In uncertain environment, it is very difficult to optimize both cost and performance in complex reverse logistics network. This paper develops an integrated strategy to solve the cost optimization problem in reverse logistics network. First, the integrated scheme is based on the fuzzy AHP, where the cost coefficient and the demand quantities are modeled as fuzzy numbers to measure different uncertain factors. Second, the linear programming is introduced for cost optimization to calculate the operational objective function of the reverse logistics network. Third, some experiments are made to verify the proposed model. According to different uncertain factors, the optimal cost strategy can be constructed for uncertain use demand. Last, some interesting conclusions are drawn on the proposed method for decision makers to optimize the cost of the reverse logistics network, and future work direction is also provided.
Published in | International Journal of Economics, Finance and Management Sciences (Volume 5, Issue 1) |
DOI | 10.11648/j.ijefm.20170501.13 |
Page(s) | 24-33 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Reverse Logistics Network, Cost Optimization, Fuzzy AHP, Linear Programming
[1] | Jung, KS; Dawande, M; Geismar, HN; Guide, VDR; Sriskandarajah, C, Supply planning models for a remanufacturer under just-in-time manufacturing environment with reverse logistics, Annals of Operations Research, 240 (2016), 533-581. |
[2] | Huang, CC; Liang, WY; Tseng, TL; Chen, PH, The rough set based approach to generic routing problems: case of reverse logistics supplier selection, Journal of Intelligent Manufacturing, 27 (2016), 781-795. |
[3] | Cannella, S; Bruccoleri, M; Framinan, JM, Closed-loop supply chains: What reverse logistics factors influence performance? International Journal of Production Economics, 175 (2016), 35-49. |
[4] | Li, S; Wang, NM; Jia, T; He, ZW; Liang, HG, Multi-objective Optimization for Multi-period Reverse Logistics Network Design, IEEE Transactions on Engineering Management, 63 (2016), 223-236. |
[5] | Tavana, M; Zareinejad, M; Di Caprio, D; Kaviani, MA, An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics, Applied Soft Computing, 40 (2016), 544-557. |
[6] | Demirel, E; Demirel, N; Gokcen, H, A mixed integer linear programming model to optimize reverse logistics activities of end-of-life vehicles in Turkey, Journal of Cleaner Productioyuchi, 112 (2016), 2101-2113. |
[7] | Djikanovic, J; Vujosevic, M, A new integrated forward and reverse logistics model: A case study, International Journal of Computational Intelligence Systems, 9 (2016), 25-35. |
[8] | Ayvaz, B; Bolat, B; Aydin, N, Stochastic reverse logistics network design for waste of electrical and electronic equipment, Resources Conservation and Recycling, 104 (2015), 391-404. |
[9] | Lee, JE; Chung, KY; Lee, KD; Gen, M, A multi-objective hybrid genetic algorithm to minimize the total cost and delivery tardiness in a reverse logistics, Multimedia Tools and Applications, 74 (2015), 9067-9085. |
[10] | Moghaddam, KS, Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty, Expert Systems with Applications, 42 (2015), 6237-6254. |
[11] | Yanik, S, Reverse logistics network design under the rsk of hazardous materials transportation, Human and Ecological Risk Assessment, 21 (2015), 1277-1298. |
[12] | Zhou, XG; Zhou, YH, Designing a multi-echelon reverse logistics operation and network: A case study of office paper in Beijing, Resources Conservation and Recycling, 100 (2015), 58-69. |
[13] | Ferri, GL; Chaves, GDD; Ribeiro, GM, Reverse logistics network for municipal solid waste management: The inclusion of waste pickers as a Brazilian legal requirement, Waste Management, 40 (2015), 173-191. |
[14] | Choudhary, A; Sarkar, S; Settur, S; Tiwari, MK, A carbon market sensitive optimization model for integrated forward-reverse logistics, International Journal of Production Economics, 164 (2015), 433-444. |
[15] | Kilic, HS; Cebeci, U; Ayhan, MB, Reverse logistics system design for the waste of electrical and electronic equipment (WEEE) in Turkey, Resources Conservation and Recycling, 95 (2015), 120-132. |
[16] | Ozkan, B; Basligil, H; Kaya, I; Ozkir, V, A fuzzy mixed integer linear programming model for a reverse logistics system with a real case application, Journal of Multiple-valued Logic and Soft Computing, 25 (2~3) 2015, 269-289. |
[17] | Acar, AZ; Onden, I; Kara, K, Evaluating the location of regional return centers in reverse logistics through integration of gis, ahp and integer programming, International Journal of Industrial Engineering-theory Applications and Practice, 22 (4) 2015, 399-411. |
[18] | Silva, ALE; Moraes, JAR; Machado, EL, Proposal for cleaner production oriented practices ecodesign and reverse logistics, Engenharia Sanitaria E Ambiental, 20 (1) 2015, 29-46. |
[19] | Hsueh, JT; Lin, CY, Constructing a network model to rank the optimal strategy for implementing the sorting process in reverse logistics: case study of photovoltaic industry, Clean Technologies and Environmental Policy, 17 (1) 2015, 155-174. |
[20] | Kim, JS; Lee, DH, An integrated approach for collection network design, capacity planning and vehicle routing in reverse logistics, Journal of The Operational Research Society, 66 (1) 2015, 76-85. |
