With the rapid development of machine learning and artificial intelligence, hotel service robots are widely used, but there are many problems to be solved in the scheduling scheme of hotel service robots. In this study, the Pareto optimal definition is used to model the problem, and a nondominated sorting heuristic method including genetic algorithm and differential evolution algorithm is designed to solve this problem. Experimental results show the effectiveness and stability of our algorithm. In addition, compared with the previous methods, the method proposed in this paper can provide a more personalized and reasonable service robot scheduling scheme for hotels. Finally, the hotel can optimize its management and operation and further deepen the degree of hotel intelligence.
Purpose
The purpose of this paper is to consider three factors, namely, intra-week demand fluctuations, interrelationship between the number of robots and order scheduling and conflicting objectives (i.e. cost minimization and customer satisfaction maximization), to optimize the robot logistics system.
Design/methodology/approach
The number of robots and the sequence of delivery orders are first optimized using the heuristic algorithm NSGACoDEM, which is designed using genetic algorithm and composite difference evolution. The superiority of this method is then confirmed by a case study of a four-star grade hotel in South Korea and several comparative experiments.
Findings
Two performance metrics reveal the superior performance of the proposed approach compared to other baseline approaches. Results of comparative experiments found that the consideration of three influencing factors in the operation design of a robot logistic system can effectively balance cost and customer satisfaction over the course of a week in hotel operation and optimize robot scheduling flexibility.
Practical implications
The results of this study reveal that numerous factors (e.g. intra-week demand fluctuations) can optimize the performance efficiency of robots. The proposed algorithm can be used by hotels to overcome the influence of intra-week demand fluctuations on robot scheduling flexibility effectively and thereby enhance work efficiency.
Originality/value
The design of a novel algorithm in this study entails enhancing the current robot logistics system. This algorithm can successfully manage cost and customer satisfaction during off-seasons and peak seasons in the hotel industry while offering diversified schemes to various types of hotels.
This article takes social network services as the research object and mainly conducts two aspects of research: first, based on previous research, combining the behavior characteristics of users in the social network environment related to digital information privacy, the digital information privacy of users pays attention to the influential factors of users’ digital information privacy information disclosure behavior, summarize and refine them, establish theoretical research models, and then use structural equation modelling to empirically analyze the significance of each influence path; then, based on the first part of the empirical research, the evolutionary game theory was used to analyze the interests of digital information privacy between social network users and service providers. This paper also studies the evolution of the willingness of each parameter to provide digital information to users under variable expected return conditions and uses MATLAB to analyze its evolutionary trends. It is found that the regulatory intensity coefficient, information leakage loss, and information sensitivity are both for users and websites. It has an important impact, and the loss of information leakage and information sensitivity can affect the evolution direction of users’ willingness to provide digital information and change the speed of website evolution, and the regulatory intensity coefficient is the opposite.
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