Manufacturer Order Fulfillment Based on Multi-Objective Optimization NSGA II Model

Abstract:

The aim of this paper is to study the production and delivery decision problem in the Manufacturer Order Fulfillment. Owing to the order fulfillment optimization condition of the manufacturer, the multi-objective optimization model of manufacturers' production and delivery has been founded. The solution of the multi-objective optimization model is also very difficult. Fast and Elitist Non-dominated Sorting Genetic Algorithm (NSGA II) have been applied successfully to various test and real-world optimization problems. These population based the algorithm provide a diverse set of non-dominated solutions. The obtained non-dominated set is close to the true Pareto-optimal front. But its convergence to the true Pareto-optimal front is not guaranteed. Hence SBX is used as a local search procedure. The proposed procedure is successfully applied to a special case. The results validate that the algorithm is effective to the multi-objective optimization model.

• Periodical: Applied Mechanics and Materials (Volume 340)
• Pages: 136-140
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.340.136
• Online since: July 2013
• Authors: Liang You Shu, Ling Xiao Yang
• Keywords: Elitist Non-Dominated Sorting Genetic Algorithm (NSGA II), Fast Non-Dominated Sorting Genetic Algorithm (NSGA II), Management Engineering, Multi-Objective Optimization Model, SBX

References

[1] Turan Paksoy, Nimet Yapici Pehliv. 2012. A Fuzzy Linear Programming Model for the Optimization of Multi-stage Supply Chain Networks with Triangular and Trapezoidal Membership Functions. Journal of the Franklin Institute 349(1), 93–109.

DOI: 10.1016/j.jfranklin.2011.10.006

[2] Hany Osman, Kudret Demirli. 2010. A Bilinear Goal Programming Model and a Modified Benders Decomposition Algorithm for Supply Chain Reconfiguration and Supplier Selection. Int. J. Production Economics 124 (1), 97–105.

DOI: 10.1016/j.ijpe.2009.10.012

[3] Nicola Costantino, Mariagrazia Dotoli, Marco Falagario, et al. 2012. A Model for Supply Management of Agile Manufacturing Supply Chains. Int. J. Production Economics 135 (1), 451–457.

DOI: 10.1016/j.ijpe.2011.08.021

[4] S.M.J. Mirzapour Al-e-hashem, H.Malekly, M.B. Aryanezhad. 2011. A Multi-objective Robust Optimization Model for Multi-product Multi-site Aggregate Production Planning in a Supply Chain under Uncertainty. Int. J. Production Economics 134 (1), 28–42.

DOI: 10.1016/j.ijpe.2011.01.027

[5] Leopoldo EduardoCa rdenas-Barron, Jinn-TsairTeng, GerardoTrevin o-Garza. 2012. An Improved Algorithm and Solution on an Integrated Production-inventory Model in a Three-layer Supply Chain. Int. J. Production Economics 136 (2), 384–388.

DOI: 10.1016/j.ijpe.2011.12.013

[6] SHU Liangyou, Yang Lingxiao.2009. Order Fulfillment Optimization Model of Manufactures Based on Time Competition. In: International Conference of Management Science and Information System part 3,393-396.

[7] YANG Lingxiao, SHU Liangyou.2011. Application of Particle swarm optimization in the Decision-Making of Manufacturers Production and Delivery, In: Electrical, Information Engineering and Mechatronics Vol.1, 83-89.

DOI: 10.1007/978-1-4471-2467-2_10

[8] S. Dhanalakshmi, S. Kannan, K. Mahadevan, S. Baskar. 2011. Application of modified NSGA-II algorithm to Combined Economic and Emission Dispatch problem. Electrical Power and Energy Systems 33 (4), 992–1002.

DOI: 10.1016/j.ijepes.2011.01.014

[9] K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182–197, 2002.

DOI: 10.1109/4235.996017

[10] K. Deb and R. B. Agrawal. 1995. Simulated binary crossover for continuous search space. Complex Systems 9(2), 115–148.

[11] K. Deb and M. Goyal. 1996. A combined genetic adaptive search (GeneAS) for engineering design. Computer Science and Informatics 26(4), 30–45.