Solving fuzzy assembly-line balancing problem with genetic algorithms
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
A review and classification of fuzzy mathematical programs
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Mixed model assembly line balancing problem with fuzzy operation times and drifting operations
Proceedings of the 40th Conference on Winter Simulation
A PRACTICAL FUZZY DIGRAPH MODEL FOR MODELING MANUFACTURING FLEXIBILITY
Cybernetics and Systems
On the centroids of fuzzy numbers
Fuzzy Sets and Systems
Multi-objective aggregate production planning with fuzzy parameters
Advances in Engineering Software
Review: Industrial applications of type-2 fuzzy sets and systems: A concise review
Computers in Industry
Multiple-colony ant algorithm for parallel assembly line balancing problem
Applied Soft Computing
A direct solution approach to fuzzy mathematical programs with fuzzy decision variables
Expert Systems with Applications: An International Journal
Multi-objective fuzzy assembly line balancing using genetic algorithms
Journal of Intelligent Manufacturing
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It is difficult to make efficient decisions in manufacturing environments due to complexity and uncertainty which one can experience during most of the production phases. Although handling uncertainty is a difficult issue, it must be considered during modeling in order to obtain more realistic solutions for complex problems. One of the flow oriented production systems which is used in manufacturing is parallel assembly lines. They are preferred due to their flexible and productive nature. In this paper, parallel assembly line balancing problem with fuzzy parameters is studied in order to provide more realistic solutions in which the problem data is imprecise. A multi-colony ant algorithm for solving parallel assembly line balancing problems with fuzzy cycle and task times is proposed. Considering task times fuzzy is necessary especially in manual assembly operations. The fuzziness of the cycle time is related to task time variability. The proposed approach is tested on benchmark problems and solutions are presented.