Supply chain management and metaheuristic algorithms: Analysing a new hybrid genetic crossover operator

The increase in the international consumer market in recent years allowed the opening of new stores, increasing price competition and diversifying the product offering. In this context, could be profitable knowing the best combination of price, quality and services, considering the cost of transport between stores. Such a problem assumes a high complexity when more products and stores were involved, and it is a classical problem in supply chain network design - one of the most important issues in modern business management. The worldwide industry has been demanded innovative solutions from its information technology staff, and this is why it is worth-while exploring new directions for finding efficient solutions. In this paper, we analyse the use of metaheuristics algorithms combined to a new genetic operator, to optimize purchases of products in stores geographically separated, searching for a purchase with the lowest possible total cost with a minimum route. Overall, the results evidence metaheuristics algorithms equipped with new genetic operator can be useful for achieving relatively good solutions in a short-time interval when compared with traditional operators.