scholarly journals Evaluation of regional industrial cluster innovation capability based on particle swarm clustering algorithm and multi-objective optimization

2021 ◽  
Author(s):  
Yongcai Yan ◽  
Mengxue He ◽  
Lifang Song
Keyword(s):  
Clustering Algorithm ◽  
Particle Swarm ◽  
Industrial Cluster ◽  
Industrial Clusters ◽  
Evaluation Index System ◽  
Industrial System ◽  
Innovation Capability ◽  
Multi Objective Optimization ◽  
Regional Cluster ◽  
Innovation Ability

AbstractWith the progress of the times and the development of science, industrial clusters have been regarded by all countries in the world as one of the important ways to enhance regional competitiveness, and become an inevitable trend of industrial development. The research on the innovation ability of industrial clusters can not only maintain sustainable development of industrial clusters and obtain sustained competitive advantages, but also provide reference for the government's policy formulation of industrial clusters. This paper aims to study the evaluation of regional industrial clusters' innovation capability based on particle swarm clustering and multi-objective optimization. This paper uses the theory of industrial cluster innovation and takes regional industrial system as the empirical research object to establish a regional industrial system capability evaluation system, which is based on the selection of indicators, combined with analytic hierarchy process and factor analysis to evaluate industrial innovation capability. On this basis, the particle swarm clustering theory is used to verify the innovation ability and evaluation index system of industrial clusters, and provide a reference for the evaluation of the innovation ability of industrial clusters. This paper divides the regional cluster innovation capability into four aspects: innovation input capability, environment support capability, self-development capability and innovation output capability, and systematically analyzes the key elements and in the composition of innovation elements and their relationships. It then constructs the evaluation index system of regional cluster innovation capability. At the same time, this paper introduces clustering analysis algorithm and swarm intelligence algorithm into regional innovation evaluation, combines particle swarm optimization algorithm and K-means clustering algorithm, and optimizes particle swarm clustering algorithm by adjusting adaptive parameters and adding fitness variance. The experimental results of this paper show that from the results of the tested innovation potential of the three industrial clusters, industrial cluster F has the strongest innovation ability, with an evaluation coefficient of 0.851, followed by industrial cluster F, which has a value of 0.623. This result is consistent with the actual innovation status of the selected industry. From this point of view, the established particle swarm clustering model for evaluating the innovation capability of regional industrial clusters is reliable and can be used to evaluate the innovation capability of different industrial clusters.

OPPORTUNITIES FOR THE FORMATION OF INTEGRATION SYSTEMS IN THE AGRO-INDUSTRIAL COMPLEX

2021 ◽  
Vol 2 (5) ◽  
pp. 47-54
Author(s):  
A. L. ABLYAMITOVA ◽  
Keyword(s):  
Structural Model ◽  
Industrial Cluster ◽  
Industrial Clusters ◽  
Industrial Complex ◽  
Specific Product ◽  
Regional Cluster ◽  
Proposed Model ◽  
Business Entities ◽  
The Village ◽  
Theoretical Foundations

The paper substantiates the theoretical foundations of the formation of agricultural territorial-industrial cluster associations, the mechanism of their effective functioning and ensuring the competitiveness of integrated business entities. The structural model of the regional association of agro-industrial clusters and cooperatives is justified. The proposed model of regional cluster integration includes industry-specific product clusters. It is proposed to create an agricultural service cooperative at the village level, and a multifunctional service cooperative at the district level.

A research on the cluster competitiveness evaluation of the Chinese automobile industry based on cuckoo-AHP

2016 ◽  
Vol 10 (4) ◽  
pp. 746-769 ◽  
Author(s):  
Xiujie Wang ◽  
Jian Liu ◽  
Can Ma
Keyword(s):  
Automobile Industry ◽  
Evaluation Method ◽  
Industrial Cluster ◽  
Cuckoo Search ◽  
Evaluation Methods ◽  
Industrial Clusters ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Content Type ◽  
Cluster Development

