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Research on Energy-Saving Production Scheduling Based on a Clustering Algorithm for a Forging Enterprise

by *,†, , and
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Marc A. Rosen
Sustainability 2016, 8(2), 136; https://doi.org/10.3390/su8020136
Received: 18 October 2015 / Revised: 23 January 2016 / Accepted: 28 January 2016 / Published: 2 February 2016
Energy efficiency is a buzzword of the 21st century. With the ever growing need for energy efficient and low-carbon production, it is a big challenge for high energy-consumption enterprises to reduce their energy consumption. To this aim, a forging enterprise, DVR (the abbreviation of a forging enterprise), is researched. Firstly, an investigation into the production processes of DVR is given as well as an analysis of forging production. Then, the energy-saving forging scheduling is decomposed into two sub-problems. One is for cutting and machining scheduling, which is similar to traditional machining scheduling. The other one is for forging and heat treatment scheduling. Thirdly, former forging production scheduling is presented and solved based on an improved genetic algorithm. Fourthly, the latter is discussed in detail, followed by proposed dynamic clustering and stacking combination optimization. The proposed stacking optimization requires making the gross weight of forgings as close to the maximum batch capacity as possible. The above research can help reduce the heating times, and increase furnace utilization with high energy efficiency and low carbon emissions. View Full-Text
Keywords: energy-saving scheduling; energy efficiency; low carbon mission; forging production; dynamic clustering; stacking combination optimization energy-saving scheduling; energy efficiency; low carbon mission; forging production; dynamic clustering; stacking combination optimization
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Tong, Y.; Li, J.; Li, S.; Li, D. Research on Energy-Saving Production Scheduling Based on a Clustering Algorithm for a Forging Enterprise. Sustainability 2016, 8, 136.

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