This paper discusses the application of neural networks to select the best heuristic algorithm to solve a given scheduling problem. The two-stage hybrid flowshop with multiple identical parallel machines at the second stage is used as an example to discuss the process of selecting a scheduling heuristic through a neural-network approach. This paper uses the genetic-algorithm-based approach for training the neural network and shows that the suggested neural-network approach is quite effective and efficient for selecting the best heuristic algorithm for solving a given scheduling problem
This study compares three meta heuristics to minimize makespan (Cmax) for Hybrid Flow Shop (HFS) Sch...
Flow shop scheduling problem consists of scheduling n jobs on m machines. As an attempt for meeting ...
Production scheduling is a branch of operational research that uses discrete approaches to address a...
A new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. T...
The expanded job-shop scheduling problem (EJSSP) is a practical production scheduling problem with p...
This paper deals with the problem of scheduling on makespan criterion in the flow shop environment. ...
We present a novel strategy to solve a two-stage hybrid flow shop scheduling problem with family set...
This is the author's copy of the conference paper published in the proceedings of the International ...
Abstract—a new heuristic algorithm was designed by combining with Johnson method, NEH method and cha...
This paper uses the genetic algorithm combined with simulation techniques to evaluate the performanc...
In this paper, a hybrid flow shop scheduling problem with a new approach considering time lags and s...
This study compares three meta heuristics to minimize makespan (Cmax) for Hybrid Flow Shop (HFS) Sch...
SILVA, J. L. C. ; SOMA, N. Y. A Constructive hybrid genetic algorithm for the flowshop scheduling pr...
This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CS...
The file attached to this record is the author's final peer reviewed version.This paper proposes a n...
This study compares three meta heuristics to minimize makespan (Cmax) for Hybrid Flow Shop (HFS) Sch...
Flow shop scheduling problem consists of scheduling n jobs on m machines. As an attempt for meeting ...
Production scheduling is a branch of operational research that uses discrete approaches to address a...
A new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. T...
The expanded job-shop scheduling problem (EJSSP) is a practical production scheduling problem with p...
This paper deals with the problem of scheduling on makespan criterion in the flow shop environment. ...
We present a novel strategy to solve a two-stage hybrid flow shop scheduling problem with family set...
This is the author's copy of the conference paper published in the proceedings of the International ...
Abstract—a new heuristic algorithm was designed by combining with Johnson method, NEH method and cha...
This paper uses the genetic algorithm combined with simulation techniques to evaluate the performanc...
In this paper, a hybrid flow shop scheduling problem with a new approach considering time lags and s...
This study compares three meta heuristics to minimize makespan (Cmax) for Hybrid Flow Shop (HFS) Sch...
SILVA, J. L. C. ; SOMA, N. Y. A Constructive hybrid genetic algorithm for the flowshop scheduling pr...
This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CS...
The file attached to this record is the author's final peer reviewed version.This paper proposes a n...
This study compares three meta heuristics to minimize makespan (Cmax) for Hybrid Flow Shop (HFS) Sch...
Flow shop scheduling problem consists of scheduling n jobs on m machines. As an attempt for meeting ...
Production scheduling is a branch of operational research that uses discrete approaches to address a...