A nonlinear bi-level programming approach for product portfolio management

Product portfolio management (PPM) is a critical decision-making for companies across various industries in today’s competitive environment. Traditional studies on PPM problem have been motivated toward engineering feasibilities and marketing which relatively pay less attention to other competitors’...

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Bibliographic Details
Published in:SpringerPlus Vol. 5; no. 1; p. 727
Main Author: Ma, Shuang
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 16.06.2016
Springer Nature B.V
Subjects:
Applied Mathematics
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
Nonlinear bi-level programming
Leader–follower joint optimization
Bi-level nested genetic algorithm
Product portfolio management
ISSN:2193-1801, 2193-1801
Online Access:Get full text
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Summary:Product portfolio management (PPM) is a critical decision-making for companies across various industries in today’s competitive environment. Traditional studies on PPM problem have been motivated toward engineering feasibilities and marketing which relatively pay less attention to other competitors’ actions and the competitive relations, especially in mathematical optimization domain. The key challenge lies in that how to construct a mathematical optimization model to describe this Stackelberg game-based leader–follower PPM problem and the competitive relations between them. The primary work of this paper is the representation of a decision framework and the optimization model to leverage the PPM problem of leader and follower. A nonlinear, integer bi-level programming model is developed based on the decision framework. Furthermore, a bi-level nested genetic algorithm is put forward to solve this nonlinear bi-level programming model for leader–follower PPM problem. A case study of notebook computer product portfolio optimization is reported. Results and analyses reveal that the leader–follower bi-level optimization model is robust and can empower product portfolio optimization.
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ISSN:2193-1801
2193-1801
DOI:10.1186/s40064-016-2421-0