Unrelated parallel machine scheduling problem with energy and tardiness cost

With pervasive applications of new information technology, a larger number of manufacturing big data is generated. This paper considers the unrelated parallel scheduling problem within the background of big data and cloud technology for manufacturing. Traditional unrelated parallel problem has been extensively investigated, and the main objective has been to improve production efficiency. With regard to the environmental concern, there has been limited literature. Therefore, this paper considers an unrelated parallel machine scheduling problem with the objective of minimization to the total tardiness and energy consumption where the energy consumption on each machine is also unrelated parallel. First, we give a mathematical model of this problem. Second, ten heuristic algorithms are, respectively, proposed based on the priority rules, the energy consumption, and the combinational rules due to the complexity of this problem. Finally, in order to test the performance of these ten algorithms, computational experiments are designed. In the computational experiments, lots of instances are generated, and the computational results indicate that the algorithms based on the combinational rules outperform the ones based on the priority rules and energy consumption, with respect to the unrelated parallel scheduling problem proposed in this paper.