Study On The Lead-time Compression Of Fast Fashion Product Based On Improved SA Algorithm

The rapid development of economy makes the market demand tendency of diversification and individuation, consumers are pursuing the fashion and low price fast fashion product. Fast fashion product has the characteristics of price sensitivity, short life cycle, high speed of new product, timeliness and intensive demand fluctuation, which requires enterprises to improve the ability of the back-end operations, such as delivery speed, new product speed and replenishment speed, etc.To shorten the lead time can significantly enhance the enterprise's ability to response market demand, but the compression cost of lead time is also a part of the enterprise can't be ignored. Efficiently shorten the lead time will play a crucial role on the improvement of enterprise competition ability. This paper mainly studied the following contents:(1) Build the fast fashion product lead time compression model. To solve the long lead time in the fast fashion product online retailing, in this paper, I will analyze the causes and characteristics of fast fashion product and discuss different product structures. According to the characteristics of attract product, profit product and promotion product put forward the corresponding requirement for lead time compression, then build minimum cost lead time compression model with service level constraint, maximum profit lead time compression model with service level constraint and maximum compression time lead time compression model with cost constraint.(2)Improve simulated annealing algorithm. In the process of solving the model, to accelerate the solving speed and improve the quality of solution, this paper based on the very fast simulated annealing thought propose an adaptive step annealing model, which makes the algorithm search the whole solution space as far as possible when it is in the high temperature, reduce the search range when it is at the low temperature. So the simulated annealing algorithm won't miss the optimal solution and speed up the convergence. Moreover because of the simulated annealing algorithm to avoid falling into local optimum, it will accept a bad solution at a certain probability, which bring the risk of lose the optimal solution in the reception process. Therefore I increase the local optimal solution temporary storage function in the algorithm, which will get the local optimal solution of each loop, and compare them to get the optimal solution. Then it avoids the algorithm falling into local optimum, and losing the optimum solution.(3) Experimental simulation and result analysis. Using MATLAB program to simulate the models, through contrast the traditional algorithm with improved algorithm in the optimization time and the optimum result. It can be seen that the convergence speed of the improved algorithm is fast and the accuracy is high, which shows that the improved algorithm is effective. Analyze the results of the models, by satisfying the attract product, profit product and promotion product lead time compression requires to explore the strategy of compression in advance under different conditions and provide guidance for business decision.