Teaching Learning based Optimization:An Optimization Technique for Job Shop Scheduling

In present days, Job Shop Scheduling Problem (JSSP) is one of the most important areas of research. JSSP is a Non - Deterministic Polynomial hard combinatorial optimization problem. In JSSP, there are 'n' jobs and 'm' machines and each job has its own predefined Operation Sequence and processed in that order. Many Metaheuristics such as Genetic Algorithm (GA), Simulated Annealing (SA), Artificial Immune System (AIS), Artificial Bee Colony (ABC) and Differential Evolution (DE) had been applied for a few years in the past to find an Optimal Operation Scheduling with minimum Makespan. TLBO is a recently developed random population optimization technique for solving scheduling problems. It has two phases namely, Teacher Phase and Learner Phase. Teacher phase indicates learning something from a teacher and Learner phase indicates learning by self study. TLBO performance can be assessed by solving 10 Taillard benchmark problems at different number of iterations and population size, and comparing the results with AIS and DE. The results show that TLBO is an effective evolutionary algorithm to develop an Optimal Operations Scheduling.