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Purchasing decision-making is an important part of logistic management for process plants. The paper considers a purchasing activity process of multiple suppliers and multiple species based on activity cost analysis. A purchasing optimization model with composition constraints is set up with minimum order quantity. The paper proposes an improved genetic algorithm to decide the optimal order quantities of each supplier based on methods of Gauss Mapping, improved adaptive penalty function and chaotic migration. A computation example is given to show the feasibility and effectiveness of the decision-making model and optimization strategy for raw material purchasing problem.