Optimization of the Flexible Job Shop Scheduling Problem for Economic Sustainability
The flexible job-shop scheduling problem (FJSP) is one of the challenging optimization problems as they occupy very large search space. Solving this kind of problems with conventional methods are obsolete now as the Internet of Things (IoT) has changed scheduling platform by means of cloud computing and advanced data analytics. Genetic Algorithms (GAs) is a popular modern tool for machine scheduling problems and in this work, a scheduling algorithm has been developed to minimize total tardiness and make span time of parallel machines which is promoting overall economic sustainability. The algorithm consists of a machine selection module (MSM) that helps to select the right machine on the right time with the help of global selection (GS) technique by generating high quality initial population. To represent an optimized solution of the FJSP, an improved chromosome representation is used while adopting uniform crossover and mutation operator. The result showed that proposed algorithm is much more effective and efficient for solving flexible job-shop scheduling problem which is helping to reduce the overall downtime significantly.
