effective inventorymanagement will directly affect the overall performance of an enterprise.
effective inventory management can improvecustomers' satisfaction and sales, improve financial performance ofenterprises, further improve the efficiency of enterprises in the fields ofsite, production capacity, equipment, construction, capital and employees, andalso have an impact on the market area, helping enterprises to further expand .themain purpose of inventory management is to analyze and control the cost ofinventory, to minimize the cost of inventory, in order to obtain more profits.
inventory cost refers to the sum of allkinds of expenses incurred by inventory, which consists of purchase cost, orderor production preparation cost, inventory holding cost and out-of-stock cost.these are the four most important components of inventory cost, and the mainresearch object of this paper is also these four kinds of costs.
the basic content ofthis paper is to use the improved genetic algorithm to study the inventorymanagement of distributed warehouse: according to the inventory situation ofeach warehouse, when the total inventory falls to the total order point, howcan each warehouse jointly order from the coordination center to a supplier; whensome warehouse inventory falls to the order point, and the total inventory doesnot fall to the total order point, how can each warehouse adjust each other.
genetic algorithm is an ordered globalstochastic optimization search algorithm, which avoids the possibility ofgeneral search algorithm falling into local optimum. by simulating biologicalevolution, the global optimal or approximate global optimal solution can beobtained, and the function to be optimized is basically unlimited. it requiresneither continuity nor differentiability. it can be an explicit functionexpressed by mathematical analytic expressions, a mapping matrix, or even animplicit function such as a neural network. it has strong adaptive and learningfunctions, and is also applicable to distributed inventory problem.
inthis paper, genetic algorithm is used to construct and solve the distributedinventory problem. an improved genetic algorithm is proposed and its operationprocess and results are analyzed.
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