[21] | Alumur, SA; Tari, I, Collection center location with equity considerations in reverse logistics network, Infor, 52 (4) 2014, 157-173. |
[22] | Eskandarpour, M; Masehian, E; Soltani, R; Khosrojerdi, A, A reverse logistics network for recovery systems and a robust metaheuristic solution approach, International Journal of Advanced Manufacturing Technology, 74 (9-12) 2014, 1393-1406. |
[23] | Ramos, TRP; Gomes, MI; Barbosa, Povoa, AP, Planning a sustainable reverse logistics system: Balancing costs with environmental and social concerns, Omega-international Journal of Management Science, 48 (2014), 60-74. |
[24] | Niknejad, A; Petrovic, D, Optimization of integrated reverse logistics networks with different product recovery routes, European Journal of Operational Research, 238 (1) 2014, 143-154. |
[25] | Roghanian, E; Pazhoheshfar, P, An optimization model for reverse logistics network under stochastic environment by using genetic algorithm, Journal of Manufacturing Systems, 33 (3) 2014, 348-356. |
[26] | Liu, DW, Network site optimization of reverse logistics for Ecommerce based on genetic algorithm, Neural Computing & Applications, 25 (1) 2014, 67-71. |
[27] | Subulan, K; Baykasoglu, A; Saltabas, A, An improved decoding procedure and seeker optimization algorithm for reverse logistics network design problem, Journal of Intelligent & Fuzzy Systems, 27 (6) 2014, 2703-2714. |
APA Style
Yunzhi Ma, Liyun Zhang, Xianglin Lv, Zhengying Cai. (2016). An Integrated Strategy for Cost Optimization of Reverse Logistics Network Under Uncertain Environment. International Journal of Economics, Finance and Management Sciences, 5(1), 24-33. https://doi.org/10.11648/j.ijefm.20170501.13
ACS Style
Yunzhi Ma; Liyun Zhang; Xianglin Lv; Zhengying Cai. An Integrated Strategy for Cost Optimization of Reverse Logistics Network Under Uncertain Environment. Int. J. Econ. Finance Manag. Sci. 2016, 5(1), 24-33. doi: 10.11648/j.ijefm.20170501.13
AMA Style
Yunzhi Ma, Liyun Zhang, Xianglin Lv, Zhengying Cai. An Integrated Strategy for Cost Optimization of Reverse Logistics Network Under Uncertain Environment. Int J Econ Finance Manag Sci. 2016;5(1):24-33. doi: 10.11648/j.ijefm.20170501.13
@article{10.11648/j.ijefm.20170501.13, author = {Yunzhi Ma and Liyun Zhang and Xianglin Lv and Zhengying Cai}, title = {An Integrated Strategy for Cost Optimization of Reverse Logistics Network Under Uncertain Environment}, journal = {International Journal of Economics, Finance and Management Sciences}, volume = {5}, number = {1}, pages = {24-33}, doi = {10.11648/j.ijefm.20170501.13}, url = {https://doi.org/10.11648/j.ijefm.20170501.13}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20170501.13}, abstract = {In uncertain environment, it is very difficult to optimize both cost and performance in complex reverse logistics network. This paper develops an integrated strategy to solve the cost optimization problem in reverse logistics network. First, the integrated scheme is based on the fuzzy AHP, where the cost coefficient and the demand quantities are modeled as fuzzy numbers to measure different uncertain factors. Second, the linear programming is introduced for cost optimization to calculate the operational objective function of the reverse logistics network. Third, some experiments are made to verify the proposed model. According to different uncertain factors, the optimal cost strategy can be constructed for uncertain use demand. Last, some interesting conclusions are drawn on the proposed method for decision makers to optimize the cost of the reverse logistics network, and future work direction is also provided.}, year = {2016} }
TY - JOUR T1 - An Integrated Strategy for Cost Optimization of Reverse Logistics Network Under Uncertain Environment AU - Yunzhi Ma AU - Liyun Zhang AU - Xianglin Lv AU - Zhengying Cai Y1 - 2016/12/29 PY - 2016 N1 - https://doi.org/10.11648/j.ijefm.20170501.13 DO - 10.11648/j.ijefm.20170501.13 T2 - International Journal of Economics, Finance and Management Sciences JF - International Journal of Economics, Finance and Management Sciences JO - International Journal of Economics, Finance and Management Sciences SP - 24 EP - 33 PB - Science Publishing Group SN - 2326-9561 UR - https://doi.org/10.11648/j.ijefm.20170501.13 AB - In uncertain environment, it is very difficult to optimize both cost and performance in complex reverse logistics network. This paper develops an integrated strategy to solve the cost optimization problem in reverse logistics network. First, the integrated scheme is based on the fuzzy AHP, where the cost coefficient and the demand quantities are modeled as fuzzy numbers to measure different uncertain factors. Second, the linear programming is introduced for cost optimization to calculate the operational objective function of the reverse logistics network. Third, some experiments are made to verify the proposed model. According to different uncertain factors, the optimal cost strategy can be constructed for uncertain use demand. Last, some interesting conclusions are drawn on the proposed method for decision makers to optimize the cost of the reverse logistics network, and future work direction is also provided. VL - 5 IS - 1 ER -