Genetic Algorithm Application in a Dynamic PCB Assembly with Carryover Sequence- Dependent Setups
We consider a typical problem in the assembly of
printed circuit boards (PCBs) in a two-machine flow shop system to
simultaneously minimize the weighted sum of weighted tardiness and
weighted flow time. The investigated problem is a group scheduling
problem in which PCBs are assembled in groups and the interest is to
find the best sequence of groups as well as the boards within each
group to minimize the objective function value. The type of setup
operation between any two board groups is characterized as carryover
sequence-dependent setup time, which exactly matches with the real
application of this problem. As a technical constraint, all of the
boards must be kitted before the assembly operation starts (kitting
operation) and by kitting staff. The main idea developed in this paper
is to completely eliminate the role of kitting staff by assigning the
task of kitting to the machine operator during the time he is idle
which is referred to as integration of internal (machine) and external
(kitting) setup times. Performing the kitting operation, which is a
preparation process of the next set of boards while the other boards
are currently being assembled, results in the boards to continuously
enter the system or have dynamic arrival times. Consequently, a
dynamic PCB assembly system is introduced for the first time in the
assembly of PCBs, which also has characteristics similar to that of
just-in-time manufacturing. The problem investigated is
computationally very complex, meaning that finding the optimal
solutions especially when the problem size gets larger is impossible.
Thus, a heuristic based on Genetic Algorithm (GA) is employed. An
example problem on the application of the GA developed is
demonstrated and also numerical results of applying the GA on
solving several instances are provided.
[1] J.E. Schaller, "A new lower bound for the flow shop group scheduling
problem," Computers and Industrial Engineering, vol. 41, pp. 151-161,
2001.
[2] S.W. Choi, and Y.D. Kim, "Minimizing total tardiness on a twomachine
re-entrant flowshop," European Journal of Operational
Research, vol. 199, pp. 375-384, 2009.
[3] V.A. Strusevic, "Group technology approach to the open shop
scheduling problem with batch setup times," Operations Research
Letters, vol. 26, pp. 181-192, 2000.
[4] D.H. Eom, H.J. Shin, I.H. Kwun, J.K. Shim, and S.S. Kim, "Scheduling
jobs on parallel machines with sequence-dependent family setup times,"
International Journal of Advanced Manufacturing Technology, vol. 19,
pp. 926-932, 2002.
[5] J.E. Schaller, J.N.D. Gupta, and A.J. Vakharia, "Scheduling a flowline
manufacturing cell with sequence dependent family setup times,"
European Journal of Operational Research, vol. 125, pp. 324-339,
2000.
[6] Y.D. Kim, H.G. Lim, and M.W. Park, "Search heuristics for a flowshop
scheduling problem in a printed circuit board assembly process,"
European Journal of Operational Research, vol. 91, pp. 124-143, 1996.
[7] L.F. McGinnis, J.C. Ammons, M. Carlyle, L. Cranmer, G.W. Depuy,
K.P. Ellis, C.A. Tovey, and H. Xu, "Automated process planning for
printed circuit card assembly," IIE Transactions, vol. 24, pp. 18-30,
1992.
[8] C.S. Tang, and E.V. Denardo, "Models arising from a flexible
manufacturing machine, part 1: Minimization of the number of tool
switches," Operations Research, vol. 36, pp. 767-777, 1988.
[9] I.O. Yilmaz, and H.O. G├╝nther, "A group setup strategy for PCB
assembly on a single automated placement machine," Operations
Research Proceedings, Bremen, 2005, pp.143-148.
[10] V.J. Leon, and I.J. Jeong, "An improved group setup strategy for PCB
assembly," International Conference on Computational Science and its
Applications, Singapore, 2005, pp. 312-321.
[11] N. Van Hop, and N.N. Nagarur, "The scheduling problem of PCBs for
multiple non-identical parallel machines," European Journal of
Operational Research, vol. 158, pp. 577-594, 2004.
[12] J. Ashayeri, and W. Selen, " A planning and scheduling model for
onsertion in printed circuit board assembly," European Journal of
Operational Research, vol. 183, pp. 909-925, 2007.
