Gravitational Search Algorithm Application to Optimize an Aggregate Production Planning Problem
Abstract
Given the need to find alternatives that allow finding various solutions to the problem of aggregate production planning (APP), this research proposes a solution through the gravitational search algorithm (GSA). Constraint management is included and, likewise, an introduction is made to the benchmark functions to which it has been subjected, which allow to show that the algorithm has the ability to find optimal solutions to problems with a high degree of difficulty; Finally, the results of the proposal are shown, demonstrating that this search and optimization algorithm can be applied to solve these types of problems where there are a large number of variables and constrictions.
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References
Abu Bakar, M. R., Bakheet, A. J. K., Kamil, F., Kalaf, B. A., Abbas, I. T., Soon,
L. L., 2016. Enhanced simulated annealing for solving aggregate production
planning. Mathematical Problems in Engineering 2016.
Al-e, S. M. J. M., Aryanezhad, M. B., Sadjadi, S. J., et al., 2012. An eficient
algorithm to solve a multi-objective robust aggregate production planning in
an uncertain environment. The International Journal of Advanced Manufacturing
Technology 58 (5-8), 765–782.
Anand Jayakumar, A., Krishnaraj, C., Nachimuthu, A., 2017. Aggregate production
planning: Mixed strategy. Pak. J. Biotechnol. Vol 14 (3), 487–490.
Bu_a, E., Taubert, W., 1972. Production-inventory systems planning and control.
Tech. rep.
Chaturvedi, N. D., Bandyopadhyay, S., 2015. Targeting aggregate production
planning for an energy supply chain. Industrial & Engineering Chemistry
Research 54 (27), 6941–6949.
Chehouri, A., Younes, R., Perron, J., Ilinca, A., 2016. A constraint-handling
technique for genetic algorithms using a violation factor. arXiv preprint
arXiv:1610.00976.
Cheraghalikhani, A., Khoshalhan, F., Mokhtari, H., 2019. Aggregate production
planning: A literature review and future research directions. International
Journal of Industrial Engineering Computations 10 (2), 309–330.
da Silva, C. G., Figueira, J., Lisboa, J., Barman, S., 2006. An interactive decision
support system for an aggregate production planning model based on
multiple criteria mixed integer linear programming. Omega 34 (2), 167–177.
Eberhart, R., Kennedy, J., 1995. A new optimizer using particle swarm theory.
In: Micro Machine and Human Science, 1995. MHS’95., Proceedings of the
Sixth International Symposium on. IEEE, pp. 39–43.
Farmer, J. D., Packard, N. H., Perelson, A. S., 1986. The immune system, adaptation,
and machine learning. Physica D: Nonlinear Phenomena 22 (1-3),
–204.
Holt, C. C., Modigliani, F., Muth, J. F., 1956. Derivation of a linear decision
rule for production and employment. Management Science 2 (2), 159–177.
Holt, C. C., Modigliani, F., Simon, H. A., 1955. A linear decision rule for
production and employment scheduling. Management Science 2 (1), 1–30.
Ibrahim, A. M., Swief, R. A., 2018. Comparison of modern heuristic algorithms
for loss reduction in power distribution network equipped with renewable
energy resources. Ain Shams Engineering Journal.
Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P., 1983. Optimization by simulated
annealing. science 220 (4598), 671–680.
Liang, J., Runarsson, T. P., Mezura-Montes, E., Clerc, M., Suganthan, P. N.,
Coello, C. C., Deb, K., 2006. Problem definitions and evaluation criteria
for the cec 2006 special session on constrained real-parameter optimization.
Journal of Applied Mechanics 41 (8), 8–31.
Mehdizadeh, E., Niaki, S. T. A., Hemati, M., 2018. A bi-objective aggregate
production planning problem with learning e ect and machine deterioration:
Modeling and solution. Computers & Operations Research 91, 21–36.
Nam, S.-j., Logendran, R., 1992. Aggregate production planning—a survey
of models and methodologies. European Journal of Operational Research
(3), 255–272.
Narimani, M. R., Vahed, A. A., Azizipanah-Abarghooee, R., Javidsharifi, M.,
Enhanced gravitational search algorithm for multi-objective distribution
feeder reconfiguration considering reliability, loss and operational cost.
IET Generation, Transmission & Distribution 8 (1), 55–69.
Paiva, R. P., Morabito, R., 2009. An optimization model for the aggregate production
planning of a brazilian sugar and ethanol milling company. Annals
of Operations Research 169 (1), 117.
Ramezanian, R., Rahmani, D., Barzinpour, F., 2012. An aggregate production
planning model for two phase production systems: Solving with genetic
algorithm and tabu search. Expert Systems with Applications 39 (1), 1256–
Rashedi, E., 2011. Gravitational search algorithm (gsa).
urlhttps://la.mathworks.com/matlabcentral/fileexchange/27756-
gravitational-search-algorithm-gsa.
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S., 2009. Gsa: a gravitational
search algorithm. Information sciences 179 (13), 2232–2248.
Yadav, A., Deep, K., 2013. Constrained optimization using gravitational search
algorithm. National Academy Science Letters 36 (5), 527–534.
Zhang, R., Zhang, L., Xiao, Y., Kaku, I., 2012. The activity-based aggregate
production planning with capacity expansion in manufacturing systems.
Computers & Industrial Engineering 62 (2), 491–503.