According to China’s economic green ecological sustainability development requirement, the energy reform of China is mainly increasing the proportion of renewable energy, and reducing the proportion of fossil energy. It will continue to force China’s thermal power units, especially coal-fired thermal power units, to carry out the flexibility transformation and upgrading of deep peak shaving ability. Due to the different characteristics of coal-fired thermal power units, it is necessary to make flexible transformation decisions by a scientific and reasonable decision-making evaluation method, so as to provide references for the one machine-one policy flexibility transformation of thermal power units. In this paper, a decision-making evaluation index system for the flexibility transformation of coal-fired thermal power units under the demand of deep peak shaving is established. The index system considers the impact of deep peak shaving on the boilers, steam turbines, and auxiliary equipment of coal-fired thermal power units, as well as the effects of the peak shaving. A hybrid evaluation method combined set-valued iteration and GRA-TOPSIS is employed to obtain the weight of the indexes. Finally, an empirical research was conducted based on the index system and the hybrid evaluation method and targeted “one machine, one policy” recommendations were put forward for the flexibility transformation of the coal-fired thermal power units.

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The rapid development of e-commerce and artificial intelligence technology has led to the rapid development of unmanned warehousing automation technology in the logistics industry. Unmanned warehousing and automated guided vehicle (AGV) equipment in unmanned warehousing have also increased. Since the AGV needs to be charged, based on the traditional simple path optimization, if the sorting efficiency of logistics needs to be further improved, the charging problem of the AGV needs to be considered. This paper constructs a multi-AGV path optimization model in an unmanned storage environment based on the charging utilization rate. The model takes the shortest path and the highest charging utilization rate as the dual goals, and selects the genetic algorithm as the method to solve the model, which is verified by simulation experiments. The proposed model and algorithm have certain validity and feasibility.