Affiliations: [a]
Business School, University of International Business and Economics, Beijing, China
| [b]
Department of Economic Management, Institute of Disater Prevention, Sanhe, Hebei, China
Correspondence:
[*]
Corresponding author. Weiling Chen, Business School, University of International Business and Economics, Beijing, 100029, China. E-mail: [email protected].
Abstract: With the development of Internet and information technology, enterprises are facing more challenges. It is urgent and necessary to innovate the mode of enterprise management. In this paper, from the perspective of contingency, a multi-objective genetic algorithm is constructed to introduce Internet of Things technology into enterprise management innovation. Through the function to solve the relationship between the various operating entities of the enterprise, the algorithm obtains the optimal solution of vehicle distribution. Firstly, contingency theory is introduced into the innovative design of enterprise information system to optimize the logistics distribution path under the environment of the Internet of Things (IoT). The multi-objective genetic algorithm steps are designed, and the non-dominant set is constructed. The crossover and mutation operations of the objectives are combined to get the genetic sub-classes, and then the relationships among the parties in the enterprise logistics innovation management activities are solved. The experimental results show that the shortest running time of the algorithm is 0.56 seconds and the longest running time is 2.48 seconds. The average running time in the whole process is not more than 1 second, which meets the actual needs. The genetic algorithm can help enterprises to arrange the distribution path of logistics fleet reasonably. The research in this paper has enlightening effect on the management innovation of enterprises under the information environment, and further expands the application field of the IoT, which has practical significance.
Keywords: Contingency perspective, IoT, business management, innovations
