With environmental problems becoming severe, many firms, including HP, Huawei, and Apple are simultaneously implementing trade-in programs and advertising to stimulate market demand. Offering trade-in service by the manufacturer is a method of price discrimination by providing replacement consumers with a rebate when they purchase new products. With the recycled, used products, the manufacturer can benefit through a strict series—via a remanufacturing process. Although numerous literatures have investigated the pricing strategy and advertising decisions in the closed-loop supply chain (CLSC), to the best of our knowledge, there is little research that analyzes and compares the economic and social performance of these two marketing strategies. To fill this gap, we establish two supply chain models with two periods, namely, an advertisement model and a joint model, while the equilibrium purchasing behavior of the replacement consumers can be characterized under three conditions: (1) all of the replacement consumers purchasing new products (ATA); (2) partial replacement consumers purchasing (PTA); (3) no replacement consumers purchasing (NTA). These three conditions are decided by the numerous relationships of the parameters. Solving the optimal decisions of the manufacturer in both models, the critical value in the joint model is higher in the advertisement model, which indicates that developing the trade-in program can enhance the robustness of the business model. Furthermore, through numerical example, we find that the market demand in the joint model is higher than in the advertisement model, and the cost of marketing strategy in the joint model is lower in the advertisement model, which means that the efficiency of the marketing strategy is higher than the single marketing strategy. As a result, comparing the economic and social performance between the two models, we conclude that the advertisement elasticity of the market demand is the key factor of the manufacturer’s profits and total social welfare.
Off-grid algorithms for direction of arrival (DOA) estimation have become attractive because of their advantages in resolution and efficiency over conventional ones. In this paper, we propose a grid reconfiguration direction of arrival (GRDOA) estimation method based on sparse Bayesian learning. Unlike other off-grid methods, the grid points of GRDOA are treated as dynamic parameters. The number and position of the grid points are varied iteratively via a root method and a fission process. Then, the grid gets reconfigured through some criteria. By iteratively updating the reconfigured grid, DOAs are estimated completely. Since GRDOA has fewer grid points, it has better computational efficiency than the previous methods. Moreover, GRDOA can achieve better resolution and relatively higher accuracy. Numerical simulation results validate the effectiveness of GRDOA.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.