The Goal Programming (GP) model is an important Multiple Objective Programming (MOP) technique that has been widely utilized for strategic decision making in presence of competing and conflicting objectives. The GP model aggregates multiple objectives and allows obtaining satisfying solutions where the deviations between achievement and the aspirations levels of the attributes are to be minimized. The GP model is easy to understand and to apply: it is based on mathematical programming techniques and can be easily solved using software packages such as LINGO, MATLAB, and AMPL. The GP describes the spectrum of the Decision Maker’s preferences through a user-friendly and learning decision-making process. This chapter aims to present the state-of-the-art of GP models and highlight its applications to strategic decision making in portfolio investments, marketing decisions and media campaign.

Goal Programming Models for Managerial Strategic Decision Making

Cinzia Colapinto
;
2020

Abstract

The Goal Programming (GP) model is an important Multiple Objective Programming (MOP) technique that has been widely utilized for strategic decision making in presence of competing and conflicting objectives. The GP model aggregates multiple objectives and allows obtaining satisfying solutions where the deviations between achievement and the aspirations levels of the attributes are to be minimized. The GP model is easy to understand and to apply: it is based on mathematical programming techniques and can be easily solved using software packages such as LINGO, MATLAB, and AMPL. The GP describes the spectrum of the Decision Maker’s preferences through a user-friendly and learning decision-making process. This chapter aims to present the state-of-the-art of GP models and highlight its applications to strategic decision making in portfolio investments, marketing decisions and media campaign.
Applied Mathematical Analysis: Theory, Methods, and Applications Editors:
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3711695
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