Enhancing optimal feeder assignment of the multi-head surface mounting machine using genetic algorithms

Authors:
Shaoyuan Li;Chaofang Hu;Fuhou Tian
Affiliations:
Institute of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Institute of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;Institute of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
Venue:
Applied Soft Computing
Year:
2008

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Abstract

The problem of minimizing the time required to populate a printed circuit board using a multi-head surface mounting machine is considered in this paper. The multi-head surface mounting machine is becoming increasingly popular due to its merit of picking or placing multiple components simultaneously in one pick-and-place operation, which reduces much portion of the assembly time. The complexity of the optimization problem of minimizing the assembly time results in that acquiring its desired solution is difficult. The total assembly time depends on two optimization problems: feeder assignment problem and pick-and-place sequencing problem. Although these two problems are interrelated, they are solved, respectively. Feeder assignment problem is one crucial problem of affecting surface mounting machine's productivity directly. Optimal feeder assignment can decrease sum of time of moving along slots, moving from slot to PCB and moving from PCB to slot for placement heads griping components after predetermining pick-and-place sequence. For it is of a combinatorial nature and NP-hard, there is no exact algorithm for it. As an efficient and useful procedure for solving the combinatorial optimization problems, the genetic algorithm with specific crossover and mutation operators is proposed in this paper. The running results show that the proposed method performs better than the conventional methods.