Ge, H.W. and Sun, L. (2008) An Effective PSO and AIS-Based Hybrid Intelligent Algorithm for Job-Shop Scheduling. IEEE Transactions on System, Man, and Cybernetics-Part A: Systems and Humans, 38, 358-368.
http://dx.doi.org/10.1109/TSMCA.2007.914753

has been cited by the following article:

• TITLE: A Promising Initial Population Based Genetic Algorithm for Job Shop Scheduling Problem

  AUTHORS: Vedavyasrao S. Jorapur, Vinod S. Puranik, Anand S. Deshpande, Mahesh Sharma

  KEYWORDS: Job Shop Scheduling, Job Based Representation, NP-Hard, Recombination Operators etc.

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.9 No.5, May 27, 2016

  ABSTRACT: Job shop scheduling problem is typically a NP-Hard problem. In the recent past efforts put by researchers were to provide the most generic genetic algorithm to solve efficiently the job shop scheduling problems. Less attention has been paid to initial population aspects in genetic algorithms and much attention to recombination operators. Therefore authors are of the opinion that by proper design of all the aspects in genetic algorithms starting from initial population may provide better and promising solutions. Hence this paper attempts to enhance the effectiveness of genetic algorithm by providing a new look to initial population. This new technique along with job based representation has been used to obtain the optimal or near optimal solutions of 66 benchmark instances which comprise of varying degree of complexity.