| Summary |
Zohn-Zong Yu, 2008.06
Department of Transportation Technology and Management National Chiao Tung University
AGV (Automated Guided Vehicle) plays an important role for today’s manufacturing and warehousing systems. In a manufacturing process, the time actually spent on the machines is sometimes not very long; on the other hand, a significant portion of the whole process time is consumed for transporting and waiting. Therefore, this study considers a common single-loop AGV system and develops a mathematical model aiming to minimize the production cycle time (i.e., to maximize the production throughput), given the predetermined ES (Early Start) AGV dispatching policy.
Due to the complexity of the decisions, it is impossible to find the optimal solution for the problems with large size. Thus, this study designs a genetic algorithm to determine the sequence for the jobs in an MJS (Minimal Job Set) so as to reduce the AGV cycle time. This study also develops a mixed integer programming (MIP) model, which can find the optimal solution of small-size problems. In addition, its linear program (LP) relaxation is found to be a very tight lower bound. In order to verify the effectiveness of the developed solution algorithm, a series of test problems are designed for a system with two machines and one AGV.
Based on the result of the numerical experiment, it is found the solution generated by the heuristic algorithm is very close to the optimal solution, normally within 1% in terms of the objective function value. Besides, the solution quality is not sensitive to the problem size, and the computation time is acceptable for the realistic operation in the field. |
