APPLYING SPEA2 TO SOLVE MULTI-OBJECTIVE MANUFACTURING PROBLEM WITH DEMAND LEAKAGE

Susmita Bandyopadhyay

Abstract


The focus of this paper is a manufacturing system producing two grades of a product. The manufacturing system produces defective items which are reworked and sold in the secondary market. The price of grade I product is greater than that for grade II product. The demand leakage is also being considered for the product of grade I. The demand leakage here means that the some of the customers who are willing to pay for product of grade I, are actually paying for product of grade II. The unsold units for both the grades are sold at giveaway price. Both the demand and sale are assumed to be fuzzy in nature. The objective of this research work is to study the behavior of the system under demand leakage and other conditions. A multi-objective problem has been proposed for the purpose and Strength Pareto Evolutionary Algorithm 2 (SPEA2) has been applied to study the behavior of the proposed problem. The variations of the objective values have been observed and analyzed. A numerical example shows the applicability of the proposed solution methodology

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References


M. Zhang, P. C. Bell, G. Cai, and X. Chen, ”Optimal fences and joint price and inventory decisions in distinct markets with demand leakage,” European Journal of Operational Research, vol. 204, pp. 589-596, 2010.

S. A. Raza, and M. Turiac, “Joint optimal determination of process mean, production quantity, pricing, and market segmentation with demand leakage,” European Journal of Operational Research, vol. 249(1), pp. 312 – 326, 2016.

S. A. Raza, F. C. Abdullakutty, and S. Rathinam, “Joint determination of process mean, price differentiation, and production decisions with demand leakage: A multi-objective approach,” Applied Mathematical Modelling, vol. 40(19-20), pp. 8446-8463, 2016.

Y. Tao, K. Meng, P. Lou, X. Peng, and X. Qian, “Joint decision-making on automated disassembly system scheme selection and recovery route assignment using multi-objective meta-heuristic algorithm,” International Journal of Production Research, vol. pp. 1-19, 2018.

M. Zhang, and P. Bell, P., “Price fencing in practice of revenue management: An overview and taxonomy,” Journal of Revenue and Pricing Management, vol. 11(2), pp. 146-159, 2012.

S. A. Raza, “An integrated approach to price di_erentiation and inventory decisions with demand leakage,” International Journal of Production Economics, vol. 164, pp. 105 – 117, 2015.

F. Y. Chen, H. Yan, and Y. Yao, “A newsvendor pricing game,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 34(4), pp. 450-456, 2004.

K. T. Talluri, and G. V. Ryzin, “The Theory and Practice of Revenue Management,” Kluwer Academic Publishers, 2004.

V. Polotski, J.-P. Kenné, and A. Gharbi, “Production control of hybrid manufacturing - remanufacturing systems under demand and return variations,” International Journal of Production Research,” pp. 1-24, 2018.

A. Jeang, “Optimal determination of the process means, process tolerances, and resetting cycle for process planning under process shifting,’ Journal of Manufacturing Systems, vol. 28(4), pp. 98-106, 2009.

C. Li, Q. Su, and M. Xie, “Economic modelling for statistical process control subject to a general quality deterioration,” International Journal of Production Research, vol. 54(6), pp. 1753-1770, 2016.

M. Rahim, and F. Tuffaha, “Integrated model for determining the optimal initial settings of the process mean and the optimal production run assuming quadratic loss functions,” International Journal of Production Research, vol. 42(16), pp. 3281-3300, 2004.

M. A. Hariga, and M. Al-Fawzan, “Joint determination of target value and production run for a process with multiple markets,” International Journal of Production Economics, vol. 96(2), pp. 201-212, 2005.

E. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: improving the strength pareto evolutionary algorithm”, In: Giannakoglou K., Tsahalis D., Periaux J., Papailou P., Fogarty T. (editors). EUROGEN Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Barcelona, Spain: CIMNE, 2001, pp. 95–100, Athens, Greece.


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