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In the increasingly competitive market, supply chain decision-makers are making efforts to improve operational efficiency and reduce costs by joint replenishment approach. Recognizing the value of joint replenishment strategy in the supply chain, we are motivated to write a review on the importance of joint replenishment strategy. Despite the vast literature on the joint replenishment problem (JRP), a comprehensive study survey for recent years is lacking. The goal of this study is to review and synthesize research on JRP from 2006 to 2022. Details of JRP are introduced first. Literature selection and an overview of the extant literature are then discussed. Recent research on JRP with relaxed assumptions is summarized, including stochastic demand, dynamic demand, and resource constraints. In addition, recent research on other JRPs and the joint replenishment and delivery (JRD) problem is summarized. The observations and insights of these studies can guide academics and practitioners to implement joint replenishment strategies in different aspects of supply chain management.

Deterministic optimization has been successfully applied to a series of design problems of square thin-walled energy absorption tubes and to a certain extent has fulfilled great expectations for the application of such structures in subway vehicles. However, most studies have not considered the uncertainty of parameters or the correlation of uncertainty parameters, leaving little or no tolerance and resulting in over-conservative structural design. This research proposes a multi-objective uncertain method with an ellipsoid-based model to address the effects of parametric uncertainties of a centrally symmetrical square tube with diaphragms (CSSTD) on design optimization, in which the ellipsoid model is adopted to describe the related uncertainty parameters. The nonlinear interval number programming method coupled with a reliability-based possibility degree of interval (RPDI) model is introduced to handle the transformation of uncertain optimization problems. Simultaneously, local-densifying technology is adopted to enhance the local accuracy of the approximate model. Finally, the outer layer of the micro multi-objective genetic algorithm (μMOGA) combined with the inner layer of the intergeneration projection genetic algorithm (IP-GA) is applied to solve the Pareto optimal solution set of the transformed deterministic optimization. The optimization results indicate that the proposed multi-objective uncertain optimization with an ellipsoid-based model not only guarantees the crashworthiness of the CSSTD, but also improves the design robustness, which means that the proposed method can provide insightful information for crashworthiness design of subways.

Organic carbon derived from terrestrial plants contributes to aquatic consumers, e.g., zooplankton in lakes. The degree of the contribution depends on the availability of terrestrial organic carbon in lake organic pool and the transfer efficiency of the carbon. Terrestrial organic carbon is poor-quality food for zooplankton with a mismatch of nutrition content and was incorporated to zooplankton with much lower efficiency than phytoplankton. Contributions of terrestrial carbon to zooplankton generally decrease with an increase in phytoplankton production, indicating a preferential incorporation of phytoplankton in previous investigations. However, in eutrophic lakes, the dominating cyanobacteria were of poor quality and incorporated to consumers inefficiently too. In that case, zooplankton in eutrophic wetlands, where cyanobacteria dominate the phytoplankton production and massive terrestrial plants are inundated, may not preferentially incorporate poor food-quality phytoplankton resource to their biomass. Therefore, we hypothesize that carbon contributions of terrestrial vegetation to zooplankton and to lake particulate organic pool should be similar in such aquatic ecosystems. We tested this hypothesis by sampling zooplankton and carbon sources in Ming Lake (Jinan University Campus, southern China) which was overgrown by terrestrial plants after drying and re-flooded. After 60 days of observations at weekly (or biweekly) intervals, applying stable carbon (

Based on the hot compression experiment of GH4169 superalloy, the constitutive equation and the microstructure evolution model of GH4169 superalloy were established, and they were introduced into the Deform® simulation software to realize the simulation of the microstructure evolution of GH4169 superalloy in closed multidirectional forging, single-sided open multidirectional forging, and bilateral open multidirectional forging. The neural network prediction model of the microstructure of GH4169 superalloy in multidirectional forging was established using MATLAB, and the neural network prediction of microstructure evolution in three kinds of multidirectional forging was determined. Experimental results show that the prediction error of the two methods for the recrystallized grain size doesn't exceed 10%, and the error under high pass is small. For the recrystallization volume fraction, the prediction errors of the two methods are less than 3.5%, and the prediction error of the neural network for higher pass deformation is lower than the finite element simulation. The established neural network model has high prediction accuracy and can be used to predict the microstructure evolution of GH4169 during hot deformation.

In the original publication, the given names and surnames of the authors were swapped in one of the references and hence incorrectly cited as Blal et al. 2017 in the article.