Abstract
Product portfolio optimization is a typical multiobjective problem. The multichoice goal programming method becomes a popular means of resolving multiobjective decision problems. However, the classic multichoice goal programming method treats the product portfolio optimization in isolation and does not consider the mutual influence between portfolio products. Researchers should consider the interaction between products in portfolio optimization so that they can be adjusted to “real world” problems. The interaction between products can be explained by population dynamics. Logistic model is a classical method to analyze the population interaction. The equilibrium point of logistic model can show the ideal state of product population coordinated development. The combination of logistic and multichoice goal programming method is an effective approach to analyze the interaction of product portfolio. This paper therefore proposes a new alternative method to formulate the multiobjective problem and also uses an illustrative example to demonstrate the usefulness of the proposed method. The comparative analysis of model optimization results shows that logistic multichoice goal programming model can take into account resource constraints, product collaboration, and output maximization. Logistic multichoice goal programming model shows good performance in the aspects of operation complexity, operation time, sensitivity analysis, and collaborative entropy evaluation.
Highlights
It is established that decision-making affects organizational performance
E highlights of this article are as follows: (1) from the perspective of population dynamics, the problem of product portfolio optimization is analyzed, which expands the application scenarios of the logistic model. (2) e equilibrium point characteristics of population growth analysis are extended to the multichoice goal programming model, which expands the analysis function of the multichoice goal programming model
By drawing on the eory of Population Dynamics, this paper considers a company’s product portfolio as a product population and explores how the product population’s structure and scale can be determined under conditions of population synergy
Summary
It is established that decision-making affects organizational performance. Companies can achieve greater profits by increasing revenue or reducing costs. E direction of this field’s development can be determined by referring to the results of the analysis Companies can use this method to study industrial policies, and this can in turn help them to create appropriate policies that promote the development of PPs. e research organization of this article is as follows: this paper (1) reviewed the progress of related research through literature review, (2) used population dynamics to analyze the influence mechanism between interactive product populations, and (3) built multichoice goal programming and logistic multichoice goal programming models for product portfolio optimization and comparative analysis. E highlights of this article are as follows: (1) from the perspective of population dynamics, the problem of product portfolio optimization is analyzed, which expands the application scenarios of the logistic model. E highlights of this article are as follows: (1) from the perspective of population dynamics, the problem of product portfolio optimization is analyzed, which expands the application scenarios of the logistic model. (2) e equilibrium point characteristics of population growth analysis are extended to the multichoice goal programming model, which expands the analysis function of the multichoice goal programming model
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