[13] C.A. Gelogullari, and R. Logendran, "Group-scheduling problems in
electronics manufacturing," Journal of Scheduling, vol. 13, pp. 177-202,
2010.
[14] J.A. Holland, Adaptation in natural and artificial systems, University of
Michigan, Ann Arbor, 1975.
[15] K.A. De Jong, Analysis of the behavior of a class of genetic adaptive
systems, Doctoral Dissertation, University of Michigan, USA, 1975.
[16] D.E. Goldberg, Genetic algorithms in search, optimization and machine
learning, Massachusetts: Wesley, 1989.
[17] V.A. Cicirello, "Non-wrapping order crossover: An order preserving
crossover operator that respects absolute position," Proceedings of
Genetic and Evolutionary Computation Conference, GECCO, USA,
2006, pp. 1125-1131.
[18] O. Abdoun, and J. Abouchabaka, "A comparative study of adaptive
crossover operators for genetic algorithms to resolve the traveling
salesman problem," International Journal of Computer Applications,
vol. 31, pp. 49-57, 2011.
[19] S.S. Joshi, Phadnis, K. Srihari, and R. Seeniraj, "Use of simulation to
improve the kitting process at an EMS provider's facility," Computers
and Industrial Engineering Conference, Singapore, 2002.
[20] V. Pandya, and R. Logendran, "Weighted tardniess minimization in
flexible flow shops," Proceedings (CD-ROM), 19th Annual Industrial
Engineering Research Conference, Cancun, Mexico, 2010.
[21] M.T. Yazdani Sabouni, and R. Logendran, "Bicriteria carryover
sequence-dependent group scheduling in PCB manufacturing,"
Proceedings (CD-ROM), 20th Annual Industrial Engineering Research
Conference (IERC), Reno, NV, USA, 2011.
[22] ILOG CPLEX. IBM, Version 12.2, 2010.
[1] J.E. Schaller, "A new lower bound for the flow shop group scheduling
problem," Computers and Industrial Engineering, vol. 41, pp. 151-161,
2001.
[2] S.W. Choi, and Y.D. Kim, "Minimizing total tardiness on a twomachine
re-entrant flowshop," European Journal of Operational
Research, vol. 199, pp. 375-384, 2009.
[3] V.A. Strusevic, "Group technology approach to the open shop
scheduling problem with batch setup times," Operations Research
Letters, vol. 26, pp. 181-192, 2000.
[4] D.H. Eom, H.J. Shin, I.H. Kwun, J.K. Shim, and S.S. Kim, "Scheduling
jobs on parallel machines with sequence-dependent family setup times,"
International Journal of Advanced Manufacturing Technology, vol. 19,
pp. 926-932, 2002.
[5] J.E. Schaller, J.N.D. Gupta, and A.J. Vakharia, "Scheduling a flowline
manufacturing cell with sequence dependent family setup times,"
European Journal of Operational Research, vol. 125, pp. 324-339,
2000.
[6] Y.D. Kim, H.G. Lim, and M.W. Park, "Search heuristics for a flowshop
scheduling problem in a printed circuit board assembly process,"
European Journal of Operational Research, vol. 91, pp. 124-143, 1996.
[7] L.F. McGinnis, J.C. Ammons, M. Carlyle, L. Cranmer, G.W. Depuy,
K.P. Ellis, C.A. Tovey, and H. Xu, "Automated process planning for
printed circuit card assembly," IIE Transactions, vol. 24, pp. 18-30,
1992.
[8] C.S. Tang, and E.V. Denardo, "Models arising from a flexible
manufacturing machine, part 1: Minimization of the number of tool
switches," Operations Research, vol. 36, pp. 767-777, 1988.
[9] I.O. Yilmaz, and H.O. G├╝nther, "A group setup strategy for PCB
assembly on a single automated placement machine," Operations
Research Proceedings, Bremen, 2005, pp.143-148.
[10] V.J. Leon, and I.J. Jeong, "An improved group setup strategy for PCB
assembly," International Conference on Computational Science and its
Applications, Singapore, 2005, pp. 312-321.
