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A nonlinear bi-level programming approach for product portfolio management.

Shuang Ma1

  • 1School of Management and Economics, Beijing Institute of Technology, Beijing, 100081 China.

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|July 5, 2016
PubMed
Summary

This study introduces a novel bi-level optimization model for product portfolio management (PPM) that accounts for competitor actions. The developed model effectively optimizes product portfolios in competitive markets.

Keywords:
Bi-level nested genetic algorithmLeader–follower joint optimizationNonlinear bi-level programmingProduct portfolio management

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Area of Science:

  • Operations Research
  • Business Strategy

Background:

  • Traditional product portfolio management (PPM) often overlooks competitor actions and competitive dynamics.
  • Mathematical optimization in PPM has historically focused on engineering and marketing feasibility, neglecting competitive relations.

Purpose of the Study:

  • To develop a mathematical optimization model for leader-follower product portfolio management (PPM) that incorporates competitive relations.
  • To address the challenge of modeling Stackelberg game-based PPM problems with multiple interacting competitors.

Main Methods:

  • A decision framework and a nonlinear, integer bi-level programming model were formulated for leader-follower PPM.
  • A bi-level nested genetic algorithm was employed to solve the developed nonlinear bi-level programming model.

Main Results:

  • The study successfully applied the leader-follower bi-level optimization model to a notebook computer product portfolio.
  • Results demonstrated the robustness of the bi-level optimization model in enhancing product portfolio optimization.

Conclusions:

  • The proposed leader-follower bi-level optimization model provides a robust framework for product portfolio management in competitive environments.
  • This approach empowers companies to make more informed decisions by considering competitor strategies within their portfolio optimization efforts.