Fuzzy goal programming model and algorithm of low carbon closed loop logistics network

被引：0
作者：
Li B. [1 ]
Zhao G. [1 ]
Zhang X. [1 ]
机构：
[1] College of Transport & Communications, Shanghai Maritime University, Shanghai
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 08期
关键词：
Carbon emission; Closed loop logistics; Fuzzy goal programming; Genetic algorithms; Network planning;
D O I：
10.13196/j.cims.2019.08.023
中图分类号：
学科分类号：
摘要：
To solve the design of low-carbon closed-loop logistics network in fuzzy environment, by considering the problem of fuzziness, carbon emissions and response capability in the network, a fuzzy linear programming model of closed-loop logistics network was established for the decision of facilities combination the location and transportation routes between nodes. It used fuzzy goal programming method with different importance and priority to minimize the logistics cost and carbon emissions and maximize response ability. According to the characteristics of the problem, the code based on route coding genetic algorithm was written in MATLAB. By using the genetic algorithm and CPLEX, the several numerical examples' results were compared and analyzed, and the result verified the feasibility of the proposed genetic algorithm. The case verified the fuzzy goal programming model could solve the conflict betwee three objectives, and the analysis indicated the effectiveness and practicability of decision. © 2019, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：2087 / 2100
页数：13
相关论文
共 20 条
  • [1] Centobelli P., Cerchione R., Esposito E., Environmental sustainability in the service industry of transportation and logistics service providers: systematic literature review and research directions, Transportation Research Part D Transport & Environment, 53, pp. 454-470, (2017)
  • [2] Talaei M., Moghaddam B.F., Pishvaee M.S., Et al., 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, pp. 662-673, (2016)
  • [3] Gao J., Wang R., Wang H., Closed-loop supply chain network design under carbon subsidies, Computer Integrated Manufacturing Systems, 21, 11, pp. 3033-3040, (2015)
  • [4] Rahmani D., Mahoodian V., Strategic and operational supply chain network design to reduce carbon emission considering reliability and robustness, Journal of Cleaner Production, 149, 15, pp. 607-620, (2017)
  • [5] Accorsi R., Cholette S., Manzini R., Et al., The land-network problem: ecosystem carbon balance in planning sustainable agro-food supply chains, Journal of Cleaner Production, 112, 20, pp. 158-171, (2016)
  • [6] Williams B.D., Roh J., Tokar T., Et al., Leveraging supply chain visibility for responsiveness: the moderating role of internal integration, Journal of Operations Management, 31, 7-8, pp. 543-554, (2013)
  • [7] Roh J., Hong P., Min H., Implementation of a responsive supply chain strategy in global complexity: the case of manufacturing firms, International Journal of Production Economics, 147, 1, pp. 198-210, (2014)
  • [8] Fattahi M., Govindan K., Keyvanshokooh E., Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers, Transportation Research Part E Logistics & Transportation Review, 101, pp. 176-200, (2017)
  • [9] Govindan K., Fattahi M., Keyvanshokooh E., Supply chain network design under uncertainty: a comprehensive review and future research directions, European Journal of Operational Research, 263, 1, pp. 108-141, (2017)
  • [10] Soleimani H., Kannan G., A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks, Applied Mathematical Modelling, 39, 14, pp. 3990-4012, (2015)
12