Fundamentals of queueing theory (2nd ed.).
Fundamentals of queueing theory (2nd ed.).
A comparison of neighborhood search techniques for multi-objective combinatorial problems
Computers and Operations Research
Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
A genetic alorithm for multiple objective sequencing problems in mixed model assembly lines
Computers and Operations Research
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Best compromise solution for a new multiobjective scheduling problem
Computers and Operations Research
Expert Systems with Applications: An International Journal
A multi-criteria approach for scheduling semiconductor wafer fabrication facilities
Journal of Scheduling
Expert Systems with Applications: An International Journal
International Journal of Computer Applications in Technology
Integrated process planning and scheduling in a supply chain
Computers and Industrial Engineering
International Journal of Computer Integrated Manufacturing - Global Competitive Manufacturing
Research on quality performance conceptual design based on SPEA2+
Computers & Mathematics with Applications
Expert Systems with Applications: An International Journal
Non-identical parallel machine scheduling using genetic algorithm
Expert Systems with Applications: An International Journal
Precast production scheduling using multi-objective genetic algorithms
Expert Systems with Applications: An International Journal
A genetic algorithm-based scheduler for multiproduct parallel machine sheet metal job shop
Expert Systems with Applications: An International Journal
Multi-objective design optimization of MCM placement
IMCAS'06 Proceedings of the 5th WSEAS international conference on Instrumentation, measurement, circuits and systems
A multiobjective optimization approach to solve a parallel machines scheduling problem
Advances in Artificial Intelligence
Advances in Engineering Software
Multi-objective genetic-based algorithms for a cross-docking scheduling problem
Applied Soft Computing
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Advances in Engineering Software
A genetic algorithm for scheduling of jobs on lines of press machines
LSSC'05 Proceedings of the 5th international conference on Large-Scale Scientific Computing
Multiobjective scheduling of jobs with incompatible families on parallel batch machines
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
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In this paper we propose a two-stage multi-population genetic algorithm (MPGA) to solve parallel machine scheduling problems with multiple objectives. In the first stage, multiple objectives are combined via the multiplication of the relative measure of each objective. Solutions of the first stage are arranged into several sub-populations, which become the initial populations of the second stage. Each sub-population then evolves separately while an elitist strategy preserves the best individuals of each objective and the best individual of the combined objective. This approach is applied in parallel machine scheduling problems with two objectives: makespan and total weighted tardiness (TWT). The MPGA is compared with a benchmark method, the multi-objective genetic algorithm (MOGA), and shows better results for all of the objectives over a wide range of problems. The MPGA is extended to scheduling problems with three objectives: makespan, TWT, and total weighted completion times (TWC), and also performs better than MOGA.