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Solving the supermarket shopping route planning problem based on genetic algorithm

机译:基于遗传算法求解超市购物路线规划问题

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Nowadays, the supermarket scale and items increase gradually. Customers will take a long time if they want to buy all things they need. Sometimes customers don't know the location of the goods, or they have to walk a repeated route, which leading to a waste of time, so we need to find the best shopping route. In this paper, we use genetic algorithm to solve this problem for customers. Due to the particularity of this problem, the start and end points of route are fixed, so we need to do some change for operator of GA. This algorithm can calculate a shortest route that isn't repeated and takes the shortest time to help customers shopping quickly. In the process of experiment, we apply it into a shopping route problem with twenty commodities. The results show that this algorithm can find the best solution after certain number of iterations.
机译:如今,超市的规模和商品逐渐增加。如果客户想购买所需的所有物品,将花费很长时间。有时,客户不知道货物的位置,或者不得不走一条重复的路线,这会浪费时间,因此我们需要找到最佳的购物路线。在本文中,我们使用遗传算法为客户解决此问题。由于此问题的特殊性,路由的起点和终点是固定的,因此我们需要对GA的运算符进行一些更改。该算法可以计算出一条最短的路线,该路线不会重复,并且需要最短的时间来帮助客户快速购物。在实验过程中,我们将其应用于具有二十种商品的购物路线问题。结果表明,该算法在经过一定数量的迭代后可以找到最佳的解决方案。

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