2results about How to "Improve local search capabilities" patented technology

The invention provides an active power distribution network low-carbon economic dispatching optimization method based on carbon-energy coupling, and belongs to the technical field of power distribution network dispatching. Comprising the following steps: constructing a double-chromosome model consisting of a carbon chromosome and an energetic chromosome; binding carbon-energy coupling constraint for the carbon chromosome and the energy chromosome, and initializing the double chromosome model; constructing a total cost function including an energy cost item and a carbon constraint penalty item; constructing a selection operator based on the total cost function, a crossover operator based on carbon violation feedback and a mutation operator of the genetic algorithm; constructing a local optimization function of the genetic algorithm; and performing iterative optimization on the double-chromosome model by using the optimized genetic algorithm, and obtaining an optimal scheduling scheme after iteration is finished. According to the method, through the double-chromosome model, the unified cost function, the adaptive genetic operator and the enhanced local optimization function, collaborative optimization of economical efficiency and low-carbon targets of the power distribution network can be realized, and a high-quality optimization result is provided for actual dispatching operation.

This invention discloses a distributed heterogeneous flexible assembly workshop scheduling method with transfer functionality. It includes the following steps: setting assumptions and basic parameters; proposing corresponding constraints based on the problem under study; constructing a bi-objective optimization model with the objectives of minimizing maximum completion time and minimizing total processing energy consumption. This bi-objective optimization model is used to optimize the flexible assembly workshop scheduling considering factory processing capacity, transportation stages, and workpiece transfer. Based on the process sequence, factory selection, and machine selection, a three-segment encoding and decoding mode corresponding to the bi-objective optimization model is designed. Based on the three-segment encoding and decoding modes, an improved multi-objective genetic algorithm based on Q-Learning is used to solve for the optimal scheduling scheme. This invention considers factors such as workshop processing capacity, collaborative processing, and transportation stage time, arranging the process sequence, factory, machine, and assembly workshop selection from a global optimization perspective.