Multi-objective Optimization of Stereoscopic Shelf Space Allocation Based on Adaptive Non-repetitive Genetic Algorithm

The random allocation of shelf space in an automated stereoscopic warehouse not only affects the stability of the shelves but also reduces the efficiency of items entering and leaving the warehouse. Genetic algorithms are most frequently used to solve the optimal allocation of shelf space; however, with an increase in the number of algorithm iterations, there will be a greater probability of repeated allocation of shelf space. To find the optimal and non-repetitive allocation of shelf space, this paper proposes a new method to solve the shelf space allocation problem using an Adaptive Non-repetitive Genetic Algorithm (ANGA) and introduces four different shelf space allocation principles: shelf stability principle, proximity storage principle, aggregation principle and correlation principle. A shelf space allocation optimization model is established based on four principles, and ANGA is proposed to solve it. Through case analysis, the minimum value of the obtained objective function is compared with the initial values of randomly allocated shelf space and the values obtained using traditional genetic algorithms. The results demonstrate that the proposed method for shelf space allocation optimization not only effectively avoids the problem of repeated shelf space allocation but also significantly improves the stability of shelves and the efficiency of items entering and leaving the warehouse.