A heuristic genetic algorithm for product portfolio planning
Computers & Operations ResearchPublished 23 August 2005
Jianxin Jiao, Yiyang Zhang, Yi Wang
Citations80
SJR quartileQ1
SJR score1.60
SNIP2.02
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TL;DR
A heuristic genetic algorithm for solving the product portfolio planning problem more effectively is developed and an unbalanced index is proposed to model the elitism of product portfolio solutions.
Abstract
Product portfolio planning has been recognized as a critical decision facing all companies across industries. It aims at the selection of a near-optimal mix of products and attribute levels to offer in the target market. It constitutes a combinatorial optimization problem that is deemed to be NP-hard in nature. Conventional enumeration-based optimization techniques become inhibitive given that the number of possible combinations may be enormous. Genetic algorithms have been proven to excel in solving combinatorial optimization problems. This paper develops a heuristic genetic algorithm for solving the product portfolio planning problem more effectively. A generic encoding scheme is introduced to synchronize product portfolio generation and selection coherently. The fitness function is established based on a shared surplus measure leveraging both the customer and engineering concerns. An unbalanced index is proposed to model the elitism of product portfolio solutions.
Keywords
Business, Management and AccountingEngineering
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