基于Multi-Agent的家用汽车供应链演化分析

为探索多型号产品供应链的经营策略,文中运用多智能体(Multi-Agent)建模仿真方法建立了多型号家用汽车供应链网络模型。以制造商Agent利润最大化为目标,分别采用刺激-反应学习机制和粒子群学习机制对模型进行演化分析。仿真表明:(1)采用刺激-反应学习机制,制造商的部分车型的销量会大幅降低,不利于追求多车型的制造商对多车型的推广,但总销量和利润均会提高。(2)采用粒子群学习机制,无论C 1和C 2如何组合,都难以同时得到销量和利润的最优。对于大众消费车型,可以选择加强C 2,以“薄利多销”的策略拓展市场,提高销量。对于高端消费车型,可以选择加强C 1,以“厚利适销”的策略制造精品,提高利润。(3)相对而言,采用粒子群学习机制的制造商能快速调整策略以应对市场变化,并且学习后策略更稳定。该研究对具有多型号产品的供应链管理具有实际的指导意义。

Evolution Analysis of Household Car Supply Chain Based on Multi-Agent

In order to explore the business strategy of multi-model product supply chain,this paper used Multi-Agent modeling and simulation method to establish a multi-model home vehicle supply chain network model.Aiming at maximizing the profit of manufacturer Agent,stimulus-response learning mechanism and the particle swarm learning mechanism are used to analyze the evolution of the model.The simulations show that,firstly,by using the stimulus-response learning mechanism,the sales volume of some models of the manufacturer will be greatly reduced.For manufacturers pursuing multiple models,it is not conducive to the promotion of multiple models,but the total sales volume and profits will increase.Secondly,by using the particle swarm learning mechanism,no matter what combination of C 1 and C 2 is,it is difficult to obtain the optimal sales and profit at the same time.For mass-consumer models,manufacturers can choose to strengthen C 2 and expand the market with“small profits but quick turnover”strategy to increase sales.For high-end consumer models,manufacturers can choose to strengthen C 1 and create high-quality products with a“benefit and profitable”strategy to increase profits.Relatively speaking,by using particle swarm learning mechanisms,manufacturers can quickly adjust strategies to cope with market changes,and the strategies are more stable after learning.This study has practical guidance for supply chain management with multiple models.