An Adaptive Genetic Algorithm for Cost Optimization of Multi-Stage Supply Chain
Keywords:
Genetic Algorithms, Supply Chain, Cost Optimization,Abstract
The distribution process of finished product is one of the important processes in the company. To maximize the profit, companies need to optimize the distribution network process by minimizing the costs. Genetic algorithm is proposed to find a solution of the problem. In this study, Genetic Algorithm use a one-cut-point (OCP) crossover, swap mutation, and elitism for the selection. Results obtained using GAs in this study is better than those achieved by baseline algorithm. This indicated that OCP crossover, swap mutation, and elitism selection are effective. To increase the quality of the solution, an adaptive genetic algorithm is proposed. The advantage of the adaptive process is producing more different chromosomes. Thus, the adaptive solution gives more efficient solution in minimizing the cost compared to the traditional genetic algorithms.References
M. A. M. Ali and Y. H. Sik, “Transportation problem: A special case for linear programing problems in mining engineering,” Int. J. Min. Sci. Technol., vol. 22, no. 3, pp. 371–377, 2012.
K. A. A. D. Raj and C. Rajendran, “A genetic algorithm for solving the fixed-charge transportation model: Two-stage problem,” Comput. Oper. Res., vol. 39, no. 9, pp. 2016–2032, 2012.
F. Ghassemi Tari and Z. Hashemi, “A priority based genetic algorithm for nonlinear transportation costs problems,” Comput. Ind. Eng., vol. 96, pp. 86–95, 2016.
R. R. P. Langroodi and M. Amiri, “A system dynamics modeling approach for a multi-level, multi-product, multi-region supply chain under demand uncertainty,” Expert Syst. Appl., vol. 51, pp. 231–244, 2016.
R. Masson, N. Lahrichi, and L. M. Rousseau, “A two-stage solution method for the annual dairy transportation problem,” Eur. J. Oper. Res., vol. 251, no. 1, pp. 36–43, 2016.
Z. Indra and Subanar, “Optimasi Biaya Distribusi Rantai Pasok Tiga Tingkat dengan Menggunakan Algoritma Genetika Adaptif dan Terdistribusi (Optimization of Supply Chain Distribution Costs Three Levels Using Adaptive Genetic Algorithm and Distributed),” IJCCS, vol. 8, no. 2, pp. 189–200, 2014.
L. Bertazzi, A. Bosco, and D. Laganà, “Min–Max exact and heuristic policies for a two-echelon supply chain with inventory and transportation procurement decisions,” Transp. Res. Part E Logist. Transp. Rev., vol. 93, pp. 57–70, 2016.
A. A. Hlayel and M. A. Alia, “Solving Transportation Problems Using the Best Candidates Method,” Comput. Sci. Eng. An Int. J., vol. 2, no. 5, pp. 23–30, 2012.
P. Sitek and J. Wikarek, “Mathematical programming model of cost optimization for supply chain from perspective of logistics provider,” Manag. Prod. Eng. Rev., vol. 3, no. 2, pp. 49–61, 2012.
Z.-H. Che, T.-A. Chiang, and Y.-C. Kuo, “Multi-Echelon Reverse Supply Chain Network Design With Specified Returns Using Particle Swarm Optimization,” Int. J. Innov. Comput. Inf. Control, vol. 8, no. 10(A), pp. 6719–6731, 2012.
A. Király, T. Varga, and J. Abonyi, “Constrained Particle Swarm Optimization of Supply Chains,” vol. 6, no. 7, pp. 1113–1121, 2012.
A. Rahmi, M. Z. Sarwani, and W. F. Mahmudy, “Genetic Algorithms for Optimization of Multi-Level Product Distribution,” Accept. to Int. J. Intell. Eng. Syst., 2016.
W. Neungmatcha, K. Sethanan, M. Gen, and S. Theerakulpisut, “Adaptive genetic algorithm for solving sugarcane loading stations with multi-facility services problem,” Comput. Electron. Agric., vol. 98, pp. 85–99, 2013.
A. H. Karami and M. Hasanzadeh, “An adaptive genetic algorithm for robot motion planning in 2D complex environments,” Comput. Electr. Eng., vol. 43, pp. 317–329, 2015.
İ. Küçükoğlu and N. Öztürk, “Simulated Annealing Approach for Transportation Problem of Cross-docking Network Design,” Procedia - Soc. Behav. Sci., vol. 109, no. 2012, pp. 1180–1184, 2014.
Z. Qiongbing, “A New Crossover Mechanism for Genetic Algorithms with Variable-length Chromosomes for Path Optimization Problems,” Expert Syst. Appl., 2016.
M. Thakur and A. Kumar, “Electrical Power and Energy Systems Optimal coordination of directional over current relays using a modified real coded genetic algorithm : A comparative study,” Int. J. Electr. POWER ENERGY Syst., vol. 82, pp. 484–495, 2016.
H. Guo, X. Wang, and S. Zhou, “A Transportation Problem with Uncertain Costs and Random Supplies,” Int. J. e-Navigation Marit. Econ., vol. 2, pp. 1–11, 2015.
W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Hybrid Genetic Algorithms for Multi-Period Part Type Selection and Machine Loading Problems in Flexible Manufacturing System,” in IEEE International Conference on Computational Intelligence and Cybernetics, 2013, vol. 8, no. 1, pp. 126–130.
I. Azim and F. Rahman, “Genetic Algorithm Based Reactive Power Management by SVC,” Int. J. Electr. Comput. Eng., vol. 4, no. 2, 2014.
S. Sivasankar, S. Nair, and M. V. Judy, “Feature Reduction in Clinical Data Classification using Augmented Genetic Algorithm,” Int. J. Electr. Comput. Eng., vol. 5, no. 6, pp. 1516–1524, 2015.
Z.-Q. Chen and R.-L. Wang, “Solving the m-way graph partitioning problem using a genetic algorithm,” IEEJ Trans. Electr. Electron. Eng., vol. 6, no. 5, pp. 483–489, 2011.
R. Jiao, Z. Yang, R. Shi, and B. Lin, “A Multistage Multiobjective Substation Siting and Sizing Model Based on Operator-Repair Genetic Algorithm,” IEEJ Trans. Electr. Electron. Eng., vol. 9, pp. S28--S36, 2014.
W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part I: Modelling,” Adv. Mater. Res., vol. 701, pp. 359–363, 2013.
Z. Li, J. Gu, H. Zhuang, L. Kang, X. Zhao, and Q. Guo, “Adaptive molecular docking method based on information entropy genetic algorithm,” Appl. Soft Comput., vol. 26, pp. 299–302, 2015.
J. Magalhães-Mendes, “A comparative study of crossover operators for genetic algorithms to solve the job shop scheduling problem,” WSEAS Trans. Comput., vol. 12, no. 4, pp. 164–173, 2013.
Y. C. Wong, N. M. Mahmod, M. M. Ibrahim, A. R. Syafeeza, and N. A. Hamid, “Adaptive Impedance Tuning Network using Genetic Algorithm : ITuneGA,” J. Telecommun. Electron. Comput. Eng., vol. 8, no. 5, pp. 55–60, 2016.
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