Optimization of job shop scheduling problems using teaching-learning-based optimization algorithm

Abstract

A job shop scheduling problem is one of the most difficult NP hard combinatorial optimization problems. In a job shop scheduling problem (JSSP), there are n jobs that should be processed on m machines. Each job consists of a predetermined sequence of task operations, each of which needs to be processed without interruption for a given period of time on a given machine. Tasks of the same job cannot be processed concurrently. In recent years, optimization algorithms such as simulated annealing (SA), genetic algorithm (GA), tabu search (TS), ant colony optimization (ACO) particle swarm optimization (PSO) and artificial bee colony (ABC) have played a significant role in solving small-scale job shop scheduling problems. However, when the problem size grows, metaheuristic algorithms usually take excessive time to converge. In this study, a recently developed Teaching-Learning-Based Optimization (TLBO) method is proposed to solve the job shop scheduling problems to minimize the makespan. The proposed algorithm is tested on 58 job shop scheduling bench mark problems from OR Library and results are compared with the results obtained by using the other algorithms. It is concluded that the TLBO algorithm can be effectively used for job shop scheduling problems.