solving a fuzzy multi-objective aggregate production planning model with learning and deterioration effects by using genetic and tabu search algorithms

in this paper a non linear integrated fuzzy multi-objective production planning model with the labor learning and machines deterioration effects is presented. the objective function consists of two quantitative objectives namely increase profits and reduces the cost of system failure and a qualitative objective namely increases the satisfaction rate of the customers. different weights for objectives and modification of the objectives by using fuzzy goal programming method are considered to convert the fuzzy multi-objective model to a deterministic single-objective model and  the obtained model is solved by genetic algorithm and tabu search algorithm. finally, the solution obtained from two algorithms compared together by using hypothesis test of equality of means. experimental results show the proposed genetic algorithm for solving the model has higher performance than the tabu search algorithm.