Results 1 
2 of
2
An improved constraint satisfaction adaptive neural network for jobshop scheduling
, 2010
"... This paper presents an improved constraint satisfaction adaptive neural network for jobshop scheduling problems. The neural network is constructed based on the constraint conditions of a jobshop scheduling problem. Its structure and neuron connections can change adaptively according to the realt ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
This paper presents an improved constraint satisfaction adaptive neural network for jobshop scheduling problems. The neural network is constructed based on the constraint conditions of a jobshop scheduling problem. Its structure and neuron connections can change adaptively according to the realtime constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark jobshop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the ba
Jobshop Scheduling Problem, Artificial Immune Algorithm,
"... Scheduling problems are difficult types of production arrangement problems that enumerated among NPComplete problems. Some of evolutionary algorithms such as Genetic Algorithm, Ant Colony Optimization etc. have been used to solve this problem. In new years, Artificial Immune Algorithm is used to so ..."
Abstract
 Add to MetaCart
(Show Context)
Scheduling problems are difficult types of production arrangement problems that enumerated among NPComplete problems. Some of evolutionary algorithms such as Genetic Algorithm, Ant Colony Optimization etc. have been used to solve this problem. In new years, Artificial Immune Algorithm is used to solve optimization problems such as routing and scheduling. One of complex scheduling problems is Jobshop Scheduling problem. In this article we use immune system concepts of human body, to implement a new artificial immune algorithm for solving Jobshop scheduling problem. A new population generation method was proposed based on G&T algorithm. We use two mutation methods, namely Shift Change method and Inverse method in Jobshop scheduling for first time. Moreover, we describe a vaccination method named MCV, to make maximum advance in solutions, and then achieve to more than one optimal solution concurrently and release from local optimum. Finally, we test our method on the very famous benchmark of JSP, namely FT06, then show experimental results and get some conclusions.