Research on Improvement of Hotel Supply Chain Resource Management Decision in the Era of Big Data

Research On Improvement Of Hotel Supply Chain Resource Management Decision In The Era Of Big Data

Chinese hotels have significant limitations in terms of internal systems, business models, and related management concepts, compared to some famous foreign hotels (Tortorella et al., 2019). In the fierce competition environment of the global market, the traditional “vertical integration” mode of production and management can no longer make enterprises respond to market demand quickly. In this case, people will naturally extend resources to other places, with the help of hotel resources, to achieve the purpose of quickly responding to market demand, so the idea of “horizontal integration” rises, and a new mode of production and management emerges as the times require (Al-Aomar & Hussain, 2019). Hotel supply chain management (SCM) refers to the material services formed with a hotel as the core object. At present, the main method is centralized SCM, which uses integer programming or mixed integer programming to establish and solve the allocation management of various resources among hotels in the supply chain. The object of supply chain quality management is each node enterprise in the supply chain. E a platform for quality information communication, building a whole supply chain quality assurance system, and achieving effective quality coordination ensure the continuous and stable quality assurance capability of the whole supply chain to enhance its competitiveness (Sari & Suslu, 2018). With the deepening of the concept of SCM, the development of hotels has gradually developed from the previous “vertical integration” mode to “horizontal integration.” It is urgent for hotel development to return to its core business and enhance its competitive advantage.

This paper introduces the ant colony algorithm (ACA) into the hotel SCM mode for optimization, simulates the strategy of ant colony path optimization, and works out a unified material demand, production, and distribution plan through the trade-off between local cost and global cost, so as to unify and coordinate the control of various business departments (Zhang & Ye, 2018). Ant colony optimization algorithm is a new type of simulated evolutionary algorithm (Turken et al.,2020). It is an artificial ACA first proposed by Italian scholars and others on the basis of studying the collective behavior of real ant colonies in nature. Ant algorithm, inspired by the behavior of ants in nature, is a new bionic evolutionary algorithm for solving research on supply chain strategic alliances, partnership, and collaboration (Bhavya & Elango, 2023). A series of management methods for supply chain to improve performance, reduce costs, and other aspects have also been proposed and gradually applied. Because ants can go through a relatively short path in a short time, and more pheromones can be accumulated on the shorter path in the same time, a large number of ants will concentrate on choosing the shorter path after a certain time. The ACA is the way to solve the optimization problem by simulating this group behavior of ant colony (Abou Kamar, 2021).