Abstract:
Facility layout is one of the most important problems for modem manufacturing
systems. Facility layout plays a key rolc for companies, and it is an inseparable part
of the manufacturing system design process. A good solution for the facility layout
problem contribute, to the overall efficiency of operatIons. Traditionally there are
two approaches to the facility layout problem. One Is the quantitative approach
aiming at minimIzing tbe total materIal handlIng cost and the other one is qualitative
approach aiming at maximizing closeness rating score. Various researcbes have
been performed on these two approaches separately. In lhis paper both approaches to
tbe facility layout problem havc been taken into consideration separately.
Subsequently, the research also solved the problem combining tbese two
approaches, at the objective function level. In this research work first the genetic
algorithm (GA) was developed for the multi-objcctive facility layout problem and
found out tbe optimal tacility location for a particular problem considering the two
objectiveS; i.e. minimIzing the material handling cost and maximizing the closeness
raling score. Then another local search technique, Simulated Annealing (SA)
algorithm, was developed to compare the result found in GA. In GA primarily an
initial population was created and by tbe crossover operator and mulation process
new offspring was generated and if the offspring meet the stopping criteria the result
was seleeted for the process. In simulated annealing firslly an initial temperature
was selected and then going through the SA process and meeting the stopping
criteria the final result was generated. By the two proposed approaches a non
domInated soiuti()Il set is found (Pareto optimal) approximately for the multi
objective facility layout problem. A Problem of 3x3 matrix was te,ted using both
genetic algoritbm and simulated annealing approach. By changing different
parameter" of genetic algorithm and simulated annealing the caSe study was
examined. From the simulation result it is found that SImulated Annealing always
gives betlcr rc~ult lhan the (,enetic Algorithm. In majority of the cases Simulated
Annealing lind the department sequence that incurs minimum material handling cost
and that d~partment sequence als" maximizes the closeness rating score than the
department sequence of Genetic Algorithm. And finally it was found that the SA
requires Ie,s computational time tban the GA. So it can be stated that tor finding tbe
optimum facility layout the simulated annealing is the best algoritbm for this
particular lype of problem.
