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Under electronic commerce circumstance, it is very urgent for modern enterprise to solve the problem how to meet customer's individual demand. The availability of data on consumer profiles has opened new possibilities for companies to increase their revenue via data mining techniques. We study algorithms for the product-set pricing problem, where, given consumer preferences among products and the costs of production, the goal is to set prices of product-set, so as to maximize the overall revenue of the company. A price sequence matrix method is proposed to solve product-set pricing model, and an application to case study shows that the give method is effective.

We consider a class of models of consumer purchasing behavior, each of which relates observed data on a consumer's requirements and budget constraint to subsequent purchasing tendencies. The available data on consumer preferences together with sophisticated analytical tools enables companies' increases in profit through optimization of prices. We can study for the multi-product pricing problem based on this available data. Based on the description, three multi-product pricing modes include min-buying mode, max-buying mode, and mixed-buying mode, were presented. Proposed models were solved through genetic algorithms. Optimal solution of the given examples shows that this model is effective for enterprises.

Enterprises need to develop a process to determine how to find and develop new product ideas and finally, how to successfully introduce them to the marketplace. To address this problem, conceptions of Optimal Introduction Period and Correlative Profit were presented. Based on the quantitative description of the product life cycle, a Non-Linear Semi-Infinite Programming model of new product introduction was proposed. The proposed model was solved by improved Particle Swarm Optimization (PSO) algorithms. Optimal solution of the given example shows that Particle Swarm Optimization has become the hotspot of evolutionary computation because of its excellent performance and simplicity for implement in solving combined optimization problems.

Through interaction with online consumers, e-commerce Web sites can gather data reflecting consumer preferences. The available data on consumer preferences together with sophisticated analytical tools enables companiespsila increases in profit through optimization of prices. We can study for the multi-product pricing problem based on this available data. Where, given consumer preferences among products, their budgets, and the order list of production, the goal is to set prices of multiple products from a single company, so as to maximize the overall revenue of the company. To address this problem, based on the customer-buying-behavior, a non-linear programming model of multi-product pricing was presented. The model was solved through genetic algorithms. Optimal solution of the given example shows that this model is effective for enterprises.