Processes 2021 , 9 , 274
13of 24
4. Case Study In order to demonstrate the feasibility of the energy-efficiency scheduling model under TOU electricity tariffs for tissue paper mills and the superiority of the proposed IMOEA/DTL, three popular multi-objective optimization algorithms, namely, MOEA/D- MR [31], Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Strength Pareto Evolutionary Algorithm 2 (SPEA2), are compared with the IMOEA/DTL in eight schedul- ing problems. The energy-saving potential of the IMOEA/DTL is evaluated by comparing IMOEA/DTL with workers in practice. The whole experiments are implemented in MAT- LAB2015b. 4.1. Experiment Data The data are collected from a tissue paper mill in Guangdong, China. The data are collected from the Jiangmen branch of Vinda Paper (China) Co., Ltd., Jiangmen City, Guang- dong Province, China. The data collection time is from 2017 to 2018. The scheduling data and product process data are mainly collected from the production scheduling department. These data come from the enterprise resource planning system. Machine data and energy consumption data are automatically collected by the manufacturing execution system. There are six production lines in the pulping and papermaking stage and seven production lines in the conversion stage. A total of 16 scheduling instances are generated randomly, the numbers and names of jobs in each scheduling problem are listed in Table 2. In each scheduling problem, the size of each job is generated randomly and obeys uniform distri- bution in the range of [10,000, 600,000]. In the tissue paper mill, the speed of the production line varies with the type of products, but each production line has a maximum speed. The maximum speeds in the pulping and papermaking stage and the conversion stage are shown as Table 3. Similarly, the power of each production line also varies with the product type. The maximum power of the pulping and papermaking stage and the conversion stage is shown Table 4. The setup energy consumption is dependent on the setup time and the setup power. In this study, the setup time of the pulping and papermaking stage and the conversion stage is randomly generated based on the real situation. The setup time of the pulping and papermaking stage shows uniform distribution in the range of [10, 100], while the setup time of the conversion stage shows uniform distribution in the range of [10, 60]. Table 5 lists the setup power of the pulping and papermaking stage and the conversion stage. The transportation energy consumption is related to the distance between production lines. The transportation energy consumption per unit product is shown in Table 6.
Table2. The name and number of jobs of 16 scheduling problems.
Problems
JobNumber
Job1 Job2 Job3 Job4 Job5 Job6 Job7 Job8 Job9
50 60 70 80 90
100 110 120 130 140 150 160 170 180 190 200
Job10 Job11 Job12 Job13 Job14 Job15 Job16
Made with FlippingBook - Online magazine maker