An Application of Genetic Simulation
Approach to Layout Problem in Robot
Arm Assembly Factory
Speaker： Ho, Zih-Ping
Advisor： Perng, Chyuan
Industrial Engineering and Enterprise
Information, Tunghai University, Taiwan.
IFORS at Hilton Hawaii Village, Honolulu, Hawaii.
July 15, 2005.
Overview
1. Introduction
2. Literature Review
3. Mathematical Formula
4. GA Approach
5. Conclusion and Suggestion
Introduction (1)
 The clean room space is expensive.
 The robot arm equipment orders change
over per three months.
 It will move up and down in a semi-circle
radius in 3D.
 To make the maximum utilization of the
clean room space.
 The radius of robot arm movable areas is
triple operations area than robot arm itself.
Introduction (2)
 This research tries to apply a GA approach
for dynamic layout problem.
 When we finish a group of robot arm
assemble, we will release the space of
floor to the next robot arm.
 It will compare the results of free spaces,
occupied spaces, rotational spaces, due
date and minimum processing time.
Literature Review (1)
1. Azadivar and Wang (2000) used GA to
optimize facility layout problems.
2. Balakrishnan and Cheng (2000)
proposed GA for dynamic layout problem.
In their research, they used strings to
represent one entire layout plan.
Azadivar,F and J.Wang (2000) Facility layout optimization using simulation and genetic
algorithms, Int.J.Prod.Res., 38(17):4369-4383.
Balakrishnan,J. and C.H.Cheng (2000) Genetic search and the dynamic layout problem,
Computers and Operations Res., 27:587-593.
Literature Review (2)
3.Balakrishnan et al. (2003) illustrated that the
dynamic plant layout problem (DPLP) deals
with the design of multi-period layout plans.
4.Li et al. (2003) used GA to solve the robust
layout problem.
Balakrishnan,J., C.H.Cheng, D.G.Conway and C.M.Lau (2003) A hybrid genetic
algorithm for the dynamic plant layout problem, Int.J.Production Economics, 86:107120.
Li,S.G., Z.M.Wu and X.H.Pang (2003) Machine robust facility layout problem in the dynamic and
flexible production environments, J. Shanghai Jiaotong, 37(5):762-765,769.
Literature Review (3)
5.Yang and Peters (1998) A robust machine
layout design problem is an NP-complete
problem, most researchers use heuristic
approaches.
6.Perng and Ho (2004) used database technique
to help the company to solve the orders due
date problem.
Yang,T. and B.A.Peters (1998) Flexible machine layout design for dynamic and uncertain production
environments, European J. Operational Research, 108:49-64.
Perng,C. and Z.P.Ho (2004) Applying information technique to layout on semi-conductor
equipments factory, The Third Conference on Innovation and Technology Management on Taiwan,
Industrial Technology Research Institute on Taiwan, Xin-Zhu City, Taiwan, Sep.11, P.114.
Mathematical Formula
k
 Min Z =  Sj Aj Pj Oj Rj-1
 where j is the j th robot arm,
 k are the total number of robot arms.
 s.t. Sj, Aj, Pj, Oj, Rj-1 > 0
 It is the minimum sum of the free spaces,
j 1
due date, processing time, occupied spaces
and inverse of rotational spaces.
Property of Robot Arm
 The robot arm dynamic layout problem involves the


operational area.
The robot arm movable areas is triple than itself.
The set of robot arm is fixed to the floor.
GA approach (1)
Solving the dynamic layout model is a NP-hard problem.
A conventional optimization method is to reduce total flow
cost (TFC).
In the beginning, we separate the layout into a lot of grids.
We choose one grid as the initial operation, and add the
representation structure by stochastic operation.
We will stop the structure until that there are no robot arm
needed to assemble.
A chromosome contains some operations and the length
of a chromosome is a dynamic value which is determined
by the robot arm jobs.
GA approach (2)
Selection strategy is concerned with choosing
chromosomes from the population spaces. It may create
a new population for the next generation based on either
parent and offspring, or part of them. For evaluating the
fitness and reaching the objective, we calculate the
summation of S.A.P.O.R-1. as a fitness function.
Due to create the next generation, crossover and
mutation are methods of trying to find a global optimal.
We set the default value is that mutation rate is 80%,
crossover rate is 50% and each of generation is 1000
times.
Feasible Solution Generation
Procedure: A feasible solution generation
Input: processing time, due date, length and width of robot arms, base set
areas of robot arm, free spaces;
While ( i < k ) do
If there are no free spaces, then the sequence is finished: Stop.
Else
According to the due date, put the operation with the processing time. The
array will remember those due dates, processing time and certain grids.
If moveable area touch on the other base set areas of robot arm, then give
up this operation.
If when the operation is completed the processing and it goes within the due
date, then we put a new time variable to represent it’s real processing time.
If one job is finished, then to release the occupied and rotational spaces.
Calculate free spaces;
End while.
Evaluate the S.A.P.O.R-1.;
End procedure.
System Main Output
System Validation- Different Due Date
Conclusion and Suggestion
 Layout on robot arm assembly factory is




important to them.
Visual Basic 6 as a tool to draw the 2D pictures
We take the Borland C++ as a tool to compile
the software to implement the GA approach.
Software would raise the utilities of the free
space in factory and it take charge of production
management progress effectively. Lower down
the burden on factory personnel.
Suggest that combines BOM and ERP software