With the developing of industry, the topic of energy-saving is attracting more and more attentions. However, the attentions have focused primarily on the design activities of the firm. The planning/scheduling activities of manufacturing are neglected. In this paper, a mathematical model, which considers the energy consumption and preventive maintenance of equipment in the flexible job shop, is established, three objectives, such as the minimization of maximum completion time, the minimization of total production energy costs and the minimization of total energy costs of maintenance that are quantized by electricity, are taken into account. The algorithm of NSGA-II is presented to solve the optimization model, and experimental results validate the effectiveness of the proposed approach.

This paper develops an integrated process parameter optimization and scheduling problem, where process parameter optimization and flow shop scheduling are considered simultaneously. Two objectives are taken into account: minimize makespan and carbon emissions. Non-dominated sorting genetic algorithm is adopted to handle such a problem. Then, the researchers propose two carbon emission reducing mechanisms to optimize the scheduling results: postponing mechanism, and process parameter preliminary optimization (PPPOM) mechanism. There are four cases depending on whether or not mechanisms are employed. The effects of those mechanisms on minimum objective functions, number of non-dominated solutions and quality of non-dominated solutions are studied. The results indicate that those mechanisms have significant influence on the optimization results. Better non-dominated solutions are produced when more mechanisms are employed.

A mathematical model of multiple-linecollaborative manufacturing was formulated in this paper, and teaching-learning-based optimization algorithm was applied to solve the problem. A set of production orders are taken as example. The results prove that the mathematical model and algorithm can perform well in optimal scheduling of multiple-linecollaborative manufacturing.