The challenge presented by simultaneous buffer and service rate allocation in manufacturing systems represents a difficult non-deterministic polynomial problem. Previous studies solved this problem by iteratively utilizing a generative method and an evaluative method. However, it typically takes a long computation time for the evaluative method to achieve high evaluation accuracy, while the satisfactory solution quality realized by the generative method requires a certain number of iterations. In this study, a data-driven hybrid approach is developed by integrating a tabu search–non-dominated sorting genetic algorithm II with a whale optimization algorithm–gradient boosting regression tree to maximize the throughput and minimize the average buffer level of a manufacturing system subject to a total buffer capacity and total service rate. The former algorithm effectively searches for candidate simultaneous allocation solutions by integrating global and local search strategies. The prediction models built by the latter algorithm efficiently evaluate the candidate solutions. Numerical examples demonstrate the efficacy of the proposed approach. The proposed approach improves the solution efficiency of simultaneous allocation, contributing to dynamic production resource reconfiguration of manufacturing systems.

This study aimed to develop strategic guidelines for content creators to achieve a competitive advantage in the online media industry by constructing a structural equation model (SEM). A mixed-methods approach was used, combining qualitative interviews with nine industry experts, a focus group with eleven specialists, and a quantitative survey of 500 executives from industrial businesses. Data were analyzed using descriptive statistics, inferential tests, and multivariate techniques. The analysis identified four key strategic components: (1) cost effectiveness, (2) data-driven, (3) differentiated creation, and (4) agile marketing. Cost effectiveness emerged as the most critical factor. Hypothesis testing indicated that business duration significantly influenced the prioritization of these components (p < 0.05). The refined SEM demonstrated strong model fit, with CMIN–p = 0.062, CMIN/DF = 1.142, GFI = 0.955, and RMSEA = 0.0173. The findings confirm the model’s applicability in supporting strategic planning and enhancing competitiveness among online content creator businesses.