Purpose The purpose of this study is that on the basis of the competitive edge theory, source mechanism and evaluation approaches of industrial cluster competitiveness, combined with international trends in the automobile industry and the features of Chinese automobile industrial cluster development, an evaluation index system about cluster competitiveness of auto industry is built with comprehensive consideration of factors such as cluster development environment, external scale effect and internal competitiveness from the perspective of value chain of automobile industry. Design/methodology/approach An evaluation index system for automobile industrial cluster competitiveness was realized by integrating current strengths and future growth capacities with multidimensional, dynamic and comprehensive characteristics, which included 3 second-level, 10 third-level and 16 fourth-level indices. In the light of evaluation methods, a group intelligence optimization algorithm – (cuckoo search) – and traditional methods of complex decision-making system – analytic hierarchy process (AHP) – were combined to propose the cuckoo-AHP evaluation method. It was applied for the calculation and optimization of weight values in an automobile industrial cluster competitiveness evaluation index for the purpose of obtaining better scientific and more reliable results. Findings The research might further enrich the evaluation theory of automobile industrial cluster competitiveness and also can be useful for showing how traditional evaluation methods can be combined with intelligent algorithms to carry out better automobile industrial cluster competitiveness evaluations. In addition, studies of channels for kick-starting Chinese auto industrial cluster competitiveness are expected to provide references for how to enhance the cluster competitiveness of the Chinese automobile industry. Practical implications Changsha and Liuzhou, the Guangxi automobile industrial clusters as the two empirical analysis objects selected for this paper, are geographically adjacent to each other. The automobile industries of the two cities are local pillar industries with the strong support of the local government. Both clusters have their own advantages and weak points with different characteristics of cluster development, and they enjoy a representative significance amongst China’s numerous auto industrial clusters that are taking shape. Comparative analysis of both clusters serves as a good reference for the objective evaluation of the competitiveness of Chinese automobile clusters in terms of their real and practical developments and in respect of the success of reasonable scientific and industrial cluster policies. Originality/value Multidimensional, dynamic, integrated evaluation index systems are constructed around automobile industrial cluster competitiveness, which has taken into account developments in current strengths and future growth capacity. The cuckoo-AHP evaluation method has been formed by combining the traditional decision-making method known as AHP with a new meta-heuristic optimization algorithm called “cuckoo search”. Both have been used in evaluations of automobile industrial cluster competitiveness in Liuzhou and Changsha, which will be beneficial for enriching automobile industrial cluster competitiveness evaluation theory and new evaluation methods that will enable better evaluations of automobile industrial cluster competitiveness.

scholarly journals Evaluation of mineral resources carrying capacity based on the particle swarm optimization clustering algorithm

2020 ◽  
Vol 120 (12) ◽  
Author(s):  
S. He ◽  
D. Luo ◽  
K. Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Carrying Capacity ◽  
Clustering Algorithm ◽  
Evaluation Method ◽  
Particle Swarm ◽  
Mineral Resources ◽  
Renewable Resource ◽  
Evaluation Index System ◽  
Swarm Optimization ◽  
Resource Endowment

SYNOPSIS As minerals are a non-renewable resource, sustainability must be considered in their development and utilization. Evaluation of the mineral resources carrying capacity is necessary for the sustainable development of mineral resource-based regions. Following the construction of a comprehensive evaluation index system from four aspects, namely resource endowment, socio-economic status, environmental pollution, and ecological restoration, a method combining particle swarm optimization (PSO) and the K-means algorithm (PSO-Kmeans) was used to evaluate the mineral resources carrying capacity of the Panxi region southwest Sichuan Province, China. The evaluation method is data-driven and does not consider the classification standards of different carrying capacity levels. At the same time, it avoids the problems of local optimization and sensitivity to initial points of the K-means algorithm, thereby providing more objective evaluation results and solving the problem of subjective division of each grade volume capacity in carrying capacity evaluation. The algorithm was verified through UCI data-sets and virtual samples. By superimposing a single index on the carrying capacity map for analysis, the rationality of the evaluation results was validated. Keywords: particle swarm optimization, K-means algorithm , mineral resources, carrying capacity, sustainability.

scholarly journals Evaluation of Innovation Ability of Industrial Clusters Based on BP Neural Network

2021 ◽  
Vol 2074 (1) ◽  
pp. 012091
Author(s):  
Chunjie Fang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Index System ◽  
International Competitiveness ◽  
Industrial Clusters ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Innovation Ability

Abstract In order to improve the innovation ability of enterprises and enhance international competitiveness, it is necessary to correctly analyze and evaluate the innovation ability of industrial clusters. Therefore, BP neural network is used to explain the innovation ability of industrial clusters, and the evaluation index system is established. By investigating industrial clusters and using it to provide references for the evaluation of industrial clusters’ innovation capabilities.

Evaluating the innovation capability of cluster-based firms: a graph-theoretic approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Cláudia Fabiana Gohr ◽  
Maryana Scoralick de Almeida Tavares ◽  
Sandra Naomi Morioka
Keyword(s):  
Industrial Cluster ◽  
Industrial Clusters ◽  
Performance Measure ◽  
Northeastern Brazil ◽  
Theoretic Approach ◽  
Innovation Capability ◽  
Assessment Framework ◽  
Content Type ◽  
Graph Theoretic ◽  
Graph Theoretic Approach

