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Applying modified NSGA-II for bi-objective supply chain problem

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Abstract

This paper minimizes the value of total cost and bullwhip effect in a supply chain. The objectives have been achieved through developing a new multi-objective formulation for minimizing the total cost and minimizing the bullwhip effect of a two-echelon serial supply chain. A new crossover algorithm for a fuzzy variable and a new mutation algorithm have also been proposed while applying Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to the proposed problem. The formulated problem has been simulated by Matlab software and the results of the modified NSGA-II have been compared with those of original NSGA-II. It is found from the results that the modified NSGA-II algorithm performs better than the original NSGA-II algorithm since the minimum values for both total cost and the bullwhip effect are obtained in case of the modified NSGA-II. The formulated bi-objective problem is new to the research community. The minimization of bullwhip effect has never been considered in a multi-objective optimization before. Besides crossover operator applied to the fuzzy variable and the mutation operator are newly introduced operators.

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References

  • Al-e-hashem, S. M. J., Mirzapour, Malekly. H., & Aryanezhad, M. B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International journal of Production Economics (in press) doi:10.1016/j.ijpe.2011.01.027.

  • Amid A., Ghodsypourb S. H., O’Brien C. (2009) A weighted additive fuzzy multiobjective model for the supplier selection problem under price breaks in a supply chain. International Journal of Production Economics 121(2): 323–332

    Article  Google Scholar 

  • Balin S. (2011) Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation. Information Sciences 181(17): 3551–3569

    Article  Google Scholar 

  • Baykasoglu A., Sönmez L., Sönmez A. I. (2004) Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems. Journal of Intelligent Manufacturing 15(6): 777–785

    Article  Google Scholar 

  • Bhattacharya R., Bandyopadhyay S. (2011) A review of the causes of bullwhip effect in a supply chain. International Journal of Advanced Manufacturing Technology 54(9–12): 1245–1261

    Article  Google Scholar 

  • Cavalieri S., Gaiardelli P. (1998) Hybrid genetic algorithms for a multiple-objective scheduling problem. Journal of Intelligent Manufacturing 9(4): 361–367

    Article  Google Scholar 

  • Coello C. A., Lamont G. B., Van Veldhuizen D. A. (2007) Evolutionary algorithms for solving multi-objective problems (2nd ed.). Springer, Berlin

    Google Scholar 

  • Deb K., Pratap A., Agarwal S., Meyarivan T. (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computing 6(2): 182–197

    Article  Google Scholar 

  • Díaz-Madroñero M., Peidro D., Vasant P. (2010) Vendor selection problem by using an interactive fuzzy multi-objective approach with modified S-curve membership functions. Computers and Mathematics with Applications 60(4): 1038–1048

    Article  Google Scholar 

  • Ding, R., Chen, Y., & He, L. (2009). Fuzzy multi-objective programming model for logistics service supplier selection. 2009 Chinese control and decision conference, CCDC 2009, 17–19 June 2009, Guilin, pp. 1527–1531.

  • Ebrahim R. M., Razmi J., Haleh H. (2009) Scatter search algorithm for supplier selection and order lot sizing under multiple price discount environment. Advances in Engineering Software 40(9): 766–776

    Article  Google Scholar 

  • Giarola S., Zamboni A., Bezzo F. (2011) Supply chain design and capacity planning: From first to second generation biofuel systems. Chemical Engineering Transactions 24: 253–258

    Google Scholar 

  • Hung S.-J. (2011) Activity-based divergent supply chain planning for competitive advantage in the risky global environment: A DEMATEL-ANP fuzzy goal programming approach. Expert Systems with Applications 38(8): 9053–9062

    Article  Google Scholar 

  • Ishibuchi, H., Tsukamoto, N., & Nojima, Y. (2009). Empirical analysis of using weighted sum fitness functions in NSGA-II for many-objective 0/1 knapsack problems. UKSim 2009: 11th international conference on computer modelling and simulation (pp. 71–76), Cambridge.

  • Jensen Mikkel T. (2003) Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms. IEEE Transactions on Evolutionary Computation 7(5): 503–515

    Article  Google Scholar 

  • Kumar, A., Sharma, D., & Deb, K. (2007). A hybrid multi-objective optimization procedure using PCX based NSGA-II and sequential quadratic programming. 2007 IEEE congress on evolutionary computation, CEC 2007 (pp. 3011–3018), Singapore.

  • Kuo-Ming L., Ming-Ren H., Jyh-Horng C., Ching-Yi G. (2011) Improved differential evolution approach for optimization of surface grinding process. Expert Systems with Applications 38(5): 5680–5686

    Article  Google Scholar 

  • Lakashminarasimman, N., Baskar, S., Alphones, A., & Iruthayarajan, M. W. (2010). Multiobjective mobile antenna location identification using evolutionary optimization algorithm. 2010 Second international conference on computing, communication and networking technologies. Chettinand, India.

