Solving fuzzy assembly-line balancing problem with genetic algorithms
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Optimally balancing assembly lines with different workstations
Discrete Applied Mathematics - Special issue: Third ALIO-EURO meeting on applied combinatorial optimization
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
A solution procedure for type E simple assembly line balancing problem
Computers and Industrial Engineering
Rule-based modeling and constraint programming based solution of the assembly line balancing problem
Expert Systems with Applications: An International Journal
Computers and Operations Research
A multi-objective genetic algorithm for mixed-model assembly line rebalancing
Computers and Industrial Engineering
A branch-and-bound algorithm for assembly line worker assignment and balancing problems
Computers and Operations Research
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In the past decades, robots have been extensively applied in assembly systems as called robotic assembly lines. When changes in the production process of a product take place, the line needs to be reconfigured in order to improve its productivity. This study presents a type II robotic assembly line balancing (rALB-II) problem, in which the assembly tasks have to be assigned to workstations, and each workstation needs to select one of the available robots to process the assigned tasks with the objective of minimum cycle time. An innovative genetic algorithm (GA) hybridized with local search is proposed for the problem. The genetic algorithm uses a partial representation technique, where only part of the decision information about a candidate solution is expressed in the chromosome and the rest is computed via a heuristic method. Based on different neighborhood structures, five local search procedures are developed to enhance the search ability of GA. The coordination between these procedures is well considered in order to escape from local optima and to reduce computation time. The performance of the hybrid genetic algorithm (hGA) is tested on 32 rALB-II problems and the obtained results are compared with those by other methods.