SOLVING JOB SHOP SCHEDULING PROBLEM WITH THE AID OF EVOLUTION OF CUB TO PREDATOR (ECP)

For promising outcome in industry 4.0, scheduling place a vital role for effective utilization of jobs allocate to machine. Job's and machine's are two attributes need to schedule for minimize makespan time, for each job we need to schedule the machine. Each job in a machine has its own process time, manipulation of all process time said to be makespan time that should minimized. Job shop scheduling is an effective tool incorporate with NP hard problems to achieve minimized makespan time. To achieve minimized makespan time optimization involve in this process those are namely Genetic Algorithm (GA), Opposition based Genetic Algorithm with Cauchy Mutation (OGA-CM), Opposition based Particle Swarm Optimization with Cauchy Mutation (OPSO-CM), Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO) and Evolution of Cub to Predator (ECP). While applying these aforementioned optimization techniques, they reveal minimized makespan time compare with benchmark problems. Amid, ECP revels minimized makespan time for all six-bench mark problems compare with other competitive algorithm.