The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors ...The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production plarming and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed small- and large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.展开更多
As the largest energy consuming manufacturing sector and one of the most important sources of carhon dioxide （CO2） emissions, the China＇s iron and steel industry has paid attention to the study of changing trend an...As the largest energy consuming manufacturing sector and one of the most important sources of carhon dioxide （CO2） emissions, the China＇s iron and steel industry has paid attention to the study of changing trend and influencing factors of CO2 emissions from energy use. The logarithmic mean Divisia index （LMD1） technique is used to decompose total change in CO2 emissions into four factors： emission factor effect, energy structure effect, energy consumption effect, and steel production effect. The results show that the steel production effect is the major factor which is responsible for the rise in CO2 emissions; whereas the energy consumption effect contributes most to the reduction in CO2 emissions. And the emission factor effect makes a weak negative contribution to the increase of CO2 emis- sions. To find out the detailed relationship between change in energy consumption or steel production and change in CO2 emissions, the correlation equations are also proposed.展开更多
基金Supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Program(Grant No.294931)National Science Foundation of China(Grant No.51175262)+1 种基金Jiangsu Provincial Science Foundation for Excellent Youths of China(Grant No.BK2012032)Jiangsu Provincial Industry-Academy-Research Grant of China(Grant No.BY201220116)
文摘The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production plarming and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed small- and large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.
基金Item Sponsored by the Fundamental Research Funds for the Central Universities of China(N090602007)
文摘As the largest energy consuming manufacturing sector and one of the most important sources of carhon dioxide （CO2） emissions, the China＇s iron and steel industry has paid attention to the study of changing trend and influencing factors of CO2 emissions from energy use. The logarithmic mean Divisia index （LMD1） technique is used to decompose total change in CO2 emissions into four factors： emission factor effect, energy structure effect, energy consumption effect, and steel production effect. The results show that the steel production effect is the major factor which is responsible for the rise in CO2 emissions; whereas the energy consumption effect contributes most to the reduction in CO2 emissions. And the emission factor effect makes a weak negative contribution to the increase of CO2 emis- sions. To find out the detailed relationship between change in energy consumption or steel production and change in CO2 emissions, the correlation equations are also proposed.
