In this paper we observe the assembly line design problem in a sewing line of a shoe manufacturing company. One new shoe style is considered as one separate model. The combination of workstations, equipments and operators needs to be decided before running the production of each new model. The model parameters are improved during the production because of task combination or learning curve; therefore the optimal solution of the problem is changeable. As a result, it is critical to develop a robust procedure to rapidly deliver an “optimal” line design.
In the study, a rank-positional-weighted heuristics and hybrid genetic algorithm are proposed to solve the Resource Constrained Assembly Line Balancing Problem (RCALBP).. First the heuristics is developed to assign tasks and required machines into workstation. Then these solutions are used as an initiative population for the hybrid genetic algorithm. Experiment design is conducted to validate the performance of the proposed methods. One existing heuristics, new bidirectional heuristic for the assembly line balancing problem (ALBP), is chosen to compare. The output of these methods is analyzed and evaluated using statistical technique.
The result shows these methods do not reach different objective value for simple problem. When the difficulty of problem increases in term of size and shape, the proposed genetic algorithm achieves better results than existing heuristics. The developed problem is inherited from the work of researchers as well as the contribution of practitioners. Assumption is established and validated by experts in footwear making industry. Therefore, the proposed approaches are capable to apply in real manufacturing environment.

In this paper we observe the assembly line design problem in a sewing line of a shoe manufacturing company. One new shoe style is considered as one separate model. The combination of workstations, equipments and operators needs to be decided before running the production of each new model. The model parameters are improved during the production because of task combination or learning curve; therefore the optimal solution of the problem is changeable. As a result, it is critical to develop a robust procedure to rapidly deliver an “optimal” line design.
In the study, a rank-positional-weighted heuristics and hybrid genetic algorithm are proposed to solve the Resource Constrained Assembly Line Balancing Problem (RCALBP).. First the heuristics is developed to assign tasks and required machines into workstation. Then these solutions are used as an initiative population for the hybrid genetic algorithm. Experiment design is conducted to validate the performance of the proposed methods. One existing heuristics, new bidirectional heuristic for the assembly line balancing problem (ALBP), is chosen to compare. The output of these methods is analyzed and evaluated using statistical technique.
The result shows these methods do not reach different objective value for simple problem. When the difficulty of problem increases in term of size and shape, the proposed genetic algorithm achieves better results than existing heuristics. The developed problem is inherited from the work of researchers as well as the contribution of practitioners. Assumption is established and validated by experts in footwear making industry. Therefore, the proposed approaches are capable to apply in real manufacturing environment.

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