Footnote:
In: Journal of Optimization Theory and Applications
Description:
In this marketing-oriented era where manufacturers maximize profits through customer satisfaction, there is an increasing need to design a product line rather than a single product. By offering a product line, the manufacturer can customize his or her products to the needs of a variety of segments in order to maximize profits by satisfying more customers than a single product would. When the amount of data on customer preferences or possible product configurations is large and no analytical relations can be established, the problem of an optimal product line design becomes very difficult and there are no traditional methods to solve it. In this paper, we show that the usage of Genetic Algorithms, a mathematical heuristic mimicking the process of biological evolution, can efficiently solve the problem. Special domain operators were developed to help the genetic algorithm mitigate cannibalization and enhance the algorithm's local search abilities. Using manufacturer's profits as the criteria for fitness in evaluating chromosomes, the usage of domain specific operators was found to be highly beneficial with better final results. We also have hybridized the genetic algorithm with a linear programming post-processing step to fine tune the prices of products in the product line. Attacking the core difficulty of canibalization in the algorithm, the operators introduced in this work are unique. Furthermore, applying our algorithm to a particular product line design problem, we find that the profile of the optimal single product is the core product of the optimal product line. The various brands in the product line are slight variations of the single product solution