Abstract: In the context of the booming development of Internet technology, people's lifestyle is constantly changing, and the functional demand of the Internet is also increasingly emphasized. As an important livelihood issue closely related to people's life and health, the combination of medical care and information technology is very necessary. During the rapid development of online retail industry, it coincides with the government departments, continuous introduction of policies to encourage medical institutions to use the Internet to provide more convenient and efficient diagnosis and treatment services for the public, and the online medical form emerges at the historic moment. It not only has basic medical services such as online consultation and online prescription, but also provides users with drug distribution services. Compared with other commodities, drugs have the particularity of strong rigid demand and complex categories, which lead to higher requirements for timeliness and safety in the process of drug distribution. Therefore, in the process of drug distribution, how to comprehensively consider the characteristics of drug distribution, rationally plan the distribution path, so as to improve the distribution efficiency and achieve the optimal cost has become an urgent problem to be solved in drug distribution.
Firstly, based on the fact that the user's satisfaction with the delivery service will affect the delivery cost in the process of drug delivery, and the drug distribution adopts the principle of non-contact delivery, the rider will not cause losses to the user when the order is delivered earlier than the expected time window of the delivery center, that is, the penalty coefficient before the earliest expected delivery time of the system is 0. Combined with the waiting cost caused by the lack of inventory in chain pharmacies and the delay in taking medicine caused by other merchants, a PDVRP model with asymmetric soft time window considering customer satisfaction is established. The total cost of delivery in the model is composed of three parts: transportation cost, waiting cost and penalty cost, and the path optimization is carried out with the goal of minimizing the total cost.
Secondly, considering the NP-hard characteristics of the delivery path problem with time window, in order to improve the problem of insufficient local search ability and slow convergence speed of the genetic algorithm, an improved genetic algorithm based on Cauchy mutation strategy is designed. The chromosome representing the delivery route of the rider is constructed by integer coding, the reciprocal of the objective function is taken as the fitness function, and the ranking-based roulette selection is selected to eliminate the huge difference of the fitness value and make the advantages of the better individuals more obvious. A new adaptive forward continuous crossover operator is designed to retain its own excellent genes and avoid code loss, and the Cauchy mutation strategy is integrated to jump out of the local optimum and enhance the global search ability of the algorithm.
Finally, in order to verify the effectiveness of the model, two different test sets are generated based on the order-related data of a platform given in the reference. The model is solved by simulation under the two different test sets, and the rider order allocation, path arrangement and cost distribution under the optimal delivery path are obtained. Then the model is solved by the traditional genetic algorithm and the improved genetic algorithm without the fusion of Cauchy mutation, and the evolution curves of the three algorithms and the cost structure under the optimal solution of each algorithm are compared. The results show that the convergence speed and the optimal solution of the improved genetic algorithm with the fusion of Cauchy mutation are better than the other two algorithms. Therefore, the effectiveness and superiority of the model and algorithm in this paper are verified.
In summary, the research content of this paper improves the problems of previous studies, such that few studies comprehensively have considered the particularity of drug distribution and customer satisfaction, but most have ignored the benefits of drug distribution efficiency, resulting in excessive penalty cost and too ideal inventory estimate for chain pharmacies. An improved genetic algorithm combined with Cauchy mutation, which can improve the convergence speed of the algorithm and jump out of the local optimum, is proposed for the optimization scheme of drug distribution path planning for chain pharmacies, which has certain reference significance for future drug distribution research. In further research, the dynamic changes of order quantity and actual road conditions can be introduced into the cost model, and various complex problems such as the remaining life of distribution vehicles can be considered to expand the application scenarios of the algorithm.

Key words: user satisfaction, asymmetric soft time window, path planning, Cauchy variation, genetic algorithm

摘要： 医疗作为与人民生命健康密切相关的重要民生问题,与信息技术的结合十分必要。连锁药店逐渐从传统的线下销售转变为线上线下融合的新模式,针对药品配送时效性差和成本高的问题,结合其品种多批量小的特征,在考虑用户满意度的基础上,以运送成本、等待成本和惩罚成本构成的配送总成本最小为目标,构建带非对称软时间窗的药品配送路径规划模型。对选择和交叉算子进行改进,设计融合柯西变异策略的改进遗传算法以提高全局搜索能力和收敛速度。通过实例仿真对模型进行求解,将本文算法与未融合柯西变异策略的改进遗传算法及传统遗传算法进行对比分析。结果表明,该模型能够有效提高用户满意度并且降低药品配送成本,改进遗传算法搜索最优成本的效率较高并且总成本较低,验证了模型及算法的有效性。结论为药品配送提供合理的路径优化方案。

关键词: 用户满意度, 非对称软时间窗, 路径规划, 柯西变异, 遗传算法