查看更多>>摘要：We consider a joint inventory-pricing control problem in a single-product, periodic-re-view, dual-supplier inventory system. The two suppliers have different lead times. One expedited supplier offers instantaneous replenishment, and one regular supplier requires an L-period lead time for delivery. The supply quantity is stochastic and the demand is price-dependent. For the expedited inventory replenishment, we characterize the optimal policy as a state-dependent almost-threshold policy by extending the stochastically linear in mid-point to a multidimensional setting. To investigate the optimal regular inventory replenishment and pricing policy, we propose the notions of partially stochastic translation (PST) and increasing partially stochastic translation (IPST), which help in obtaining the antimultimodularity preservation in dynamic programming problems. We provide prop-erties, sufficient conditions, and examples for PST and IPST functions. By applying PST and IPST, we obtain the antimultimodularity of the profit functions. The antimultimodular profit functions ensure that the optimal regular ordering quantity and the optimal price are monotone in the current inventory level and outstanding order quantities. Moreover, we reveal that as the time interval increases, the effects of previous outstanding orders on the optimal regular ordering and pricing decisions are decreasing and increasing, respectively. PST and IPST also enable us to further characterize the optimal expedited ordering quantity as decreasing in the inventory level. However, the optimal expedited ordering quantity can be non-monotone with respect to the outstanding order quantities, as shown in the example.
查看更多>>摘要：Intelligent transformation of manufacturers requires smart logistics transformation collaboration, which improves competitiveness. In this study, we construct a Stackelberg game model based on the mutual influence and restriction in the relationship between a manufacturer and a logistics service provider (LSP) undergoing smart logistics trans-formation. We investigate whether cost-sharing (CS) or revenue-sharing (RS) contracts can coordinate the supply chain and suggest a hybrid CSeRS contract to improve performance. We find that, compared with decentralized options, CS and RS contracts achieve a higher level of smart logistics transformation. While the coordination and effectiveness of CS contracts are superior to those of RS contracts, neither can fully coordinate the supply chain. The proposed hybrid CSeRS contract allows the manufacturer to share the LSP's costs before the transformation and its partial revenue after transformation, so that the LSP can reduce its service charge, thereby achieving full supply chain coordination.
查看更多>>摘要：Intermittent demand refers to the specific demand pattern with frequent periods of zero demand. It occurs in a variety of industries including industrial equipment, automotive and specialty chemicals. In some industries or some sectors of industry, even majority of products are in intermittent demand pattern. Due to the usually small and highly variable demand sizes, accurate forecasting of intermittent demand has always been challenging. However, accurate forecasting of intermittent demand is critical to the effective inventory management. In this study we present a band new method-modified TSB method for the forecasting of intermittent demand. The proposed method is based on TSB method, and adopts similar strategy, which has been used in mSBA method to update demand interval and demand occurrence probability when current demand is zero. To evaluate the pro- posed method, 16289 daily demand records from the M5 data set that are identified as intermittent demands according to two criteria, and an empirical data set consisting three years' monthly demand history of 1718 medicine products are used. The proposed mTSB method achieves the best results on MASE and RMASE among all comparison methods on the M5 data set. On the empirical data set, the study shows that mTSB attains an ME of 0.07, which is the best among six comparison methods. Additionally, on the MSE mea- surement, mTSB shows a similar result as SES, both of which outperform other methods.
查看更多>>摘要：This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.
查看更多>>摘要：Social responsibility investment (SRI) has attracted worldwide attention for its potential in promoting investment sustainability and stability. We developed a three-step framework by incorporating environmental, social, and governance (ESG) performance into portfolio optimization. In comparison to studies using weighted ESG rating scores, we constructed a data envelopment analysis (DEA) model with quadratic and cubic terms to enhance the evidence of two or more aspects, as well as the interaction between the environmental, social, and governance attributes. We then combined the ESG scores with financial in-dicators to select assets based on a cross-efficiency analysis. The portfolio optimization model incorporating ESG scores with selected assets was constructed to obtain a social responsibility investment strategy. To illustrate the effectiveness of the proposed approach, we applied it in the United States industrial stock market from 2005 to 2017. The empirical results show that the obtained SRI portfolio may be superior to traditional investment strategies in many aspects and may simultaneously achieve the consistency of investment and social values.
查看更多>>摘要：The efficient evacuation of people from dangerous areas is a key objective of emergency management. However, many emergencies give little to no advanced warning, leading to spontaneous evacuation with no time for planning or management. For large emergencies, destinations become less certain, with traffic demand imbalanced and concentrated on a few oversaturated routes familiar to evacuees. Ultimately, this leads to rapid congestion and delay on some routes, while others remain barely used, extending clearance times with an accumulating population at risk. In this study we address these issues through incorporating spatio-temporal traffic resilience dynamics into a destination choice model utilizing the available capacity of the overall network. We validate our model through a post-concert egress event. The results suggest that our method can reduce total egress times and average travel time by 20％e43％ over the no-guidance condition. Our method can be used to estimate and quantify emergency conditions to optimally guide destinations and routing choice for evacuees and/or autonomously moving vehicles during evacuations.
查看更多>>摘要：This paper analyzes the influence of downside risk on defaultable bond returns. By introducing a defaultable bond-trading model, we show that the decline in market risk tolerance and information accuracy leads to trading loss under downside conditions. Our empirical analysis indicates that downside risk can explain a large proportion of the variation in yield spreads and contains almost all valid information on liquidity risk. As the credit level decreases, the explanatory power of downside risk increases significantly. We also investigate the predictive power of downside risk in cross-sectional defaultable bond excess returns using a portfolio-level analysis and Fama-MacBeth regressions. We find that downside risk is a strong and robust predictor for future bond returns. In addition, due to the higher proportion of abnormal transactions in the Chinese bond market, downside risk proxy semi-variance can better explain yield spreads and predict portfolio excess returns than the proxy value at risk.
查看更多>>摘要：Healthcare waste (HCW) management plays a vital role in the development of modern society. In HCW management, failure mode and effects analysis (FMEA) is a popular method to implement risk management for improving the quality of healthcare. However, the shortcomings of the traditional FMEA method have been widely discussed in litera-tures. This paper proposes an information fusion FMEA method based on 2-tuple linguistic information and interval probability. The 2-tuple linguistic set theory is adopted to change the heterogeneous information into interval numbers. Meanwhile, the interval probability comparison method is applied to analyze failure modes. Finally, a case study is presented to verify the reliability and effectiveness of the proposed method by comparing different FMEA methods.
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