An improved fusion crossover genetic algorithm for a timeweighted maximal covering location problem for sensor siting under satelliteborne monitoring Abstract   Traditional location problems usually focus on spatial and temporal impacts of facilities, but few studies have focused on sensor siting under satelliteborne monitoring in a spaceground integrated sensor network. Given the partial coverage and the requirement for continuous coverage in space and time, a timeweighted maximal covering location problem with partial coverage (TMCLPPC) model is introduced. This model is solved using a modified genetic algorithm (GA)based approach that utilizes the spatiotemporal characteristics of potential facility sites for faster convergence. The performance of the new GA is tested on a precipitation station siting problem in the Jinsha River Basin on the upper reaches of the Yangtze River in China. The results demonstrate that the proposed GA can significantly reduce the computation time compared with CPLEX, a commercial integer programming solver, and can outperform the greedy algorithm and the GAs with onepoint, twopoint, fusion, and uniform crossover operators. The applicability of the proposed method and exploration of the design in the new GA are also discussed.

Hybrid Covering Location Problem: Set Covering and Modular Maximal Covering Location Problem To benefit from the location advantages provided from two main covering location problems, namely set covering location problem and maximal covering location problem, a new mathematical model is presented in this study. In this model, the main facilities are located gradually through the planning periods, providing full coverage for the incremental demand of nodes. In addition, a limited number of modules are assigned to the located facilities to maximize the covered demand nodes in operational periods. Combining the set covering and maximal covering location problems in one model, to take advantage of the merits of both models is studied. Some test problems are solved to investigate the proposed model. The numerical results confirm the applicability of the model and provide insights for the proposed model.