Collaborative Product Portfolio Design Based on the Approach of Multichoice Goal Programming

Discrete Dynamics in Nature and Society

Wan-ming

2021

Self Cite

The classical Lotka–Volterra model is mainly a two-dimensional configuration. The two-dimensional form limits the application scenarios of the Lotka–Volterra model. The research objective of this paper is to develop a feasible three population equilibrium model to analyze the industry competition. In order to expand the Lotka–Volterra model to empirical analysis, this paper proposes a symbiosis model of enterprise population based on a three-dimensional Lotka–Volterra model. This paper analyzes the symbiotic system from two aspects: the balanced development of three species and the competitive evolution of the three species, taking the sales volume of three local automobile enterprises in China as the sample for empirical analysis. The symbiotic optimization under the equilibrium state of three populations indicates that there is the possibility of equilibrium and reciprocity in the growth of three automobile product populations. The empirical analysis fully explains the feasibility of this research paradigm. The analysis of competition evolution shows that cooperative action is better than competitive strategy and that comprehensive cooperation is the ideal state of a three-population symbiotic system.

Section: Ree-dimensional Lotka-volterra Modelmentioning

confidence: 99%

Competition Analysis on Industry Populations Based on a Three-Dimensional Lotka–Volterra Model

Discrete Dynamics in Nature and Society

Wan-ming

2021

Self Cite

“…g i, min and g i, max are the lower and upper limits of the target for g i , respectively. Considering the symbiotic relationship [29,30], new constraints need to be added to the above model:…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Application of Interaction Effect Multichoice Goal Programming in Project Portfolio Analysis

Zhai

Mathematical Problems in Engineering

et al. 2021

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How to choose the optimal project portfolio based on the enterprise objectives is very important for the survival and development of an enterprise. However, the current portfolio analysis needs to be improved in the aspects of interaction effect analysis, multichoice target planning, and method simplicity and practicability. The method of portfolio selection proposed in this study takes into consideration the complex interaction within the portfolio, which makes up for the deficiency of the existing research on portfolio selection. This paper illustrates the application of the multichoice object programming method in the analysis of the combination of two-project and three-project interaction effects by a series of examples. The results show that the method is applicable in portfolio evaluation. The method of multichoice goal programming of the interaction effect provides the basis for enterprise to measure the complex relationship within the portfolio and also provides a new method and idea for the selection of the enterprise project portfolio.

“…e equilibrium solution (EP) and sample data (P) are nonfull collaborative entropy. e nonfull synergy entropy and full synergy entropy are defined as [16]…”

Section: Synergy Evaluationmentioning

confidence: 99%

“…e research shows that the introduction of the population dynamics model into market competition and diffusion produces better analysis results [13,14]. e population dynamics model can be adopted to explain the dynamic competition relationship of the stock market [15], product portfolio optimization [16], the symbiotic relationship between competitors in the mobile communication market [17], and the dynamic competition and equilibrium point of TV product population [18]. Based on previous studies, this paper selects interdependent innovation groups as the research object and uses the population dynamics model to study the coordination and balance level between innovation groups in the innovation ecosystem.…”

Section: Introductionmentioning

confidence: 99%

Growth Scale Optimization of Discrete Innovation Population Systems with Multichoice Goal Programming

Zhai

Liu

Discrete Dynamics in Nature and Society

et al. 2021

Self Cite

How are limited resources efficiently allocated among different innovation populations? The performances of different innovation populations are quite different with either synergy or competition between them. If the innovation population is kept under an appropriate scale, full use can be made of the allocated resources. The maximization of the development and performance for a certain scale of innovation population is a typical multichoice development problem. Therefore, the scale optimization of the innovation population should be analyzed. According to the population dynamics, a resource constraint model for the growth of innovation population is developed, and the growth of innovation population under resource constraints is in equilibrium accordingly. With the help of a multichoice goal programming model, the scale optimization of innovation population performance can be obtained. The results of the resource constraint model and multichoice goal programming model are used to determine the optimal scale of the innovation population. From the panel data of the innovation population in Jiangsu Province from 2000 to 2017, we have found that R&D investment was the main innovation resource variable and that patent number was the main innovation output variable. Based on these data, the scale optimization of the innovation population under resource constraints can be calculated. The results of the study show that, in the observation period, the enterprise innovation population is often in the appropriate scale state. The scale development of enterprise innovation population is often more suitable for innovation ecosystem than that of scientific research institutions. According to these results, the government can provide appropriate guiding policies and incentives for different innovation populations. The innovative population can adjust its own development strategy and plan in time accordingly.