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.
This study proposes an effective wind speed forecasting model combining a data processing strategy, neural network predictor, and parameter optimization method. (a) Variational mode decomposition (VMD) is adopted to decompose the wind speed data into multiple subseries where each subseries contains unique local characteristics, and all the subseries are converted into two-dimensional samples. (b) A gated recurrent unit (GRU) is sequentially modeled based on the obtained samples and makes the predictions for future wind speed. (c) The grid search with rolling cross-validation (GSRCV) is designed to simultaneously optimize the key parameters of VMD and GRU. To evaluate the effectiveness of the proposed VMD-GRU-GSRCV model, comparative experiments based on hourly wind speed data collected from the National Renewable Energy Laboratory are implemented. Numerical results show that the root mean square error, mean absolute error, mean absolute percentage error, and symmetric mean absolute percentage error of this proposed model reach 0.2047, 0.1435, 3.77%, and 3.74%, respectively, which outperform the benchmark predictions using popular parameter optimization methods, data processing techniques, and hybrid neural network forecasting models.
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