Research on Energy-Saving Charging Combination Based on Group Genetic Algorithm (GGA) and Best Fit (BF) Algorithm

ABSTRACT

As a traditional high energy-consuming industry, the forging industry consumes a lot of energy. The activity consuming highest energy during forging process is the forging and heating. In order to solve the problem regarding how to separate work pieces with different holding temperature intervals and combine them for charging, a group of work pieces with compatible holding temperatures are analyzed for energy-saving charging combination. To aim this, an optimal charging combination model for t energy saving is proposed as well as a group genetic algorithm based on temperature compatibility rule for problem solving. To obtain better optimal solutions, a BF (best fit) heuristic algorithm is designed to generate individuals and initial population as well as an improved crossover operator. At last an instance is analyzed to verify the effectiveness of the proposed model and algorithm, and compared with the present charging planning used in forging enterprises.

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