Abstract:
Job Shop Scheduling Problem (JSSP) is one of the
most common problems in manufacturing due to its
widespread application and the usability across the
manufacturing industry. Due to the vast solution space the
JSSP problem deals with, it is impossible to apply brute force
search techniques to obtain an optimal solution. In this
research, Genetic Algorithm (GA) approach, which is another
widely used nonlinear optimization technique, has been used to
propose a solution using a novel chromosome representation
which makes seeking solutions for the Dynamic JSSP more
efficient. Due to operation order criteria of the jobs and the
machine allocation requirement on machines, generating
solutions for JSSP needs an extra effort to eliminate infeasible
solutions. Due to level of the complexity with added
constraints, there is a high tendency to get more infeasible
solutions than feasible solutions. This results in consuming a lot
of computing resources to correct such a conventional orderbased
chromosome representation. Due to this, a new
representation is proposed in this paper. It is found that the
proposed new chromosome representation approach makes it
possible to model such dynamic behaviours of schedules
without compromising the performances of GA.
Citation:
C. Kurera and P. Dasanayake, "New Approach to Solve Dynamic Job Shop Scheduling Problem Using Genetic Algorithm," 2018 3rd International Conference on Information Technology Research (ICITR), 2018, pp. 1-6, doi: 10.1109/ICITR.2018.8736128.