Sequencing to minimize work overload in assembly lines with product options
Management Science
Sequencing JIT mixed-model assembly lines
Management Science
A genetic alorithm for multiple objective sequencing problems in mixed model assembly lines
Computers and Operations Research
Multiobjective evolutionary algorithm test suites
Proceedings of the 1999 ACM symposium on Applied computing
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A dynamic programming algorithm for scheduling mixed-model, just-in-time production systems
Mathematical and Computer Modelling: An International Journal
Pareto multi-criteria decision making
Advanced Engineering Informatics
A new multi-objective algorithm for a project selection problem
Advances in Engineering Software
Advances in Engineering Software
Computers and Operations Research
Computers and Industrial Engineering
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A mixed-model assembly line (MMAL) is a type of production line where a variety of product models similar to product characteristics are assembled. There is a set of criteria on which to judge sequences of product models in terms of the effective utilization of this line. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three prominent multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSS outperforms the existing genetic algorithms, especially for the large-sized problems.