solving a generalized aggregate production planning problem by genetic algorithms

this paper presents a genetic algorithm (ga) for solving a generalized model of single-item resource-constrained aggregate production planning (app) with linear cost functions. app belongs to a class of pro-duction planning problems in which there is a single production variable representing the total production of all products. we linearize a linear mixed-integer model of app subject to hiring/firing of workforce, avail-able regular/over time, and inventory/shortage/subcontracting allowable level where the total demand must fully be satisfied at end of the horizon planning. due to np-hard class of app, the real-world sized problems cannot optimality be solved within a reasonable time. in this paper, we develop the proposed genetic algo-rithm with effective operators for solving the proposed model with an integer representation. this model is optimally solved and validated in small-sized problems by an optimization software package, in which the obtained results are compared with ga results. the results imply the efficiency of the proposed ga achiev-ing to near optimal solutions within a reasonably computational time.