Dynamic capacitated maximal covering location problem by considering dynamic capacity

Jafar Bagherinejad, Mahnaz Shoeib
<span title="">2018</span> <i title="Growing Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r3eu5lbtdnecte3lctgdxnhplq" style="color: black;">International Journal of Industrial Engineering Computations</a> </i> &nbsp;
Capacitated maximal covering location problems (MCLP) have considered capacity constraint of facilities but these models have been studied in only one direction. In this paper, capacitated MCLP and dynamic MCLP are integrated with each other and dynamic capacity constraint is considered for facilities. Since MCLP is NP-hard and commercial software packages are unable to solve such problems in a rational time, Genetic algorithm (GA) and bee algorithm are proposed to solve this problem. In order
more &raquo; ... o achieve better performance, these algorithms are tuned by Taguchi method. Sample problems are generated randomly. Results show that GA provides better solutions than bee algorithm in a shorter amount of time. Dynamic models can be classified into two categories: explicitly dynamic models and implicitly dynamic models. In explicitly dynamic models, in order to respond to changes in parameters over time, facilities are closed and opened in pre-specified times and locations. In implicitly dynamic models, all facilities are to be open in the beginning of time horizon and remain open throughout the time horizon. These models are considered to be dynamic because they try to consider changes in parameters such as demand changes over time (Current et al., 1998) .
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