Research on Storage Space Assignment Optimization in Warehousing-sales Integrated Supermarket
In view of the development of the social economy and constantly growing resident consump-tion level,warehouse-style supermarkets which integrate the functions of warehousing with sales,and whole-sale with retail have gained great popularity.This type of supermarkets relies on warehouse-style commodity display to reduce intermediate storage and secondary transportation costs.Though master package and high turnover of the commodities can cover procurement costs and quality losses,they may also require more ardu-ous handling.However,due to space constraints,large handling devices are not viable options inside supermar-kets.Therefore,it is very necessary to optimize the allocation of cargo space in warehouse-type supermarkets to reduce the handling workload.On the other hand,previous researches on cargo space optimization only examined shelves with only one I/O,that is,the goods were transported from different starting points to a common destination.But in reality,a warehouse-type supermarket needs to move goods from storage shelves to the corresponding sales shelves,that is,from different starting points to different destinations.Therefore,in this paper,in order to reduce the handling workload in warehouse-type supermarkets,taking into account constraints such as shelf center of gravity and aggregation of same-category goods,we established a mathematical model to solve the correspond-ing storage space assignment problem,and assigned goods to suitable slots according to certain assignment rules,while ensuring that goods of the same kind are placed as concentrated as possible,the center of gravity of the shelf does not exceed a certain height,and the time required to move goods to their corresponding sales slots is minimized.Then,to solve the model,we designed an improved adaptive crossover mutation genetic algo-rithm which employed three different neighborhood search operators to improve the mutation operator.The result of a numerical analysis showed that the improved adaptive crossover mutation genetic algorithm is supe-rior to the greedy algorithm and the general genetic algorithm.Moreover,the algorithm could also lead to shorter handling time and more concentrated goods placement.Finally,through analyzing the impact of key parameters on the objective function,it was found that given the same scale,lower storage shelf tiers could lead to smaller objective function outcome,from which,we could learn that when arranging the storage locations,the warehouse managers should reduce total number of shelf tiers as much as possible.
cargo space optimizationwarehousing-sales mixed cargo spacewarehouse-type supermar-ketimproved genetic algorithm
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