In order to meet the customers' requirements, firms need to design a product line rather than a single product because customers belong to different segments and each segment has its own specific product requirement which usually does not meet by a single product. The two major objectives of Product Line Design (PLD) are: (i) customers' satisfaction and (ii) the firm's profit maximization. Existing works consider customer preferences for a product selection based on an attraction choice model using a different combination of attribute levels and the firm's own pricing strategy. The existing models utilize Bratley-Terry-Luce (BTL), Multinomial Logit (MNL), Multiplicative Competitive Interaction (MCI) techniques for computing customer choice, which suffers the limitation imposed by Independence of Irrelevant Alternative property. However, these models do not consider the impact of customer leakage and impact of competitor pricing on product offering for the same customer segment. In this research work we model the PLD problem which considers both customer leakage and competitor pricing strategy. It also use Mixed Logit (MXL) to fix the limitation imposed by IIA property. The resulting model is NP-hard, which computationally intractable for realist problems using an exact solution approach. Thus, this research discussed the use of meta-heuristics to solve the problem. In addition, the utilization of Radial Basis function (RBS) in neural networks is demonstrated to estimate customer preferences and other model related parameters.


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