Purpose This paper aims to propose an assessment framework to evaluate companies' innovation capability in the context of industrial clusters. Design/methodology/approach The assessment framework was built based on the Graph-Theoretic Approach (GTA) to measure the influence of the factors and sub-factors of innovation capabilities. To quantify the level of interdependence between factors and sub-factors of innovation capability Delphi method was adopted. The authors developed five case studies in firms from an Information and Communications Technology and Creative Economy cluster in Northeastern Brazil to test the framework's applicability. Findings The results showed that identifying and evaluating the factors of innovation capability allows a larger understanding of what affects these capabilities to a greater or lesser extent and contributes to strategic decision-making. Research limitations/implications The framework evaluates the innovation capability of each firm, not providing an index for the whole industrial cluster. Besides, the framework does not consider the innovations developed by the companies through the innovation's capabilities. As the Delphi technique was adopted to analyze the levels of influence or interdependence between factors and sub-factors of innovation capability, different experts may lead to different results. Practical implications Among the managerial implications, the authors can highlight the innovation capability index as a practical performance measure to stimulate improvement initiatives regarding innovations in industrial clusters. Besides, as the proposed framework is generic, research organizations, public institutions and regional governments can adopt it to analyze innovation capabilities in cluster-based companies. Originality/value Previous industrial cluster studies have concentrated on knowledge transfer as the main attribute influencing innovation capabilities. The literature also presents assessment frameworks focusing on qualitative analyses or innovation capabilities outcomes (patents and products). Differently, the authors proposed a quantitative assessment framework considering specific factors (and sub-factors) of innovation capabilities in industrial clusters.

scholarly journals Evaluation of Hebei Province’s Innovation Ability under the Integration of Beijing-Tianjin-Hebei

2021 ◽  
Vol 251 ◽  
pp. 01078
Author(s):  
He Menghuan
Keyword(s):  
Technological Innovation ◽  
Objective Evaluation ◽  
Hebei Province ◽  
Evaluation Index System ◽  
Innovation Capability ◽  
Regional Innovation ◽  
Innovation Ability ◽  
Innovation Output ◽  
The Government ◽  
Capability Evaluation

Regional innovation capability is one of the important indicators to measure the comprehensive development level of a region. This paper uses the analytic hierarchy process to evaluate the regional innovation capability of Beijing-Tianjin-Hebei. A total of 10 indicators were selected from the three aspects of regional innovation foundation, regional innovation input and regional innovation output to construct a regional innovation capability evaluation index system. Using 2019 data to comprehensively evaluate the innovation capability of the Beijing-Tianjin-Hebei region. Finally, the regional innovation ability scores and basic rankings of Beijing, Tianjin, and Hebei provinces were obtained. The study concluded that Hebei Province still has many gaps in innovation input and innovation output compared with Beijing and Tianjin. Therefore, Hebei Province needs to strengthen its innovation input and innovation output. The integration of Beijing-Tianjin-Hebei has created good development opportunities for Hebei Province. Therefore, an objective evaluation of the scientific and technological innovation capabilities of Hebei Province under the integration of Beijing-Tianjin-Hebei is of certain significance for the government to formulate science and technology strategies and improve technological innovation capabilities.

scholarly journals Does an Ecological Industry Chain Improve the Eco-Efficiency of an Industrial Cluster? Based on Empirical Study of an Energy-Intensive Industrial Cluster in China

2019 ◽  
Vol 11 (6) ◽  
pp. 1651 ◽  
Author(s):  
Jiliang Zheng ◽  
Xiaoting Peng
Keyword(s):  
Building Materials ◽  
Thermal Power ◽  
Industrial Cluster ◽  
Industrial Clusters ◽  
Two Dimensions ◽  
Evaluation Index System ◽  
Industry Chain ◽  
Ecological Industry ◽  
Metallurgy Industry ◽  
Energy Intensive Industries

An energy-intensive industrial cluster is a combination and integration of energy-intensive industries formed by ecological industry chains. Eco-efficiency may reflect the effect of ecological industry chains in an energy-intensive industrial cluster. To evaluate the eco-efficiency of energy-intensive industries, industry chains, and industrial clusters with different level of eco-industry chains, the eco-efficiency is decomposed into two dimensions of resource efficiency and environment efficiency. The eco-efficiency evaluation index system and models of energy-intensive industries are constructed to analyze the eco-efficiency using a two-dimensional three-layer matrix framework, including energy-intensive industries, ecological industry chains, and industrial clusters. This paper presents an empirical and comparative analysis based on data from the chemical industry, building materials industry, metallurgy industry, and thermal power industry from 2004 to 2015. The results show that the eco-efficiency of energy-intensive industry, energy-intensive industry chains, and energy-intensive industrial clusters are all on the rise. The eco-efficiency of energy-intensive industrial clusters and energy-intensive industry chains are obviously higher than that of any single energy-intensive industry. This finding indicates that the ecological industry chains of an energy-intensive industrial cluster have improved the eco-efficiency. In recent years, the effect of ecological industry chains and network construction has been significant, but not tight enough.

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