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In the 5G era, the communication networks tend to be ultra-densified, which will improve the accuracy of indoor positioning and further improve the quality of positioning service. In this study, we propose an indoor three-dimensional (3D) dynamic reconstruction fingerprint matching algorithm (DSR-FP) in a 5G ultra-dense network. The first step of the algorithm is to construct a local fingerprint matrix having low-rank characteristics using partial fingerprint data, and then reconstruct the local matrix as a complete fingerprint library using the FPCA reconstruction algorithm. In the second step of the algorithm, a dynamic base station matching strategy is used to screen out the best quality service base stations and multiple sub-optimal service base stations. Then, the fingerprints of the other base station numbers are eliminated from the fingerprint database to simplify the fingerprint database. Finally, the 3D estimated coordinates of the point to be located are obtained through the K-nearest neighbor matching algorithm. The analysis of the simulation results demonstrates that the average relative error between the reconstructed fingerprint database by the DSR-FP algorithm and the original fingerprint database is 1.21%, indicating that the accuracy of the reconstruction fingerprint database is high, and the influence of the location error can be ignored. The positioning error of the DSR-FP algorithm is less than 0.31 m. Furthermore, at the same signal-to-noise ratio, the positioning error of the DSR-FP algorithm is lesser than that of the traditional fingerprint matching algorithm, while its positioning accuracy is higher.

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Job-shop scheduling is one of the most important problems in workshop scheduling and is significant for improving production efficiency. This paper proposes a hybrid fruit fly algorithm based on bi-objective job-shop scheduling. Based on the existing fruit fly optimization algorithm, the paper presents a hybrid step-size olfactory search method to improve search efficiency. It uses the global collaboration mechanism to increase diversity and cooperation of the fruit fly population, to avoid falling into the local optimum and escape premature convergence, and to make the algorithm have more opportunities to jump away from the local extrema. This paper proposes the external essence of a library evolution strategy based on the crowding-distance approach to objectively determine the multi-objective fitness value, and strategically guide the hybrid fruit fly algorithm to evolve to the Pareto fronts. The simulation results show that the algorithm is simple and has strong global optimization ability for effectively solving the bi-objective job-shop scheduling problem.