1. Authors: William Ho and Ping Ji Published Date: April 2009 Presented by: Mark Sydenham

2. References Altinkemer, K., Kazaz, B., Köksalan, M., & Moskowitz, H. (2000). Optimization of printed circuit board manufacturing: integrated modeling and algorithms. European Journal of Operational Research, 124, 409–421. Ball, M. O., & Magazine, M. J. (1988). Sequencing of insertions in printed circuit board assembly. Operations Research, 36, 192–201. Broad, K., Mason, A., Rönnqvist, M., & Frater, M. (1996). Optimal robotic component placement. Journal of the Operational Research Society, 47,1343–1354. Crama, Y., Flippo, O. E., Klundert, J. V. D., & Spieksma, F. C. R. (1997). The assembly of printed circuit boards: A case with multiple machines and multiple board types. European Journal of Operational Research, 98, 457–472. Ellis, K. P., Vittes, F. J., & Kobza, J. E. (2001). Optimizing the performance of a surface mount placement machine. IEEE Transactions on Electronics Packaging Manufacturing, 24, 160–170. Foulds, L. R., & Hamacher, H. W. (1993). Optimal bin location and sequencing in printed circuit board assembly. European Journal of Operational Research, 66,279–290. Francis, R. L., Hamacher, H. W., Lee, C. Y., & Yeralan, S. (1994). Finding placement sequences and bin locations for Cartesian robots. IIE Transactions, 26, 47–59. Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering design. New York: Wiley. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. New York: Addison-Wesley. Ji, Z., Leu, M. C., & Wong, H. (1992). Application of linear assignment model for planning of robotic printed circuit board assembly. Journal of Electronic Packaging, 114, 455– 460. Kumar, R., & Li, H. (1995). Integer programming approach to printed circuit board assembly time optimization. IEEE Transactions on Components, Packaging, and Manufacturing Technology – Part B, 18, 720–727. Leu, M. C., Wong, H., & Ji, Z. (1993). Planning of component placement/insertion sequence and feeder setup in PCB assembly using genetic algorithm. Journal of Electronic Packaging, 115, 424–432. Loh, T. S., Bukkapatnam, S. T. S., Medeiros, D., & Kwon, H. (2001). A genetic algorithm for sequential part assignment for PCB assembly. Computers & Industrial Engineering, 40, 293–307. Magyar, G., Johnsson, M., & Nevalainen, O. (1999). On solving single machine optimization problems in electronics assembly. Journal of Electronics Manufacturing, 9, 249– 267. Ong, N. S., & Khoo, L. P. (1999). Genetic algorithm approach in PCB assembly. Integrated Manufacturing Systems, 10, 256–265. Ong, N. S., & Tan, W. C. (2002). Sequence placement planning for high speed PCB assembly machine. Integrated Manufacturing Systems, 13, 35–46. Osman, I. H., & Kelly, J. P. (1996). Meta-heuristics: Theory & applications. Boston: Kluwer Academic Publishers. Wilhelm, W. E., & Tarmy, P. K. (2003). Circuit card assembly on tandem turret-type placement machines. IIE Transactions, 35, 627–645.

3. Function of Paper  Process planning issues Setup optimization ○ Line assignment ○ Machine grouping ○ PCB grouping ○ PCB sequencing Process optimization ○ Component allocation ○ Feeder arrangement ○ Component sequencing  The purpose of this paper is to integrate the feeder arrangement and component sequencing for sequential pick- and-place (PAP) machines. In other words, optimize these problems simultaneously. By using two methods Mathematical modeling A hybrid genetic algorithm

4. Why is optimizing these problems simultaneously important?  If, for example, the arrangement of components in the feeders is not made carefully and the sequencing is optimized, the over-all system performance can be very poor.  So to maximize performance by minimizing production time, these two problems must be solved simultaneously.

5. Is this paper related to the technical area in the course?  Yes, it is related. In class we have discussed electronics assembly and pick and place machines. And this paper is attempting to optimize this pick and place process.

6. Design of Pick and Place Machines  The machine  The process

7. Design principle or purpose  Minimize the distance the placing head travels, which in turn, reduces the take needed to place all the components

8. Definition of parameters for the mathematical models

9. The different mathematical models formulated M3 (non-linear – contain both binary and integer values) M4 (linear version of M3) M5 (simplified version of M3) M6 (M5 made linear)

10. Experimental equipment for the mathematical models  These equations were optimized by two software packages CPLEX ○ A integer linear programming solver ○ Used to solve M4 BARON ○ A computational system for solving non- convex optimization problems ○ Used to solve M5

11. Results of mathematical models  Mathematical models are too complex and require too much time to solve.

12. The hybrid genetic algorithm method (HGA)  The basic idea of this method is to maintain a population of possible solutions that evolve as the process proceeds

13. Method

14. Method continued

15. Results of the HGA method  Solved in 9 seconds versus 11 hours or 15 days

16. Results Summary  Mathematic Method Very accurate but takes to long to perform  HGA Method Reaches a good, but not perfect, optimization very quickly.

17. Technical advancement?  Authors boast that their results constitute a reduction of about 2.2 seconds in cycle time per PCB.

18. Is this advancement practical for industrial use?  There is the potential for this study to benefit industry but the explanation on how to perform the proposed HGA method is difficult to understand

19. Which industries would benefit from this study?  Manufacturers of PCB  Manufacturers of machines that are used to produce PCB