A survey of exact algorithms for the simple assembly line balancing problem
Management Science
Identifying multiple solutions for assembly line balancing having stochastic task times
Computers and Industrial Engineering
An efficient heuristic for solving stochastic assembly line balancing problems
Computers and Industrial Engineering
Ranking fuzzy numbers with integral value
Fuzzy Sets and Systems
Solving fuzzy assembly-line balancing problem with genetic algorithms
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms for assembly line balancing with various objectives
Computers and Industrial Engineering - Special issue: IE in Korea
Computers and Operations Research
A Hybrid Genetic Algorithm for Assembly Line Balancing
Journal of Heuristics
Computers and Operations Research
Assembly line balancing problem with deterioration tasks and learning effect
Expert Systems with Applications: An International Journal
A Heuristic Approach for Fuzzy U-shaped Line Balancing Problem
FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
A solution procedure for type E simple assembly line balancing problem
Computers and Industrial Engineering
Multi-objective fuzzy assembly line balancing using genetic algorithms
Journal of Intelligent Manufacturing
Computers and Operations Research
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Consideration is given to a single-model assembly line balancing problem with fuzzy task processing times. The problem referred to herein as f-SALBP-E consists of finding a combination of the number of workstations and the cycle time as well as a respective line balance such that the efficiency of the line is maximized. f-SALBP-E is an extension of the classical SALBP-E under fuzziness. First, a formulation of the problem is given with the tasks processing times presented by triangular fuzzy membership functions. Then, since the problem is known to be NP-hard, a meta-heuristic based on a Genetic Algorithm (GA) is developed for its solution. The performance of the proposed solution approach is studied and discussed over multiple benchmarks test problems taken from the open literature. The results demonstrate very satisfactory performance for the developed approach in terms of both solution time and quality.