Copyright @ 2001 Elsevier Science LtdA new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the feasible solution. Two heuristics are presented, which can be combined with the neural network. One heuristic is used to accelerate the solving process of the neural network and guarantee its convergence, the other heuristic is used to obtain non-delay schedules from the feasible solutions gained by the neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency. The strategy for solving practical job-shop scheduling problems is provided.Th...
Cataloged from PDF version of article.Artificial neural networks (ANNs) have been successfully appli...
This paper discusses the application of neural networks to select the best heuristic algorithm to so...
This thesis focuses on the development of a rule-based scheduler, based on production rules derived ...
A new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. T...
The file attached to this record is the author's final peer reviewed version.This paper proposes a n...
Job-shop scheduling is one of the most difficult production scheduling problems in industry. This pa...
An efficient constraint satisfaction based adaptive neural network and heuristics hybrid approach fo...
Job-shop scheduling is one of the most difficult production scheduling problems in industry. This pa...
Job-shop scheduling is one of the most difficult production scheduling problems in industry. This pa...
This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CS...
Copyright @ Springer Science + Business Media, LLC 2009This paper presents an improved constraint sa...
Copyright @ 2000 IEEEThis paper presents a constraint satisfaction adaptive neural network, together...
This paper presents an improved constraint satisfaction adaptive neural network for job-shop schedul...
The expanded job-shop scheduling problem (EJSSP) is a practical production scheduling problem with p...
Artificial neural networks (ANNs) have been successfully applied to solve a variety of problems. Thi...
Cataloged from PDF version of article.Artificial neural networks (ANNs) have been successfully appli...
This paper discusses the application of neural networks to select the best heuristic algorithm to so...
This thesis focuses on the development of a rule-based scheduler, based on production rules derived ...
A new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. T...
The file attached to this record is the author's final peer reviewed version.This paper proposes a n...
Job-shop scheduling is one of the most difficult production scheduling problems in industry. This pa...
An efficient constraint satisfaction based adaptive neural network and heuristics hybrid approach fo...
Job-shop scheduling is one of the most difficult production scheduling problems in industry. This pa...
Job-shop scheduling is one of the most difficult production scheduling problems in industry. This pa...
This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CS...
Copyright @ Springer Science + Business Media, LLC 2009This paper presents an improved constraint sa...
Copyright @ 2000 IEEEThis paper presents a constraint satisfaction adaptive neural network, together...
This paper presents an improved constraint satisfaction adaptive neural network for job-shop schedul...
The expanded job-shop scheduling problem (EJSSP) is a practical production scheduling problem with p...
Artificial neural networks (ANNs) have been successfully applied to solve a variety of problems. Thi...
Cataloged from PDF version of article.Artificial neural networks (ANNs) have been successfully appli...
This paper discusses the application of neural networks to select the best heuristic algorithm to so...
This thesis focuses on the development of a rule-based scheduler, based on production rules derived ...