Permutation Flow Shop Scheduling with dynamic job order arrival

The Permutation Flow Shop Scheduling Problem (PFSP) is known as complex combinatorial optimization problem. In PFSPs, the jobs are sequenced by optimizing certain performance measure such as makespan. As of the literature, the existing algorithms deal with static PFSPs. However, in practice, the jobs arrive continuously with random inter-arrival time. It may not be feasible to process all the jobs by satisfying all the constraints. In this paper, we propose a new algorithm, based on Genetic Algorithm (GA), to deal with multiple jobs arriving at different point in time in Permutation Flow Shop environment. To explain the insight of problem complexity, we provide some simulations results.