowding of the online consultation system. It helps deepen the understanding of the relationship between online and offline services in internet hospitals is this study， further enriching the theoretical framework of integrated online and offline services. The research results not only provide practical optimization strategies for internet hospital managers， helping them flexibly adjust the proportion of online and offline services based on varying patient needs but also offer strong support for the allocation of hospital medical resources， aiming to improve overall service efficiency and the patient experience.

The connection between agriculture and super is an important way to promote the upward movement of agricultural products， and one of the reasons for the difficulty of farmers' continuous income increase in the connection between agriculture and super is the lack of efficient agricultural products operation mode and logistics cooperation system. At this time， the decision of whether logistics enterprises cooperate is related to logistics costs and farmers' income. Therefore， considering the characteristics of small scale and scattered tasks， the logistics cooperation mode of agricultural and super docking is constructed from the concept of cooperative game. Firstly， based on the characteristics of “wide area、 long line and many points”， the logistics cooperation mechanism of agricultural and super docking logistics is designed. Secondly， in order to solve the scheduling optimization problem in actual logistics， a multi-party cooperative logistics cost minimization model considering time window constraint is established， and the total logistics cost is allocated by Shapley value method. Finally， based on numerical experiments， the effectiveness of the proposed model is verified， and the farmer's income model is constructed considering logistics efficiency， and the change of farmer's income in different models is compared.The research shows that the logistics cooperation mode of connecting agricultural and super supermarkets can reduce the total logistics cost of about 8.77%-35.80%， improve the transportation efficiency， and increase the income of farmers by about 54.85%-71.58%， and encourage farmers to participate in the connection of agricultural and super supermarkets to achieve continuous income increase. When the Rural-super docking is carried out nationwide， the number of participating farmers increases， and the logistics cooperation mode is more beneficial to farmers， enterprises and supermarkets， which provides a new scientific idea for solving the actual logistics problems of rural-super docking.

To win the competition， all major online video platforms are now increasing the number of their exclusive video for differentiation. However， this action will directly change the original competition logic between the platforms by generating multi-homing viewers. Simply deciding the number of exclusive rights from the perspective of competitive differentiation is unwise. Combining the “positive and negative cross network externalities” of streaming media platforms， the dynamic game theory is applied to give the optimal exclusive content procuring decision of such platforms under different clients' multi-homing scenarios. The optimal exclusive rights ratio decisions of platforms under different market homing scenarios and specifies three sets of environmental factors that platforms should focus on in their decisions are given. In particular， the platform's two-sided pricing and exclusive rights procurement cost directly affect the platform's decision， while the homing of the two-sided market exerts an indirect influence on the exclusive rights ratio by affecting the platform's pricing decision. Subsequently， by comparing the equilibrium results of different scenarios， the complex impact of multi-homing behavior of two-sided clients on platforms' exclusive rights decisions and competitive strategies is further explored. Viewers' multi-homing behavior can cause competitively weak platforms to “free-rider” when making exclusive rights decisions. However， if advertisers also engage in multi-homing behavior， the interaction between multi-homing viewers and advertisers can lead competing platforms to jointly increase exclusivity ratios in some cases in order to increase market size. At this point， a cooperative relationship exists between competing platforms. Finally， the impact of multi-homing behavior of two-sided clients on platform profits is analyzed. This impact is reflected in the fact that the multi-homing behavior of different clients in video platforms changes the scope and direction of the impact of exclusive rights on platform profits. Existing studies related to the competition for exclusive rights in video platforms mainly focus on the impact of viewers' multi-homing behavior on platforms' competition in the content market， and few studies focus on the impact of viewers' multi-homing behavior on platforms' competition in the advertising market. Even fewer studies have focused on the impact of viewers' and advertisers' multi-homing behavior on competition in the two-sided market， and have considered the interaction between the two types of multi-homing clients in video platforms' exclusive rights decisions. The ratio of exclusive rights of platforms in competitive markets is analyzed by integrating the multi-homing behaviors of viewers and advertisers， which is of theoretical value. From a practical point of view， most of the current video platforms are obsessed with pursuing a high percentage of exclusive rights as a means to differentiate themselves from the competition. The irrationality of this approach is proved. Due to the existence of multi-homing viewers and multi-homing advertisers， a high percentage of exclusive rights does not necessarily bring a competitive advantage to platforms， but instead leads to heavy cost burdens.

