(1.School of Advanced Manufacturing， Fuzhou University， Quanzhou 362251， China； 2.Quanzhou Institute of Equipment Manufacturing， Haixi Institutes， Chinese Academy of Science， Quanzhou 362216， China； 3.Fujian Seven Brand Fashion & Technology Co.， Ltd.， Quanzhou 362200， China)
Abstract： Aiming at the problem that the clothing industry is affected by fashion trends and the material model is updated rapidly, which leads to the confusion of warehouse inventory structure and low efficiency of operation, a storage location optimization method combining Apriori algorithm with improved genetic algorithm (AIGA) is designed. First of all, Apriori algorithm mines the association rules between material groups, combines the materials with strong correlation to form a large class of warehouse area, and dynamically adjusts the location of the warehouse area according to the large class picking frequency. Secondly, based on the material correlation and picking frequency, a storage allocation model is established with the main objective of minimizing the picking distance. The storage allocation search is carried out by genetic algorithm, and the initialization, crossover and mutation operators of genetic algorithm are improved, and the catastrophe mechanism is designed to improve the search performance of the algorithm. The results show that compared with the existing storage allocation scheme, the picking distance is reduced by 23.85% on average, and the warehouse operation efficiency is effectively improved.
Key words : location allocation；association rules；genetic algorithm；catastrophic operation；clothing accessories warehouse
