Research On The Optimization Of Storage Location Allocation Of Automated Warehouse Based On MOGAPSO Algorithm

As the core of the warehousing system,automated three-dimensional warehouses have the characteristics of mechanized access,process information,and storage space rationalization,which have attracted more and more attention from modern enterprises.However,some enterprise automated three-dimensional warehouses unilaterally pursue mechanization of warehousing,and adopt computer-assisted random storage mode to allocate cargo positions.Problems such as chaotic cargo storage,storage congestion,high equipment energy consumption,and shelf dumping not only affect the efficiency of the storage system,but also endanger The core production capacity of the enterprise and increase the operating cost of the enterprise.Before the goods are put into storage operations,the reasonable allocation of the storage space of the automated three-dimensional warehouse according to the production requirements and the characteristics of the goods is the key to improving the production capacity of the enterprise and reducing the operating cost of the enterprise.Under the premise of analyzing domestic and foreign cargo location allocation optimization objectives and the current research status of the solution algorithm,according to the cargo location allocation theory,multi-objective optimization theory,genetic algorithm and particle swarm algorithm theory,the optimization research of the automated warehouse location allocation is carried out.Determine the location allocation strategy and principle of the automated three-dimensional warehouse to improve the efficiency of goods in and out of the warehouse,maintain the overall stability of the shelf and the nearby storage of relevant goods as the optimization goal,and use the mathematical modeling method to build a multi-objective optimization model of the location allocation.On this basis,the MOGAPSO algorithm based on the NSGA-? algorithm and the MOPSO algorithm serially mixed longitudinally is proposed.The fast non-dominated sorting individual based on the crowding degree distance is stored in the external file,and the population diversity is increased through genetic operations,and the dominance relationship is used to update the external File,and then select the global optimal position according to the distance of individual crowding degree to adjust the speed and position of the individual.Finally,the computer simulation method is used to optimize the cargo location allocation problem of XZ Agricultural Products Company by using the MOGAPSO algorithm.The results show that compared with the original solution using random storage,the optimization scheme has significant optimization effects in terms of goods in and out of storage,overall shelf stability,and nearby storage of related goods,and the MOGAPSO algorithm is better than the NSGA-II algorithm and the MOPSO algorithm.It has stronger performance and faster convergence speed,which provides theoretical and practical reference for enterprises to optimize cargo location according to actual storage needs.