Browse > Article
http://dx.doi.org/10.9723/jksiis.2021.26.1.055

Reinforcing Reverse Logistics Activities in Closed-loop Supply Chain Model: Hybrid Genetic Algorithm Approach  

Yun, YoungSu (조선대학교 경상대학 경영학부)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.26, no.1, 2021 , pp. 55-65 More about this Journal
Abstract
In this paper, a methodology for reinforcing reverse logistics (RL) activities in a closed-loop supply chain (CLSC) model is proposed. For the methodology, the activities of the recovery center (RC) which can be considered as one of the facilities in the RL are reinforced. By the reinforced activities in the RC, the recovered parts and products after checking and recovering processes of the returned product from customer can be reused in the forward logistics (FL) of the CLSC model. A mathematical formulation is suggested for representing the CLSC model with reinforced RL activities, and implemented using a hybrid genetic algorithm (HGA) approach. In numerical experiment, two different scales of the CLSC model are presented and the performance of the HGA approach is compared with those of some conventional approaches. The experimental results show that the former outperforms the latter in most of performance measures. The robustness of the CLSC model is also proved by regulating various rates of the recovered parts and products in the RC.
Keywords
closed-loop supply chain model; reverse logistics; recovery center; hybrid genetic algorithm approach;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Gen, M., and Cheng, R. (1997). Genetic algorithms and engineering design, John Wiley & Son, New York, NY, USA.
2 Gen, M., and Cheng, R. (2000). Genetic algorithms and engineering optimization, John-Wiley & Sons, New York, NY, USA
3 Gen, M., Lin, L., Yun, Y. S., and Inoue, H. (2018). Recent advances in hybrid priority-based genetic algorithms for logistics and SCM network design, Computers and Industrial Engineering, 115, 394-412.
4 Kanagaraj, G., Ponnambalam, S. G., and Jawahar, N. (2013). A hybrid cuckoo search and genetic algorithm for reliability-redundancy allocation problems, Computers and Industrial Engineering, 66, 1115-1125.   DOI
5 Min, H., Ko, C. S., and Ko, H. J. (2006). The spatial and temporal consolidation of returned products in a closed-loop supply chain network, Computers and Industrial Engineering, 51, 309-320.   DOI
6 Ozceylan, E., Demirel, N., Cetinkaya, C., and Demirel, E. (2017). A closed-loop supply chain network design for automotive industry in Turkey, Computers and Industrial Engineering, 113, 729-745.
7 Chen, X., Chuluunsukh, A., Yun, Y. S., and Gen, M. (2019). Optimization of closed-loop supply chain model using hybrid genetic algorithm approach for tire industry in Korea, Xu, J., Cooke, F. L., Gen, M. and Ahmed, S. E. (Eds.), Proceedings of the Twelfth International Conference on Management Science and Engineering Management, Springer, pp. 1593-1612.
8 Chen, Y. T., Chan, F. T. S., and Chung, S. H. (2015). An integrated closed-loop supply chain model with location and allocation problem and product recycling decisions, International Journal of Production Research, 53, 3120-3140.   DOI
9 Savaskan, R. C., Bhattacharya, S., and Van Wassenhove, L. V. (2004). Closed-loop supply chain models with product remanufacturing, Management Science, 50, 239-252.   DOI
10 Paksoy, T., Bektas, T., and Ozceylan, E. (2011). Operational and environmental performance measures in a multiproduct closed-loop supply chain, Transportation Research Part E, 47, 532-546.   DOI
11 Soleimani, H., and Kannan, G. (2015). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks, Applied Mathematical Modelling, 39(14), 3990-4012.   DOI
12 Talaei, M., Moghaddam, B. F., Pishvaee, M. S., Bozorgi-Amiri, A., and Gholamnejad, S. A. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry, Journal of Cleaner Production, 113, 662-673.   DOI
13 Wang, H. F., and Hsu, H. W. (2010). A closed-loop logistic model with a spanning tree based genetic algorithm, Computers and Operations Research, 37(2), 376-389.   DOI
14 Xinyu, L., and Liang, G. (2016). An effective hybrid genetic algorithm and Tabu search for flexible job shop scheduling problem, International Journal of Production Economics, 174, 93-110.   DOI
15 Yun, Y. S. (2006). Hybrid genetic algorithm with adaptive local search scheme, Computers and Industrial Engineering, 51, 128-141.   DOI
16 Yun, Y. S. (2018). Supply chain network design considering environmental factor and transportation types, Journal of the Korea Industrial Information Systems Research, 23(5), 33-41.   DOI
17 Yun, Y. S. (2020). Sustainable closed-loop supply chain model for mobile phone: Hybrid genetic algorithm approach, Journal of the Korea Industrial Information Systems Research, 25(2), 115-127.   DOI
18 Yun, Y. S., Chuluunsukh, A., and Xing, C. (2017). Adaptive hybrid genetic algorithm approach for optimizing closed-loop supply chain model, Journal of the Korea Industrial Information Systems Research, 22(2), 79-90.   DOI
19 Yun, Y. S., Gen, M., and Hwang, R. K. (2012). Adaptive genetic algorithm to multi-stage reverse logistics network design for product resale, Information: An International Interdisciplinary Journal, 15, 6117-6138.
20 Yun, Y. S., Chuluunsukh, A., Gen, M. (2020). Sustainable closed-loop supply chain design problem: a hybrid genetic algorithm approach, Mathematics, 8(1), 84, doi.org/10.3390/math8010084   DOI
21 Yun, Y. S., Chung, H. S., and Moon, C. U. (2013). Hybrid genetic algorithm approach for precedence-constrained sequencing problem, Computers and Industrial Engineering, 65, 137-147.   DOI