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Multi-objective optimization Model for Flexible Job Shop Scheduling Problem Considering Transportation Constraints: A Comparative Study
- Source :
- CEC
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Flexible job shop scheduling problem (FJSP) has long been a complex problem due to the resource flexibility and strong constraints, generating a mixed-integer non-linear optimization problem. The problem becomes more complex with the increasing demand of energy reduction and the corresponding environmental impacts. Proper production scheduling is of significant potential in saving energy in the manufacturing system. In this paper, a multi-objective FJSP model is formulated with the objectives of minimizing the makespan and energy consumption considering strong transportation constraints. Two popular multi-objective optimization solver including Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and A Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) are employed and compared in a real-world instance of the FJSP, associated with novel coding schemes. The results show that the proposed model is well solved by the two solvers and NSGA-II get the better solutions.
- Subjects :
- Mathematical optimization
Optimization problem
Job shop scheduling
Computer science
020209 energy
Scheduling (production processes)
Sorting
Evolutionary algorithm
02 engineering and technology
Energy consumption
010501 environmental sciences
Solver
01 natural sciences
Multi-objective optimization
Evolutionary computation
0202 electrical engineering, electronic engineering, information engineering
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 IEEE Congress on Evolutionary Computation (CEC)
- Accession number :
- edsair.doi...........f193e3a813b1e8de6989fe060aa2cba9