An improved genetic algorithm for a parallel machine scheduling problem with energy consideration

In recent years, there has been growing interest in reducing energy consumption in manufacturing industry. This paper focuses on the parallel machine scheduling problem extracting from the high-energy heating process in iron and steel enterprises. We first present a mixed integer mathematic model with the objective of minimizing the total energy consumption. Next, we propose an improved genetic algorithm (IGA) to find high-quality solutions to this mathematic model. Since the scheduling problem is NP-hard, the proposed IGA improves standard genetic algorithm (SGA) in following aspects: crossover operation and mutation operation based on problem characteristics and adaptive adjustment. To evaluate the proposed algorithm, we select two comparison algorithms: SGA and adaptive genetic algorithm (AGA), and conduct a serial of experiments with the case scenarios generated according to real-world production process. The results show that the proposed IGA has superior performance to the other two algorithms.