Development of Hybrid Genetic Algorithms for Product Line Designs

Abstract

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 fairy 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.

Income distribution, quality differentiation and product line design

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Optimizing differentiation and commonality levels among models in car line-ups: An empirical application of a nature-inspired heuristic mechanism

“…Evidently, the literature on product (line) design has presented various heuristic procedures over the last decades to address the optimization problem, such as Dynamic Programming (Kohli & Sukumar, 1990), Beam Search (Nair, Thakur, & Wen, 1995), Lagrangian Relaxation with Branch and Bound (Belloni, Freund, Selove, & Simester, 2008;), Genetic Algorithms (Balakrishnan, Gupta, & Jacob, 2004), and Ant Colony Optimization (Albritton & McMullen, 2007). Although PSO has been extensively implemented in various research fields since its original introduction by Kennedy and Eberhart in 1995, the algorithm has just recently been implemented to the optimal design problem.…”