Tourism supply chain resilience assessment and optimization based on complex networks and genetic algorithms

Abstract

Tourism supply chain (TSC) resilience is a measure of the TSC's response to external risks. Currently, intelligent models related to TSC resilience are basically blank. This article is based on the Collaborative Planning Forecasting and Replenishmen (CPFR) model to study the supply chain collaboration mode of smart tourism, providing a train of thought for the research of smart tourism, the purpose is to further improve the accuracy of tourism supply chain toughness assessment, and provide theoretical support for scenic spots to improve their own supply chain toughness. Simultaneously combining machine learning methods to construct a supply chain collaborative prediction model provides a new approach for collaborative prediction in the supply chain. This paper proposes a collaborative model of smart TSC based on CPFR, which not only reflects the operation process of smart TSC, but also incorporates the idea of CPFR to integrate the smart TSC into a system that can operate stably and effectively. Moreover, this paper proposes a resilience evaluation and forecasting algorithm of TSC combining complex network and genetic algorithm with genetic algorithm. In addition, this paper predicts the ability of TSC to cope with external shocks while assessing the resilience of TSC. Finally, according to the experimental research results, the model can converge after 50 iterations, and the prediction error accuracy of the test set is 5.68%, which is improved compared with the existing models The most important influencing factor in the evaluation of tourism supply chain elasticity is the tourist attractions themselves, followed by the economic environment and tourism facilities and services. Under the premise of investment level of 100, the evaluation results of the three are 33.25, 19, 19, respectively. The model proposed in this paper can realize the early forecasting of the TSC, improve the ability of the TSC to cope with risks, and promote the effective improvement of the resilience of the TSC.