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Since the limitation of carbon emissions, China's photovoltaic (PV) industry has developed vigorously, while some traditional heavy industries have been violently hit. Therefore, the industrial production data exhibits significant nonlinear and complexity characteristics, which may affect prediction accuracy, thus hindering the corresponding department's decision-making. Consequently, a novel structure-adaptive fractional Bernoulli grey model is presented in this paper to surmount this toughie, and the core innovations can be summarized as follows. Initially, a novel time function term is utilized to depict the accumulative time effect, which can smoothly represent the dynamic variations and significantly strengthen the robustness of the new model. Besides, the fractional-order accumulation technique, which could effectively improve the predicting accuracy, is employed in the proposed model. Furthermore, the adaptability and generalizability of the proposed model can be enhanced by the self-adaptive parameters optimized by the Particle Swarm Optimization. For illustration and verification purposes, experiments on forecasting the annual output of Photovoltaic modules in China and the annual output of steel in Beijing are compared with a range of benchmarks, including the classic GM (1, 1), conventional econometric technology, and machine learning methods. And the results confirmed that the proposed model is superior to all benchmark models, which indicates that the novel model is indeed suitable for industrial production forecasting. [ABSTRACT FROM AUTHOR]

This paper studies a location-allocation problem to determine the selection of emergency shelters, medical centers, and distribution centers after the disaster. The evacuation of refugees and allocation of relief resources are also considered. A mixed-integer nonlinear multiobjective programming model is proposed to characterize the problem. The hierarchical demand of different refugees and the limitations of relief resources are considered in the model. We employ a combination of the simulated annealing (SA) algorithm and the particle swarm optimization (PSO) algorithm method to solve the complex model. To optimize the result of our proposed algorithm, we absorb the group search, crossover, and mutation operator of GA into SA. We conduct a case study in a district of Beijing in China to validate the proposed methodology. Some computational experiments are conducted to analyze the impact of different factors, such as the target weight setting, selection of candidate shelters, and quantity of relief resources. [ABSTRACT FROM AUTHOR]