Production furnace grouping is critical in routine operation of NdFeB processes and the grouping results directly affect production efficiency. Based on actual requirements of a production unit

a mathematical model of grouping performance index

constraint conditions and decision variable was established and algorithm of best priority and variable neighborhood search was proposed for furnace grouping. There were three components of the algorithm

which included multi-layer quick sorting algorithm to determine furnace sequences

best priority and variable neighborhood search algorithm

and heuristic algorithm based on production rules for various grade of raw materials in stock. By using this algorithm for furnace grouping of 20 production work orders

sum of deviations of delivery time

product grade

and production priority was decreased from 58 to 42 with a reduction rate of 27.59% and satisfaction rate of raw material preparation was increased from 4 to 6 with an increase rate of 50%. Compared to results of manual furnace grouping for 40 production work orders

the furnace grouping results of this algorithm was reduced by 2 times of furnace usage. Further comparison with discrete particle swarm algorithm

acoustic variable neighborhood search algorithm

and adaptive variable neighborhood search algorithm demonstrated effectiveness of the proposed algorithm and validity of the mathematical model.