Quantum-like Bayesian Network Decision-making Method And Its Application In The Pharmaceutical Logistics Distribution Selection

In recent years,the emergence of a new medical diagnosis and treatment model that combines online and offline has also driven the further development of pharmaceutical logistics.How to choose a suitable pharmaceutical logistics distribution mode becomes a key issue.Due to the particularity of pharmaceutical products,it is necessary to consider not only the scale of the enterprise,logistics capabilities,etc.,but also the satisfaction of customers receiving pharmaceutical products when choosing a distribution plan.In this thesis,a Quantum-like Bayesian network model is introduced to conduct basic theoretical research on multi-attribute decision-making evaluation,and a whole-process model of decision-making evaluation is constructed for the pharmaceutical logistics distribution model.First of all,this thesis introduces a quantum-like Bayesian network model for the multi-attribute decision-making problem in uncertain situations to identify the correlation between evaluation indicators.The decision-making preference of the decision-maker is transformed into objective data for calculating the final weight,and the degree of correlation between the evaluation indicators is calculated in combination with Deng entropy.Then,based on the behavioral decision-making method,an uncertain multi-attribute decision-making method based on Quantum-like Bayesian network is proposed,and the prioritization results of the alternatives are obtained.Finally,the validity and rationality of the proposed multi-attribute decision-making model are verified by an example.Secondly,this thesis systematically describes the evaluation problem of multi-attribute decision-making under uncertain circumstances,and defines the process and specific calculation steps.According to Deng entropy,the potential correlation between the attributes of the medical logistics distribution mode optimization problem is identified,and the uncertainty in the evaluation information of the decision makers is retained to determine the optimal solution.Finally,this thesis applies the proposed Quantum-like Bayesian network model to the optimization of Jilin Pharmacy’s logistics distribution mode,establishes an optimal index system for pharmaceutical logistics distribution mode,collects the qualitative evaluation results of decision makers for calculation,and analyzes the evaluation results.The optimization of the pharmaceutical logistics distribution mode involves multiple departments and multiple subjects and presents the characteristics of complex risk factors and high uncertainty.Therefore,more effective methods are urgently needed in the process of problem solving.The Bayesian network model proposed in this thesis considers the interference between the two properties,so the calculated probability value is more convincing and objective.From the subjective and objective information,the information is effectively extracted,the attribute weight is more reasonable,and the interference of the subjectivity of experts is effectively reduced in the calculation process.