In the context of economic globalization， the complexity of supply chains has been increasingly amplified. Enterprises are simultaneously confronted with uncertain market environments and mounting pressures to achieve sustainable development， necessitating a careful balance between enhancing resilience capabilities and attaining sustainability objectives. Among the critical aspects of supply chain management， the supply chain networks design （SCND） plays a pivotal role， as it determines the infrastructure and physical configuration of the supply chain. Decisions in SCND fundamentally influence an enterprise’s ability to operate effectively in complex and uncertain environments over the long term. Existing studies underscore the profound impact of resilient supply chain networks on operational performance and sustainability outcomes. To deepen the understanding of how enterprises can enhance supply chain resilience through network design and the adoption of resilience strategies， as well as to explore the interplay between resilience measures and sustainability goals， a comprehensive review of the literature on sustainable and resilient supply chain networks is conducted， with a particular focus on quantitative methodologies. The review synthesizes the general theoretical frameworks in this field， characterizes the structural attributes of supply chain networks， categorizes the risks faced by supply chains， and examines commonly employed resilience strategies. Special attention is given to analyze the interrelationship between resilience strategies and sustainability objectives. From the perspective of optimization modeling， the formulation of resilience and sustainability objective functions， the representation of uncertainties， and the selection of appropriate optimization models are further investigated. The findings reveal that substantial progress has been made in recognizing the importance of addressing supply chain risks， identifying preventive and reactive strategies during the network design phase to mitigate disruptions and operational risks， and employing optimization modeling to balance resilience and sustainability objectives. Nonetheless， research in this domain remains at a nascent stage， with numerous influencing factors， resilience strategies， and methodological approaches yet to be fully explored. It highlights several open questions and provides a foundation in this study for future research in this critical area.

Echelon utilization of power batteries refers to the process of inspecting， classifying， and disassembling “retired” power batteries， followed by recycling them to restore their functionality， either partially or fully， for application in other fields. Considering the safety of echelon utilization of electric vehicle batteries， a two-stage game model is constructed， consisting of a battery manufacturer and an authorized processor， based on the advantage of blockchain technology in information traceability， and the impact of introduction of blockchain technology on the optimal decisions of supply chain members is investigated. The battery manufacturer is the leader in the game， while the authorized processor is the follower. The model is solved using backward induction. The research findings indicate that： （1） Battery manufacturers and authorized processors are likely to cooperate only when the competition intensity between the two types of echelon products is moderate， and both parties' profits increase with the intensification of product competition； （2） For battery manufacturers， there is a critical value for the cost of introducing blockchain technology. Only when the introduction cost is below this critical value does the manufacturer have the incentive to introduce blockchain technology. At this point， consumer surplus and social welfare are both superior to scenarios without blockchain technology. （3） Compared to the battery manufacturer， the authorized processor has no incentive to introduce blockchain， as the processor’s profit in this scenario is lower than the situation where the battery manufacturer is responsible for introducing blockchain. （4） When the battery manufacturer is in a dominant position relative to the authorized processor， its profit share will be higher than that of the authorized processor. Finally， using BYD's "Tang" as an example， this paper validates the model by collecting actual industry data. The calculation results show that the model has strong robustness and applicability. The conclusions of this study further enrich the application of blockchain technology in operations management.

The prevalent information asymmetry problem in supply chain seriously restricts its overall effectiveness. Promoting the in-depth development of supply chain signaling theory is a key prerequisite for optimizing supply chain management and enhancing industrial competitiveness. In order to further optimize the supply chain signaling mechanism， improve the transparency and collaborative efficiency of the supply chain， and realize the efficient operation and sustainable development of the supply chain， the quantitative method of mapping is used to sort out and analyze the theoretical research dynamics of the supply chain signaling theory based on the CNKI and Web of Science databases. Firstly， based on the existing research， the research perspectives of supply chain signaling are sorted out， focusing on the core elements of supply chain management： demand， cost， and quality， and summarizing the focuses of the existing signaling research and the future research trends from this perspective. Secondly， it clarifies the theoretical development of supply chain signaling， and establishes “the origin of supply chain signaling mechanism-the revelation of supply chain signaling mechanism-the realization of supply chain signaling mechanism” as the main research vein. It establishes “the origin of supply chain signaling mechanism-revealed supply chain signaling mechanism-realization of supply chain signaling mechanism” as the main research vein. Once again， the controversy of the current supply chain signaling theory research is discussed in terms of the role， authenticity and effectiveness of signaling. Finally， the future research direction of supply chain signaling theory is proposed in the context of current economic globalization and supply chain digitalization. Thus， the systematic review of supply chain signaling theory in this paper is of great significance in promoting the in-depth development of supply chain signaling theory， which in turn contributes to the efficient operation and sustainable development of supply chains.

