151. Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times
- Author
-
Xin Yang, Zhenxiang Zeng, Xue-Shan Sun, and Rui-Dong Wang
- Subjects
0209 industrial biotechnology ,Fossil Fuels ,Computer science ,lcsh:Medicine ,02 engineering and technology ,020901 industrial engineering & automation ,Mathematical and Statistical Techniques ,Manufacturing ,0202 electrical engineering, electronic engineering, information engineering ,lcsh:Science ,Multidisciplinary ,Mathematical model ,Mathematical Models ,Applied Mathematics ,Simulation and Modeling ,Sorting ,Energy consumption ,Coal ,Physical Sciences ,Engineering and Technology ,020201 artificial intelligence & image processing ,Organic Materials ,Algorithms ,Research Article ,Optimization ,Mathematical optimization ,Materials Science ,Equipment ,Fuels ,Research and Analysis Methods ,Population Metrics ,Genetic algorithm ,Materials by Attribute ,Consumption (economics) ,Population Density ,Stochastic Processes ,Job shop scheduling ,Population Biology ,business.industry ,Stochastic process ,Genetic Algorithms ,lcsh:R ,Biology and Life Sciences ,Models, Theoretical ,Probability Theory ,Energy and Power ,lcsh:Q ,business ,Energy Metabolism ,Mathematics - Abstract
This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems.
- Published
- 2016