The problem of coordinating a two-echelon green product supply chain with environmentally conscious consumers under demand uncertainty is studied in this paper. In the green product supply chain, a manufacturer produces green product through developing green technology and a retailer promotes the green product through green marketing. The demand of the green product is uncertain with the mean and the variance as the only known information. After analyzing the utility that a consumer gained from the green product, the problem is then formulated as a distribution-free-based Stackelberg game for a decentralized system under three contracts, i.e., wholesale price-only (WPO), revenue and green marketing cost-sharing (RGMS), and two-part tariff (TPTF) contracts. The analytical results are also proposed to show the effects of the system parameters on supply chain optimal decisions. By comparing with the centralized system, the coordination level for each contract is investigated. Numerical examples and sensitivity analysis with respect to several system parameters are presented to illustrate the effectiveness of the contracts under demand uncertainty. The results demonstrate that both RGMS and TPTF contracts are superior to WPO contract. Especially, TPTF contract can always coordinate the supply chain, while RGMS contract can improve the supply chain coordination level only if the revenue and cost sharing coefficients satisfy certain conditions. INDEX TERMS Supply chain, green product, green marketing, coordination, demand uncertainty.

It is of great significance to timely and accurately forecast the safety state of the bridge as far as the maintenance is concerned. Bayesian forecasting is a method of deriving posterior distribution in accord with the sampling information and prior information, where real time online forecasting is realized by means of recursive algorithm and the stationary assumption. Bayesian dynamic linear model is created to forecast the reliability of the bridge on the basis of the observed stress information of a bridge structure. According to the observed information, the model created is a superposition of constant mean model and seasonal effect model. The analysis of a practical example illustrates that Bayesian dynamic linear modes can provide an accurate real time forecast of the reliability of the bridge

Subject to various factors under loading, bridges appear to be discrete. Thus, it is unavoidable to take the practical bridge into consideration with regard to the bridge deflection forecasting. Given this, the Bayesian dynamic forecasting theory is introduced to forecast the bridge deflection. Since the bridge deflection can stay stable in a long term, create constant mean discount Bayesian conditional equation and observational equation and deduce the Bayesian posterior probability of the bridge deflection conditional parameters on the basis of the prior information of the parameters. With recursive deduction, the conditional parameters keep updating as observational data are imported. The results of Bayesian forecasting comprise values and confidence interval, which makes it more instructive. Finally, practical examples are adopted to examine the superior performance of Bayesian dynamic forecasting theory.