A green product supply chain consisting of manufacturers and sellers is proposed， which includes four types of green supply chain models： manufacturers promoting green innovation with fair preference （MM）， sellers promoting green innovation with fair preference （RM）， manufacturers promoting green innovation with fair preference （MR）， and sellers promoting green innovation with fair preference （RR）. Applying game theory methods to solve， the results indicate that： （1） In both types of green supply chain models， namely， the manufacturer-driven green innovation with manufacturer's fairness preference and the retailer-driven green innovation with manufacturer's fairness preference， the efforts of green innovation， product demand， manufacturer's profit， retailer's profit， system total profit， manufacturer's utility， and retailer's utility are all negatively correlated with the degree of manufacturer's preference. （2） The product demand， manufacturer's profit， retailer's profit， system total profit， manufacturer's utility， and retailer's utility under the retailer-driven green innovation with manufacturer's fairness preference strategy are higher than those under the manufacturer-driven green innovation with manufacturer's fairness preference strategy. （3） In the manufacturer-driven green innovation with retailer's fairness preference strategy， when the degree of retailer's fairness preference is controlled within a reasonable range， it helps to enhance the retailer's profit. The retailer's green innovation efforts， product demand， and system total profit are not related to retailer's fairness preference. （4） The product demand， manufacturer's profit， retailer's profit， system total profit， manufacturer's utility， and retailer's utility under the retailer-driven green innovation with retailer's fairness preference strategy are higher than those under the manufacturer-driven green innovation with retailer's fairness preference strategy. Based on the above conclusions， it is recommended for the retailer to implement green innovation strategies， regardless of whether the manufacturer has fairness preference or the retailer has fairness preference. （5） Among the four strategies， the retailer-driven green innovation with retailer's fairness preference strategy is the best choice. The product demand， retailer's profit， and system total profit are optimal under this strategy. When the retailer's preference meets certain conditions， the manufacturer's profit is also optimal under the retailer-driven green innovation with retailer's fairness preference strategy. It is recommended that the government encourage retailers to promote green products with fairness preference. Retailers can make specific green innovation efforts such as green promotion and green advertising. This not only has profound significance for the development of the green economy and the construction of a resource-saving society but also plays an important role in the healthy development and safety of the green industry.

As supply chains become increasingly intricate and interconnected， the business model of “mutual customers and suppliers” is becoming more prevalent， which not only deepens the cooperation between upstream and downstream of the supply chain， but also poses new challenges to management decision-making. The characteristics of the two-way supply chain structure are extracted， the cooperative relationship between suppliers and manufacturers is analyzed， and the efficiency of the two-way supply chain structure and the design of the cooperative mechanism are discussed. Firstly， a benchmark model of two-way supply chain with mutual customers and suppliers is given for centralized and decentralized decision making. The Stackelberg model is used to find the equilibrium solution and give the two-way supply chain efficiency， and it is found that there is still room for coordination in the two-way supply chain. Then the two-way supply chain reverse order discount contract model is designed and the profit compensation mechanism is utilized to coordinate the two-way supply chain.The results show that when the scale of cooperation reaches a certain level， the two-way supply chain structure of mutual customer and supplier can significantly improve supply chain efficiency and increase supply chain profits. This uncovers the inherent value of this structure. However， there is still room for coordination when the scale of cooperation is small. The reverse order discount contract is introduced to explore ways to further improve the supply chain performance， it is observed that the reverse order discount contract damages the manufacturer's profit at the cost， and supply chain coordination can only be realized under the premise of giving a certain profit compensation to the downstream manufacturer.Based on the background of the development of deep integration of supply chains， the structural characteristics of two-way supply chains that are customers and suppliers of each other are portrayed， the value of coordinating supply chain operation decision-making in this kind of structure is revealed， the scope of theoretical research on supply chain structure is expanded， and practical insight for decision-makers of complex supply chain management is provided.

