Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection

Kuo, R. J.; Shen, Hong; Yu, Hui

doi:10.1016/j.apm.2010.03.033

Cited by 115 publications

(48 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First proposed by Kennedy and Eberhart (1995), PSO is initialized with a population of random solutions, which it then searches for optima by updating generations. Then, unlike genetic algorithm, which is based on the survival of fitness, the potential solutions will move through the problem space by following the current optimum particles (Kuo et al 2010b). In more detail, each particle's movement is guided toward its local best known position.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…PSO can search a very large space of possible solutions, which makes it very suitable for criteria construction problems in GSCs. In other words, compared to other multi-objective optimization algorithms, such as genetic algorithm, PSO algorithm is a helpful metaheuristic approach which can clearly obtain acceptable solutions (Kuo et al 2010b). On the other hand, the PSO technique is flexible enough to solve the multiple-objective optimization problem, which makes it very suitable for decision-making in partner selection criteria construction.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Partner selection in green supply chains using PSO – a practical approach

Barnes

2016

Production Planning & Control

View full text Add to dashboard Cite

Partner selection is crucial to green supply chain management as the focal firm is responsible for the environmental performance of the whole supply chain. The construction of appropriate selection criteria is an essential, but often neglected prerequisite in the partner selection process. This paper proposes a three-stage model that combines Dempster-Shafer belief acceptability theory and particle swarm optimization technique for the first time in this application. This enables optimization of both effectiveness, in its consideration of the inter-dependence of a broad range of quantitative and qualitative selection criteria, and efficiency in its use of scarce resources during the criteria construction process to be achieved simultaneously. This also enables both operational and strategic attributes can be selected at different levels of hierarchy criteria in different decision-making environments. The practical efficacy of the model is demonstrated by an application in Company ABC, a large Chinese electronic equipment and instrument manufacturer.

show abstract

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Partner selection in green supply chains using PSO – a practical approach

Barnes

2016

Production Planning & Control

View full text Add to dashboard Cite

show abstract

“…On the AI, Tsai et al [13] developed an approach based on the attribute-based ant colony system (AACS) to construct a platform to examine the critical factors for decision-making in a dynamic business environment in order to select the appropriate suppliers. Kuo et al [14] developed an intelligent supplier decision support system which was able to consider both the quantitative and qualitative factors. It was composed of (1) the collection of quantitative data such as profit and productivity, (2) a particle swarm optimization-(PSO-) based fuzzy neural network (FNN) to derive the rules for qualitative data, and (3) a decision integration model for integrating both the quantitative data and fuzzy knowledge decision to achieve the optimal decision.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Multilevel Supplier Selection Problem Based on Weighted-Directed Network and Its Solution

Wei

Zuo

Jiang

et al. 2017

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

With the rapid development of economy, the supplier network is becoming more and more complicated. It is important to choose the right suppliers for improving the efficiency of the supply chain, so how to choose the right ones is one of the important research directions of supply chain management. This paper studies the partner selection problem from the perspective of supplier network global optimization. Firstly, this paper discusses and forms the evaluation system to estimate the supplier from the two indicators of risk and greenness and then applies the value as the weight of the network between two nodes to build a weighted-directed supplier network; secondly, the study establishes the optimal combination model of supplier selection based on the global network perspective and solves the model by the dynamic programming-tabu search algorithm and the improved ant colony algorithm, respectively; finally, different scale simulation examples are given to testify the efficiency of the two algorithms. The results show that the ant colony algorithm is superior to the tabu search one as a whole, but the latter is slightly better than the former when network scale is small.

show abstract

“…The result showed that it can provide better performance compared to the conventional BP neural network. Kuo et al [25] developed an intelligent supplier decision support system which employed a PSO-based fuzzy neural network to derive the rules for qualitative data and build an integration model by taking into account both qualitative and quantitative factors. Gong et al [14] developed a novel PSO algorithm with the tentative reader elimination (TRE) operator to deal with the RFID network planning (RNP) problem.…”

Section: Applications Of Soft Computing Techniques To Indoor Positionmentioning

confidence: 99%

An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

Wang

Shi

2017

Information

View full text Add to dashboard Cite

Location-based services (LBS) have long been recognized as a significant component of the emerging information services. However, the localization cost and the performance of algorithm still need to be optimized. In the study, an improved particle swarm optimization algorithm based on a feed-forward neural network (IMPSO-FNN) combined with RFID sensors is proposed, which can achieve the best indoor positioning location and overcome the problems effectively. In IMPSO-FNN, an improved PSO algorithm (IMPSO) is developed to determine the optimal connecting weights and markedly optimize the network parameters and structural parameters for the FNN, and then an optimal location prediction model is established by the IMPSO-FNN. To avoid the interference of environmental noise for the experimental data, some preprocessing methods are used during the positioning process. The computational results for learning two continuous functions show that the proposed positioning algorithm has a faster convergence rate and higher generalization performance. The model evaluation results also verify that the proposed positioning method really is superior to other algorithms in terms of the learning ability, efficiency, and positioning accuracy.

show abstract

Integration of particle swarm optimization-based fuzzy neural network and artificial neural network for supplier selection

Cited by 115 publications

References 52 publications

Partner selection in green supply chains using PSO – a practical approach

Partner selection in green supply chains using PSO – a practical approach

Modeling Multilevel Supplier Selection Problem Based on Weighted-Directed Network and Its Solution

An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

Contact Info

Product

Resources

About