Author(s)

Vedavyasrao S. Jorapur¹, Vinod S. Puranik², Anand S. Deshpande³, Mahesh Sharma⁴

¹Department of Industrial and Production Engineering, Basaveshwar Engineering College, Bagalkot, India.
²Basaveshwar Engineering College, Bagalkot, India.
³Gogte Institute of Technology, Belagavi, India.
⁴Fr. Conceicao Rodrigues College of Engineering, Bandra, West Mumbai, India.

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.

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

Jorapur, V. , Puranik, V. , Deshpande, A. and Sharma, M. (2016) A Promising Initial Population Based Genetic Algorithm for Job Shop Scheduling Problem. Journal of Software Engineering and Applications, 9, 208-214. doi: 10.4236/jsea.2016.95017.