Through theoretical analysis， some significant conclusions are obtained. First， the presence of imitative competition will reduce the crowdfunding price and advertisement level under the independent advertising mode， but interestingly， it may increase the creator’s crowdfunding amount. Second， the cooperative advertising mode will induce the crowdfunding platform to increase the advertising level when the community benefit or the similarity of imitation product is relatively low. Moreover， when the platform’s advertisement is effective， cooperative advertising will simultaneously increase the creator’s crowdfunding amount and profit， the platform’s profit as well as the imitator’s profit. Under the cooperative advertising mode， crowdfunding platforms should set commission levels reasonably and improve the effectiveness of advertising actively to alleviate market competition and achieve win-win results for all parties.With the emergence of Kickstarter， Indiegogo and other crowdfunding platforms， reward-based crowdfunding has become an important form of fundraising that allows new set-ups and small firms to raise money for new product development. To improve the success rate of crowdfunding， creators usually invest in advertising to convey product information and raise consumer awareness， but such investment often inadvertently attract imitators to enter the market. Therefore， in the face of imitative competition， effective allocation of limited initial funds and the choice of advertising mode （independent advertising vs. cooperative advertising） are crucial decisions for crowdfunding creators. It focuses on the pricing and advertising strategies for reward-based crowdfunding projects in the face of imitative competition. An innovative enterprise is considered with limited initial funds that raises money through reward-based crowdfunding for the development of a new product. To improve the success rate of crowdfunding， the creator invests a certain proportion of initial funds for advertising at the beginning of the crowdfunding campaign. If the crowdfunding campaign succeeds， the imitator will also enter the regular market to compete with the creator. By developing a two-stage dynamic game model， the optimal pricing and advertising decisions under the two advertising modes （independent advertising vs. cooperative advertising） are analyzed， respectively. Through comparative analysis， the impacts of imitative competition on crowdfunding decisions are obtained， and the optimal advertising strategies under various situations are derived.

Natural disasters， major social economic events， public security and other emergencies frequently lead to supply disruptions and spread along the supply chain， which cause huge economic losses to enterprises and even society. The government and enterprises should work together to face this challenge to consider effective coping strategies. Existing strategies to cope with the risk of disruption are divided into two main categories， including the enterprise's own coping strategies and help from third-party government organizations. Among them， business interruption insurance， as one of the enterprises' own coping strategies， has achieved better results in business practice. Besides， the insurance been applied by scholars to explore the supply interruption problem. Moreover， the other type of governmental organizations has also effectively helped enterprises to recover their production by adopting control mechanisms such as tax reductions， incentives and penalties， or loans， but there are relatively few research studies on the governmental policy of supply interruption. There is a lack of research on the role of purchasing business interruption insurance in conjunction with low-interest government loans in responding to supply disruptions. In addition， policymakers have increasingly focused on supply disruptions after the epidemic and have also stepped up their efforts to provide assistance to firms. Therefore， in order to improve the resilience of the supply chain， appropriate strategies coping with the risk of supply disruptions caused the uncertainty of output on the supply side should be developed.Based on this， the combination of purchasing business interruption insurance and government low-interest loans is considered to explore the following questions through comparative analyses： （1） What are the transaction prices， order quantities， and compensation amounts for insurance purchases， and the members' expected benefits under different strategies？ （2） What are the effects of different coping strategies on the relevant decisions and members' expected returns？ （3） How effective are the different strategies in coping with the risk of supply disruption？ What is the optimal coping strategy for each party？ Is it possible to realize a win-win situation under the same strategy？To solve the problems， four coping strategies are developed taking no measures， purchasing business interruption insurance， applying for low-interest loans， and hybrid measures （combinations of measures） to explore the strategy options under the risk of supply disruption. The expected profit model of the four strategies is constructed in the two-stage supply chain consisting of the supplier and the manufacturer， in which the supplier provides the products to the manufacturer based on the contract and considers whether to buy business interruption insurance or apply for a low-interest loan from government. Then， the optimal decision-making and the impact of the coping strategy choices on both supply chain members are analyzed. It is shown that the relationship among transaction price under different strategies is affected by the allocation ratio of loan， and low-interest loans can prompt the supplier to increase business interruption insurance compensation. Both low-interest loans or business interruption insurance can increase firms' expected profits， and the unit default payment and the low-interest loan rate will affect optimal strategy choice ed by. When the unit default fee is low and the government loan interest rate is high， or when the unit default fee is high， the coping strategy of purchasing business interruption insurance and applying for low-interest government loan at the same time can encourage manufacture to order more products， which is more conducive for both parties to obtain a higher expected profit and achieve a win-win situation. The results provide references for supply chain pricing， ordering decisions and coping strategy choices under supply disruptions risk and improve the ability of enterprises to cope with the risk of supply disruptions. It also provides theoretical support for the government's use of low-interest loan regulation policies to help enterprises resume production.

