An improved genetic algorithm for solving flexible job shop scheduling problem

The Flexible Job Shop Scheduling Problem (FJSP) is a very important problem in the modern manufacturing system. It is an extension of the classical job shop scheduling problem. It allows an operation to be processed by any machine from a given set. It is also a NP-hard problem. Since FJSP requires an additional decision of machine allocation during scheduling, therefore it is much more complex problem than JSP. This paper proposed an improved genetic algorithm (IGA) to solve FJSP. We tested the IGA against the GA method. Simulation results demonstrate that it can be superior to the regular GA. We also tested the IGA with the exhaustion method to show the algorithm's efficiency.