Tavakkoli-Moghaddam, R., Safaei, N. (2006). Solving a generalized aggregate production planning problem by genetic algorithms. Journal of Industrial Engineering, International, 2(2), 53-64.
R Tavakkoli-Moghaddam; N Safaei. "Solving a generalized aggregate production planning problem by genetic algorithms". Journal of Industrial Engineering, International, 2, 2, 2006, 53-64.
Tavakkoli-Moghaddam, R., Safaei, N. (2006). 'Solving a generalized aggregate production planning problem by genetic algorithms', Journal of Industrial Engineering, International, 2(2), pp. 53-64.
Tavakkoli-Moghaddam, R., Safaei, N. Solving a generalized aggregate production planning problem by genetic algorithms. Journal of Industrial Engineering, International, 2006; 2(2): 53-64.
Solving a generalized aggregate production planning problem by genetic algorithms
1Associate Professor, Department of Industrial Engineering , Faculty of Engineering, University of Tehran, Iran
2Research Scholar, Department of Industrial Engineering Iran University Science and Technology, Tehran, Iran
Abstract
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.
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