For the research on the co-creation value distribution of enterprise innovation networks， scholars mostly use cooperative game methods， using Shapley values and modified Shapley values for distribution， ignoring the impact of network connection structure， the direction and strength of the connection relationship on the distribution of co-creation value. In this paper， the edge density is used to characterize the connection structure in the innovative network， and the Myerson value considering the network connection structure is constructed， and it is proved that it is the only distribution law that satisfies the validity and fairness of the branch. Secondly， the direction and strength of the connection relationship are measured by the importance of the enterprise in the innovation network， and the Myerson value considering the network connection structure is corrected， and the cooperative game solution of the co-creation value distribution of the enterprise innovation network is obtained. Finally， through the numerical test and comparative analysis， it is revealed that the cooperative game solution constructed in this paper is not only reasonable and effective in solving the problem of co-creation value distribution of innovation networks， but also provides a more general allocation method with wide application value.The innovation of this paper is mainly reflected in the following three points　First， the connection structure of the network is considered. In this paper， the edge density is used to characterize the connection structure in the innovative network， and the Myerson value considering the network connection structure is constructed， which makes up for the deficiency that the classical cooperative game can only distribute the benefits to the fully connected network， and provides a scientific decision basis for the co-creation value distribution of the innovative network with different connection structure. Secondly， the direction and strength of the connections in the network are considered. The direction and strength of connections in innovation networks are also key factors affecting co-creation value distribution. In this paper， the importance of enterprises in innovation networks is used to measure the direction and strength of connections， which makes up for the deficiency that the direction and strength of connections are not considered in the studies of cooperative games in existing literatures. Finally， a more general allocation method is constructed for the co-creation value allocation of enterprise innovation network， which has a wide range of application value.

Carbon emissions from household consumption constitute a significant share of global emissions and represent an increasingly important area of policy concern. The household carbon footprint—defined as the sum of direct and indirect carbon emissions of household consumption along the supply chain—has received increasing attention. In China， the coexistence of various population trends such as household miniaturization （i.e.， a rise in single- and two-person households） and persistently low fertility rates make it crucial to understand the relationship between household characteristics （e.g.， household size and structure） and household carbon footprints. However， research on household carbon footprints based on household characteristics is still in its infancy. To address this issue， the micro-household survey data are combined with the input-output tables to build an extended input-output model that estimates household carbon footprints for different household sizes and structures， covering both urban and rural areas in China. Specifically， the input-output tables and population data are sourced from the National Bureau of Statistics， the carbon emissions inventory is sourced from the Carbon Emission Account and Datasets （CEADs）， and household consumption expenditure data is sourced from the China Family Panel Studies （CFPS）. The results reveal three key findings. First， per capita household carbon footprints decrease with increasing household size， displaying a decreasing marginal contribution due to economies of scale in shared consumption （e.g.， housing and transport）. Second， within households of the same size， those with more children exhibit higher per capita carbon footprints， primarily due to increased spending on culture， education， and entertainment. Third， although urban households generally have higher per capita emissions than rural ones， the gap narrows as household size increases. These results highlight the importance of incorporating household and demographic characteristics into climate mitigation strategies. A better understanding of how household composition influences carbon footprints can inform targeted mitigation strategies and promote more equitable and effective climate action at the household level.