Multi-period Multi-product Supply Chain Network Design Under Price And Quality Competition

In recent years,with increasingly fierce market competition,the low level of price war and low quality services no longer meet the demand of the existing market competition,on the other hand,consumer demand for product diversity is also more complicated,the cycle is no longer a single in the new situation,the way alone out of the competition between enterprises has gradually tend to be integrated into the supply chain of synergetic competition and win-win situation,to consider how to both price and quality competition under the multiple cycles of new products major decisions related to supply chain network design problem can be combined with enterprise market decision effectively,has certain theoretical and practical significance.Based on this background,the related research at home and abroad on the basis of summarizing and analyzing the characteristics of the traditional supply chain network design problem,aiming at the shortcomings of the existing research,a multi-period multi-product supply chain network model with profit maximization is designed considering the price and quality competition environment.The supply chain model needs to provide multiple types of products to specific demand market area in multiple periods,in which the demand price function of a retailer is related to the retail prices of all retailers in the supply chain.The model also considers the real situation,and the decision-making process considers the specific random customer behavior based on the attractiveness function model,which depends on the location and quality level of the retailer at the same time.On this basis,the model needs to make decisions on the facility location of manufacturers and retailers,production type,distribution quantity and pricing.In order to solve the mixed integer nonlinear programming model of the supply chain network design problem,which cannot be directly solved by the traditional commercial solver,two heuristic optimization algorithms,namely simulated annealing algorithm and particle swarm optimization algorithm,were used to solve the problem.In order to verify the generality of the model and the effectiveness of the proposed algorithm,three sets of numerical examples of different sizes were analyzed in the simulation experiment.After the comparison and analysis of the experimental results,it is concluded that the simulated annealing algorithm is better than the particle swarm optimization algorithm for solving the model of this problem.In order to verify the stability of the algorithm,under different scales,the two algorithms in 10 runs were recorded by five common evaluation parameters of mean standard deviation,maximum and minimum median values,as well as the degree of model performance volatility measured by the median.In addition,further through analyzing the sensitivity of the three sensitivity coefficient,explore the influence of the retailer's self-price sensitivity coefficient and the demand price sensitivity coefficient of the rival retailer on the profit of the supply chain,and the influence of the distance sensitivity coefficient on the decision-making of the retailer,thereby on the basis of the simulation experiment shows some management implications,which provides supply chain network design optimization problems with decision support.