Abstract:
Job shop scheduling problems are onc of the oldest combinatorial optimization
problems has becn studied, In real world situation the problem adds different
parameters than the classical one. Most of the real world problems are fuzzy in natw'e.
Besides, there are multiple objectives that should be taken into consideration. In ajob
shop environment the allocation of available resourccs is critical. In this research,
fuzzy processing times of operations and fuzzy due dates of jobs are considered to
incorpor:rte fuzziness in the problem, Inventory consumption and profrt earned for a
palticular order plays an impOitant role in job shop cases. There are some orders that
consumes a large share of available inventOlYresulting less profit. Again profit is
rc1atedto volumes of orders Percentage of inventory consumption and profit eamed
fonn the orders are also considered in this FMOJSSP. Mamdani based Fuzzy
Inference System is used to calculate the job weights based on the percentage of
inventory consumption for a particular job and profit can be earned from the jobs, To
calculate the FIS based weights of the jobs MATLAB 7.0 was used. Average
weighted tardiness, number of tardy jobs, total flow time and idle times of machincs
are considercd as objectives which should be minimizcd. Satisfaction grade technique
is used to aggrcgate the objectives to a single objective ftInction value. In this research
Genetic algorithm is used as a heuristic technique with specially encoded
chromosomes that denotes the complete schedule of the jobs. The single objective
function vallle was considered as he fitness function value for GA. Elitism is ensured
in each generation through the selection mechanism. A local search technique,
Simulated annealing is also used to compare the results obtained in two different
methods. Different problem sizes has bcen tested and the fitness function values and
computation times of the problems for each method was compared.
