scholarly journals Multiobjective Evaluation of Coevolution among Innovation Populations Based on Lotka–Volterra Equilibrium

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Sheng-Yuan Wang ◽  
Wan-Ming Chen ◽  
Rong Wang ◽  
Xiao-Lan Wu
Keyword(s):  
Goal Programming ◽  
Evaluation Method ◽  
Optimization Problems ◽  
Programming Model ◽  
Interaction Mechanism ◽  
Volterra Model ◽  
Application Process ◽  
Goal Programming Model ◽  
Enterprise Innovation ◽  
Collaborative Evaluation

The collaborative evaluation of enterprise innovation populations is a hot issue. The Lotka–Volterra model is a mature method used to evaluate the interaction mechanism of populations and is widely used in innovation ecology research studies. The Lotka–Volterra model mainly focuses on the quantitative characteristics of the interactive populations. The growth mechanisms cannot explain all the synergy mechanisms of the innovative populations. The collaborative evaluation between enterprise innovation populations is a typical multiobjective evaluation problem. The multichoice goal programming model is a mature method to solve multiobjective optimization problems. This paper combines the Lotka–Volterra model and multichoice goal programming method to construct a three-stage multiobjective collaboration evaluation method based on Lotka–Volterra equilibrium. An evaluation example is used to illustrate the application process of this method. The method proposed in this paper has excellent performance in computing, parameter sensitivity analysis, and model stability analysis.

scholarly journals Hybrid Uncertainty-Goal Programming Model with Scaled Index for Production Planning Assessment

FinTech ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 1-24
Author(s):  
Junzo Watada ◽  
Nureize Binti Arbaiy ◽  
Qiuhong Chen
Keyword(s):  
Production Planning ◽  
Goal Programming ◽  
Optimization Problems ◽  
Programming Model ◽  
Business Goals ◽  
Target Values ◽  
Goal Programming Model ◽  
Quantitative Results ◽  
Hybrid Uncertainty ◽  
Fuzzy Random

Goal programming (GP) can be thought of as an extension or generalization of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved. Unwanted deviations from this set of target values are then minimized in an achievement function. Production planning is an important process that aims to leverage the resources available in industry to achieve one or more business goals. However, the production planning that typically uses mathematical models has its own challenges where parameter models are sometimes difficult to find easily and accurately. Data collected with various data collection methods and human experts’ judgments are often prone to uncertainties that can affect the information presented by quantitative results. This study focuses on resolving data uncertainties as well as multi-objective optimization using fuzzy random methods and GP in production planning problems. GP was enhanced with fuzzy random features. Scalable approaches and maximum minimum operators were then used to solve multi-object optimization problems. Scaled indices were also introduced to resolve fuzzy symbols containing unspecified relationships. The application results indicate that the proposed approach can mitigate the characteristics of uncertainty in the analysis and achieve a satisfactory optimized solution.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Su-Lan Zhai ◽  
Ying Liu ◽  
Sheng-Yuan Wang ◽  
Xiao-Lan Wu
Keyword(s):  
Goal Programming ◽  
Resource Constraints ◽  
Programming Model ◽  
Resource Constraint ◽  
Innovation Ecosystem ◽  
Goal Programming Model ◽  
Enterprise Innovation ◽  
Appropriate Scale ◽  
Scale Optimization ◽  
Constraint Model

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.

scholarly journals A Multicriteria Goal Programming Model for Ranking Universities

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 459
Author(s):  
Fernando García ◽  
Francisco Guijarro ◽  
Javier Oliver
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Decision Makers ◽  
University Rankings ◽  
Central Tendency ◽  
Performance Variables ◽  
World University Rankings ◽  
Goal Programming Model ◽  
Objective Quantification

This paper proposes the use of a goal programming model for the objective ranking of universities. This methodology has been successfully used in other areas to analyze the performance of firms by focusing on two opposite approaches: (a) one favouring those performance variables that are aligned with the central tendency of the majority of the variables used in the measurement of the performance, and (b) an alternative one that favours those different, singular, or independent performance variables. Our results are compared with the ranking proposed by two popular World University Rankings, and some insightful differences are outlined. We show how some top-performing universities occupy the best positions regardless of the approach followed by the goal programming model, hence confirming their leadership. In addition, our proposal allows for an objective quantification of the importance of each variable in the performance of universities, which could be of great interest to decision-makers.

Allocation of teaching personnel: A goal programming model

1983 ◽  
Vol 17 (4) ◽  
pp. 211-216 ◽  
Author(s):  
Sheila M. Lawrence ◽  
Kenneth D. Lawrence ◽  
Gary R. Reeves
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Goal Programming Model ◽  
Teaching Personnel

scholarly journals Addressing uncertainty in sugarcane harvest planning through a revised multi-choice goal programming model

2015 ◽  
Vol 39 (18) ◽  
pp. 5540-5558 ◽  
Author(s):  
Aneirson Francisco da Silva ◽  
Fernando Augusto Silva Marins ◽  
Erica Ximenes Dias
Keyword(s):  
Goal Programming ◽  
Programming Model ◽  
Goal Programming Model
Sign in / Sign up

Export Citation Format

Share Document