Optimal product line design : Genetic algorithm approach to mitigate cannibalization

• Published in: Journal of optimization theory and applications Vol. 131; no. 2; pp. 227 - 244
• Main Authors: FRUCHTER, G. E, FLIGLER, A, WINER, R. S
• Format: Journal Article
• Language: English
• Published: New York, NY Springer 01-11-2006; Springer Nature B.V
• Subjects: Algorithms; Applied sciences; Chromosomes; Customer satisfaction; Customers; Design; Dynamic programming; Exact sciences and technology; Fitness; Genetic algorithms; Heuristic; Integer programming; Linear programming; Marketing; Mathematical analysis; Mathematical programming; Mechanical engineering. Machine design; Operational research and scientific management; Operational research. Management science; Operators; Optimization; Prices; Product design; Product lines; Segments; Studies; Willingness to pay; Local search; cannibalization; Biological evolution; Clientele; Satisfaction; Chromosome; Linear programming; Optimization; Search algorithm; Marketing; Optimal design; Genetic algorithms; Engineering design; Product structure; heuristics; product line design; Genetic algorithm; Preference; Pricing; Heuristic method; Profit; Product development; Safety; By product
• ISSN: 0022-3239; 1573-2878
• DOI: 10.1007/s10957-006-9135-3

In this marketing-oriented era where manufacturers maximize prots 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 prots by satisfying more customers than a single product would. When the amount of data on customer preferences or possible product congurations is large and no analytical relations can be established, the problem of an optimal product line design becomes very difcult and there are no traditional methods to solve it. In this paper, we show that the usage of genetic algorithms, a mathematical heuristics mimicking the process of biological evolution, can solve efciently the problem. Special domain operators were developed to help the genetic algorithm mitigate cannibalization and enhance the algorithms local search abilities. Using manufacturers profits as the criteria for tness in evaluating chromosomes, the usage of domain specic operators was found to be highly benecial with better nal results. Also, we have hybridized the genetic algorithm with a linear programming postprocessing step to ne tune the prices of products in the product line. Attacking the core difculty of cannibalization in the algorithm, the operators introduced in this work are unique. [PUBLICATION ABSTRACT]