Fuzzy optimization of multi-objective job shop scheduling based on inventory information

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.