Multi-level genetic algorithm (MLGA) for the construction of clock binary tree
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Computers and Operations Research
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
ACM Transactions on Information Systems (TOIS)
Computers and Operations Research
Expert Systems with Applications: An International Journal
Optimal product positioning with consideration of negative utility effect on consumer choice rule
Decision Support Systems
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In this paper, we investigate the efficacy of artificial intelligence (AI) based meta-heuristic techniques namely genetic algorithms (GAs), for the product line design problem. This work extends previously developed methods for the single product design problem. We conduct a large scale simulation study to determine the effectiveness of such an AI based technique for providing good solutions and bench mark the performance of this against the current dominant approach of beam search (BS). We investigate the potential advantages of pursuing the avenue of developing hybrid models and then implement and study such hybrid models using two very distinct approaches: namely, seeding the initial GA population with the BS solution, and employing the BS solution as part of the GA operator's process. We go on to examine the impact of two alternate string representation formats on the quality of the solutions obtained by the above proposed techniques. We also explicitly investigate a critical managerial factor of attribute importance in terms of its impact on the solutions obtained by the alternate modeling procedures. The alternate techniques are then evaluated, using statistical analysis of variance, on a fairly large number of data sets, as to the quality of the solutions obtained with respect to the state-of-the-art benchmark and in terms of their ability to provide multiple, unique product line options.