A Genetic Algorithm for Job Shop Scheduling Problem in Agile Manufacturing System

The new challenges of Agile Manufacturing system led to study of computational cooperative problem solving models. The goal is to develop appropriate computational approaches to support adaptive, cost-effective responsiveness. In particular, the challenging problem of job shop scheduling, this has been one of the primary foci of production scheduling research. In this paper, we propose genetic algorithm to overcome the impact of agile environment such as changing customers’ preferences, machine breakdowns, deadlocks, etc. by inserting the slack that can absorb these disruptions without affecting the other scheduled activities. The proposed algorithm also focuses on the impact of agility in the job shop environment in such highly complex scenarios. The algorithm inherits the delicacies of Genetic Algorithm (GA) converges towards optimality in less computational time. The proposed model encompasses the objectives of minimizing the delay time and flow time using the genetic algorithm