Wang, F.; Rao, Y.; Zhang, C.; Tang, Q.; Zhang, L. Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes. Sustainability2016, 8, 762.
Wang, F.; Rao, Y.; Zhang, C.; Tang, Q.; Zhang, L. Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes. Sustainability 2016, 8, 762.
Wang, F.; Rao, Y.; Zhang, C.; Tang, Q.; Zhang, L. Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes. Sustainability2016, 8, 762.
Wang, F.; Rao, Y.; Zhang, C.; Tang, Q.; Zhang, L. Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes. Sustainability 2016, 8, 762.
Abstract
With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.
Keywords
energy-efficiency scheduling; flexible flow shop; estimation of distribution algorithm; turning processing
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