  • Liang C., Guo J., Yang Y. (2011) Multi-objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning. Journal of Intelligent Manufacturing 22(3): 471–479

    Article  Google Scholar 

  • Liu, Y., Zhou, C., & Ye, W. J. (2005). A fast optimization method of using nondominated sorting genetic algorithm (NSGA-ll) and 1-nearest neighbor (1 NN) classifier for numerical model calibration. IEEE international conference on granular computing. Beijing, China.

  • Long, Y., Pan, W., & Haoshen, Z. (2010). A novel diversity preservation strategy based on ranking integration for solving some specific multi-objective problems. 2010 ninth international symposium on distributed computing and applications to business, engineering and science (pp. 97–101), Hong Kong.

  • Mansour, M. R., Santos, A. C., London, J. B., Jr., Delbem, A. C. B., & Bretas, N. G. (2010). Node-depth encoding and evolutionary algorithms applied to service restoration in distribution systems. 2010 IEEE power and energy society general meeting (pp. 1–8), Minneapolis, MN.

  • Maocai, W., Guangming, D., Hanping, H., & Maocai, W. (2010). Improved NSGA-II algorithm for optimization of constrained functions. 2010 international conference on machine vision and human-machine interface (pp. 673–675). TBD Kaifeng, China.

  • Mirakhorli, A., Farahani, M. H., & Ramtin, F. (2009). New approach in supplier selection using linear physical programming. IEEE/INFORMS international conference on service operations, logistics and informatics, SOLI 2009 (pp. 47–51), Chicago, IL, USA.

  • Mirakhorli, A. (2010). Multi-objective optimization of reverse logistics network with fuzzy demand and return-product using an interactive fuzzy goal programming approach. 40th international conference on computers and industrial engineering: Soft computing techniques for advanced manufacturing and service systems, CIE40 2010, Awaji Island, Japan.

  • Murugan P., Kannan S., Baskar S. (2009) Application of NSGA-II algorithm to single-objective transmission constrained generation expansion planning. IEEE Transactions on Power Systems 24(4): 1790–1797

    Article  Google Scholar 

  • Niknam T. (2011) An efficient multi-objective HBMO algorithm for distribution feeder reconfiguration. Expert Systems with Applications 38(3): 2878–2887

    Article  Google Scholar 

  • Niknam T., Meymand Hamed Z., Mojarrad Hasan D. (2011) A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants. Renewable Energy 36(5): 1529–1544

    Article  Google Scholar 

  • Ozkok Beyza A., Tiryaki F. (2011) A compensatory fuzzy approach to multi-objective linear supplier selection problem with multiple-item. Expert Systems with Applications 38(9): 11363–11368

    Article  Google Scholar 

  • Pinto-Varela, T., Barbosa-Póvoa, A. P. F. D., & Novais, A. Q. (2011). Bi-objective optimization approach to the design and planning of supply chains: Economic versus environmental performances. Computers and Chemical Engineering. doi:10.1016/j.compchemeng.2011.03.009 (in press).

  • Sato, M., Aguirre, H. E., & Tanaka, K. (2006). Effects of δ-similar elimination and controlled elitism in the NSGA-II multiobjective evolutionary algorithm. 2006 IEEE congress on evolutionary computation (pp. 1164–1171), Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada.

  • Singh, H. K., Ray, T., Smith, W. (2010) Surrogate assisted simulated annealing (SASA) for constrained multi-objective optimization. IEEE congress on evolutionary computation (pp. 1–8), Barcelona.

  • Wu D., Olson David L. (2008) Supply chain risk, simulation, and vendor selection. International Journal of Production Economics 114(2): 646–655

    Article  Google Scholar 

  • Wu C., Barnes D., Rosenberg D., Luo X. (2009) An analytic network process-mixed integer multi-objective programming model for partner selection in agile supply chains. Production Planning & Control 20(3): 254–275

    Article  Google Scholar 

  • Wu Desheng D., Zhang Y., Wu D., Olson David L. (2010) Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach. European Journal of Operational Research 200(3): 774–787

    Article  Google Scholar 

  • Yücel A., Güneri Ali F. (2011) A weighted additive fuzzy programming approach for multi-criteria supplier selection. Expert Systems with Applications 38(5): 6281–6286

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Production Engineering, Jadavpur University, Raja S. C. Mallick Road, Kolkata, West Bengal, India

    Susmita Bandyopadhyay & Ranjan Bhattacharya

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  1. Susmita Bandyopadhyay
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  2. Ranjan Bhattacharya
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Correspondence to Susmita Bandyopadhyay.

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Bandyopadhyay, S., Bhattacharya, R. Applying modified NSGA-II for bi-objective supply chain problem. J Intell Manuf 24, 707–716 (2013). https://doi.org/10.1007/s10845-011-0617-2

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  • DOI: https://doi.org/10.1007/s10845-011-0617-2

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