Optimization of integrated supply chain network problem using hybrid genetic algorithm approach

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    In this paper, an integrated supply chain network (ISCN) problem is designed. The ISCN problem is composed of forward and reverse logistics and represented by a nonlinear mixed integer programming (NMIP). The objective of the ISCN problem is to maximize the total profit which is consisted of total revenues and total costs resulting from its implementation. A hybrid genetic algorithm (HGA) approach proposed in this paper is applied to solve the NMIP. In numerical experiment, five scales of the ISCN problem are presented and they are solved using the proposed HGA approach and some conventional approaches. Experimental results show that the proposed HGA approach outperforms the others.


  • Keywords


    Integrated supply chain network problem, hybrid genetic algorithm, forward logistics, reverse logistics, nonlinear mixed integer programming.

  • References


      [1] Georgiadis P & Besiou M, “Sustainability in electrical and electronic equipment closed-loop supply chains: A System Dynamics approach”, Journal of Cleaner Production, Vol.16, pp.1665-1678, (2008).

      [2] Wang HF & Hsu HW, “A closed-loop logistic model with a spanning-tree based genetic algorithm”, Computers & Operations Research, Vol.37, pp.376-389, (2010).

      [3] Amin SH & Zhang G, “An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach”, Expert Systems with Applications, Vol.39, pp.6782-6791, (2012).

      [4] Amin SH & Zhang G, “A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return”, Applied Mathematical Modelling, Vol.37, pp.4165-4176, (2013).

      [5] Chen TT, Chan FTS & Chung SH, “An integrated closed-loop supply chain model with location allocation problem and product recycling decisions”, International Journal of Production Research, Vol.53, No.10, pp.3120–3140, (2015).

      [6] Holland JH, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT Press, (1992).

      [7] Cheong F and Lai R, “Constraining the optimization of a fuzzy logic controller using an enhanced genetic algorithm”, IEEE Transaction Systems, Man, and Cybernetics-Part B: Cybernetics, Vol.30, No.1, pp.31-46, (2000).

      [8] Gen M & Cheng R, Genetic algorithms and engineering design, John Wiley & Son, New York, (1997).

      [9] Gen M & Cheng R, Genetic algorithms and engineering optimization, John-Wiley & Sons, New York, (2000).

      [10] Mak KL, Wong YS & Wang XX, “An adaptive genetic algorithm for manufacturing cell formation”, International Journal of Manufacturing Technology, Vol.16, pp.491-497, (2000).

      [11] Holden NP & Freitas AA, “A hybrid PSO/ACO algorithm for classification”, Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO), (2007).

      [12] Surendar, A., Rani, N.U.”High speed data searching algorithms for DNA searching”,(2016) International Journal of Pharma and Bio Sciences, 2016 (Special Issue), pp. 73-77.

      [13] Settles M & Soule T, “Breeding swarms:a GA/PSO hybrid”, Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation (GECCO), (2005).

      [14] Yun YS, “Genetic algorithm with fuzzy logic controller for preemptive and non-preemptive job shop scheduling problems”, Computers & Industrial Engineering, Vol.43, No.3, pp.623-644, (2002).

      [15] Shi XH, Liang YC, Lee HP, Lu C & Wang LM, “An improved GA and a novel PSO-GA-based hybrid algorithm”, Information Processing Letters, Vol.93, pp. 255-261, (2005).

      [16] Kanagaraj G, Ponnambalam SG & Jawahar N, “A Hybrid cuckoo search and genetic algorithm for reliability-redundancy allocation problems”, Computers & Industrial Engineering, Vol.66, No.4, pp.1115-1125, (2013).

      [17] Surendar, A., Arun, M.”Efficient DNA sequence analysis for reduced gene selection using frequency analysis”, (2016) Journal of Chemical and Pharmaceutical Sciences, 9 (4), pp. 3367-3373.

      [18] Surendar, A., George, A.”A real-time searching and sequencing assembly platform based on an FPGA implementation for Bioinformatics applications”,(2016) International Journal of Pharma and Bio Sciences, 7 (4), pp. B642-B647.


 

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Article ID: 8905
 
DOI: 10.14419/ijet.v7i1.1.8905




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