TY - JOUR
T1 - A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization
AU - Wang, Han
AU - Jiang, Zhigang
AU - Wang, Yan
AU - Zhang, Hua
AU - Wang, Yanhong
PY - 2018/3/27
Y1 - 2018/3/27
N2 - Scheduling can have significant impacts on energy saving in manufacturing systems. The complex process constraints and dynamic manufacturing tasks in flexible manufacturing system make the scheduling a complicated nonlinear programming problem. To this end, this paper proposes a two-stage energy-saving optimization method for Flexible Job-Shop Scheduling Problems (FJSP). In this method, an operation-based integrated chart is firstly proposed to reveal the dynamic characteristics of the operations, enabling the energy-saving scheduling optimization. Then the optimization is conducted at two stages: the machine tool stage and the operation sequence stage. A Modified Genetic Algorithm (MGA) is applied at the first stage and a hybrid method that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) is adopted at the second stage. Finally, a case study is employed to illustrate the applicability and validity of the proposed method. The results revealed that the proposed method can effectively optimize FJSP. This may provide a basis for decision makers to utilize a manufacturing scheduling that is optimized regarding its energy saving.
AB - Scheduling can have significant impacts on energy saving in manufacturing systems. The complex process constraints and dynamic manufacturing tasks in flexible manufacturing system make the scheduling a complicated nonlinear programming problem. To this end, this paper proposes a two-stage energy-saving optimization method for Flexible Job-Shop Scheduling Problems (FJSP). In this method, an operation-based integrated chart is firstly proposed to reveal the dynamic characteristics of the operations, enabling the energy-saving scheduling optimization. Then the optimization is conducted at two stages: the machine tool stage and the operation sequence stage. A Modified Genetic Algorithm (MGA) is applied at the first stage and a hybrid method that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) is adopted at the second stage. Finally, a case study is employed to illustrate the applicability and validity of the proposed method. The results revealed that the proposed method can effectively optimize FJSP. This may provide a basis for decision makers to utilize a manufacturing scheduling that is optimized regarding its energy saving.
KW - Energy consumption
KW - Energy-saving scheduling
KW - Flexible job-shop scheduling problem
KW - Modified genetic algorithm
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=85047493844&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2018.03.254
DO - 10.1016/j.jclepro.2018.03.254
M3 - Article
AN - SCOPUS:85047493844
SN - 0959-6526
VL - 188
SP - 575
EP - 588
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
ER -
