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.