[11] N. Van Hop, and N.N. Nagarur, "The scheduling problem of PCBs for
multiple non-identical parallel machines," European Journal of
Operational Research, vol. 158, pp. 577-594, 2004.
[12] J. Ashayeri, and W. Selen, " A planning and scheduling model for
onsertion in printed circuit board assembly," European Journal of
Operational Research, vol. 183, pp. 909-925, 2007.
[13] C.A. Gelogullari, and R. Logendran, "Group-scheduling problems in
electronics manufacturing," Journal of Scheduling, vol. 13, pp. 177-202,
2010.
[14] J.A. Holland, Adaptation in natural and artificial systems, University of
Michigan, Ann Arbor, 1975.
[15] K.A. De Jong, Analysis of the behavior of a class of genetic adaptive
systems, Doctoral Dissertation, University of Michigan, USA, 1975.
[16] D.E. Goldberg, Genetic algorithms in search, optimization and machine
learning, Massachusetts: Wesley, 1989.
[17] V.A. Cicirello, "Non-wrapping order crossover: An order preserving
crossover operator that respects absolute position," Proceedings of
Genetic and Evolutionary Computation Conference, GECCO, USA,
2006, pp. 1125-1131.
[18] O. Abdoun, and J. Abouchabaka, "A comparative study of adaptive
crossover operators for genetic algorithms to resolve the traveling
salesman problem," International Journal of Computer Applications,
vol. 31, pp. 49-57, 2011.
[19] S.S. Joshi, Phadnis, K. Srihari, and R. Seeniraj, "Use of simulation to
improve the kitting process at an EMS provider's facility," Computers
and Industrial Engineering Conference, Singapore, 2002.
[20] V. Pandya, and R. Logendran, "Weighted tardniess minimization in
flexible flow shops," Proceedings (CD-ROM), 19th Annual Industrial
Engineering Research Conference, Cancun, Mexico, 2010.
[21] M.T. Yazdani Sabouni, and R. Logendran, "Bicriteria carryover
sequence-dependent group scheduling in PCB manufacturing,"
Proceedings (CD-ROM), 20th Annual Industrial Engineering Research
Conference (IERC), Reno, NV, USA, 2011.
[22] ILOG CPLEX. IBM, Version 12.2, 2010.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:58291", author = "M. T. Yazdani Sabouni and Rasaratnam Logendran", title = "Genetic Algorithm Application in a Dynamic PCB Assembly with Carryover Sequence- Dependent Setups", abstract = "We consider a typical problem in the assembly of
printed circuit boards (PCBs) in a two-machine flow shop system to
simultaneously minimize the weighted sum of weighted tardiness and
weighted flow time. The investigated problem is a group scheduling
problem in which PCBs are assembled in groups and the interest is to
find the best sequence of groups as well as the boards within each
group to minimize the objective function value. The type of setup
operation between any two board groups is characterized as carryover
sequence-dependent setup time, which exactly matches with the real
application of this problem. As a technical constraint, all of the
boards must be kitted before the assembly operation starts (kitting
operation) and by kitting staff. The main idea developed in this paper
is to completely eliminate the role of kitting staff by assigning the
task of kitting to the machine operator during the time he is idle
which is referred to as integration of internal (machine) and external
(kitting) setup times. Performing the kitting operation, which is a
preparation process of the next set of boards while the other boards
are currently being assembled, results in the boards to continuously
enter the system or have dynamic arrival times. Consequently, a
dynamic PCB assembly system is introduced for the first time in the
assembly of PCBs, which also has characteristics similar to that of
just-in-time manufacturing. The problem investigated is
computationally very complex, meaning that finding the optimal
solutions especially when the problem size gets larger is impossible.
Thus, a heuristic based on Genetic Algorithm (GA) is employed. An
example problem on the application of the GA developed is
demonstrated and also numerical results of applying the GA on
solving several instances are provided.", keywords = "Genetic algorithm, Dynamic PCB assembly, Carryover sequence-dependent setup times, Multi-objective.", volume = "7", number = "4", pages = "598-9", }