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- W3203260080 abstract "With the goal of providing food to people in need, the FBST serves nearly 4,000 square miles in six counties in New York State. The MFP program is among the main activities of FBST, using an MFP truck loaded with food to serve 70 sites in New York State. An effective and fair schedule for visiting all 70 sites is necessary to optimize the MFP plan. In order to describe the problem, a triad {G, R, C} of goal, resource and constraint is established according to the decision theory. Based on the established triad, this paper establishes a multi-objective optimization model with the goal of improving validity and fairness, a genetic algorithm has been adopted to solve the problem. The time schedule for visiting sites is finally obtained after the iteration of 2000 times, which took 156.40 seconds. Considering that customers can go to further sites, the number of service sites and the location of sites can be optimized. The K-means clustering algorithm based on the improved demand centroid solution method is used to solve the site location selection. After optimizing the location of the site, the performance of seniorsite and regular-site systems is improved by 30.755% and 36.413% respectively." @default.
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- W3203260080 date "2021-09-01" @default.
- W3203260080 modified "2023-10-14" @default.
- W3203260080 title "Site Location Optimization Based on Genetic Algorithm" @default.
- W3203260080 cites W1977556410 @default.
- W3203260080 doi "https://doi.org/10.1088/1742-6596/2025/1/012056" @default.
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