A Genetic Algorithm approach for solving a Job Shop Scheduling problem

Job or task scheduling with shared resource is challenging. With the increase in the size of the problem manual or sequential approach fails. Scheduling becomes a costly and tedious process. Not only schedules are ineffective, but also the task to prepare schedules becomes overhead. As the time increases, the associated cost also increases. The allocation of shared resources (M) to jobs (J) such that a specific optimization criterion is met is called job shop scheduling(JSS). In this study the focused criteria are makespan, average flow time & average cost. JSS has complexity (J!)boolean AND M, which makes it NP hard. Researchers have been applying many different to solve the JSS problem. Metaheuristic techniques like Genetic Algorithm (GA) have shown good results and have been proven to be better performers than other techniques.