Abstract
Cold chain logistics has become one of the main sources of carbon emissions. Meanwhile, the implementation of low-carbon economy has become an inevitable way to promote sustainable development. However, previous studies on the cold chain inventory routing problem (IRP) paid less attention to the cost of carbon emissions. In this paper, a linear programming (LP) model is established, which takes the costs of vehicle transportation, time window and carbon emission into consideration. Although the simple LP model is easy to be solved, it cannot handle the problems with uncertainty. Therefore, in order to overcome the influence of uncertainty, the proposed LP model is developed into three low-carbon robust optimization (RO) models. In addition, this paper takes a cold chain logistics enterprise in Yangtze River Delta as an example for empirical analysis. The results of the case study prove that the RO models can quickly solve the problems with uncertainty and still maintain robustness, while the LP model has failed. Specifically, the R-ellipsoid model produces the best result among the three RO models. It is suggested that when the carbon emission tax increases, the decision makers tend to choose a better path planning scheme, which will not only reduce the total cost, but also obtain environmental benefits. Finally, the findings of this paper generate some implications for the low-carbon transformation of cold chain logistics enterprises.
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References
Bai, Y., & Liu, Y. (2013). An exploration of residents low-carbon awareness and behavior in Tianjin. China, Energy Policy, 61, 1261–1270.
Bang, G., Victor, D. G., & Andresen, S. (2017). California’s cap-and-trade system: Diffusion and lessons. Global Environmental Politics, 17(3), 12–30.
Bell, W., Dalberto, L., Fisher, M., & Greenfield, A. (1983). Improving the distribution of industrial gases with an on-line computerized routing and scheduling optimizer. Interfaces, 13(6), 4–23.
Ben-Tal, A., & Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations Research Letters, 25(1), 1–13.
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.
Boyd, S., & Vandendenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.
Ceyhun, A., Hasan, S., & Irem, O. (2007). A fuzzy multi-objective covering-based vehicle location model for emergency services. Computers & Operations Research, 34(3), 705–726.
Dan, Z., Yao, J., & Yang, S. (2015). Online risk assessment and coordinated decision scheme for emergency load shedding control. Automation of Power Systems, 39(20), 66–71.
Fakhrzad, M. B., & Goodarzian, F. (2019). A fuzzy multi-objective programming approach to develop a green closed-loop supply chain network design problem under uncertainty: Modifications of imperialist competitive algorithm. RAIRO-Operations Research, 53(3), 963–990.
Fathollahi-Fard, A., Ahmadi, A., Goodarzian, F., & Cheikhrouhou, N. (2020). A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment. Applied Soft Computing, 93, 106385. https://doi.org/10.1016/j.asoc.2020.106385.
Goldberg, J. (2004). Operations research models for the deployment of emergency services vehicles. EMS Management Journal, 1(1), 20–39.
Goodarzian, F., & Hosseini-Nasab, H. (2019). Applying a fuzzy multi-objective model for a production–distribution network design problem by using a novel self-adoptive evolutionary algorithm. International Journal of Systems Science: Operations and Logistics. https://doi.org/10.1080/23302674.2019.1607621.
Goodarzian, F., Hosseini-Nasab, H., Muuzuri, J., & Fakhrzad, M. B. (2020). A multi-objective pharmaceutical supply chain network based on a robust fuzzy model: A comparison of meta-heuristics. Applied Soft Computing, 92, 106331. https://doi.org/10.1016/j.asoc.2020.106331.
Haddadsisakht, A., & Ryan, , S. M. (2018). Closed-loop supply chain network design with multiple transportation modes under stochastic demand and uncertain carbon tax. International Journal of Production Economics, 195, 118–131.
Harewood, S. (2002). Emergency ambulance deployment in Barbados: A multi-objective approach. Journal of Operational Research Society, 53(2), 185–192.
Huang, R., Qu, S., & Liu, Z. (2019). Multi-stage distributionally robust optimization with risk aversion. Journal of Industrial and Management Optimization, 10(6), 34–39.
James, S. J., & James, C. (2010). The food cold-chain and climate change. Food Research International, 43(7), 1944–1956.
Jiang, J., Xie, D., Ye, B., Shen, B., & Chen, Z. (2016). Research on China’s cap-and-trade carbon emission trading scheme: Overview and outlook. Applied Energy, 178, 902–917.
Ji, Y., Du, J., Han, X., Wu, X., Huang, R., Wang, S., & Liu, Z. (2020a). A mixed integer robust programming model for two-echelon inventory routing problem of perishable products. Physica A: Statistical Mechanics and its Applications, 548, 124481. https://doi.org/10.1016/j.physa.2020.124481.
Ji, Y., Qu, S., Wu, Z., & Liu, Z. (2020b). A fuzzy-robust weighted approach for multicriteria bilevel games. IEEE Transactions on Industrial Informatics, 16(8), 5369–5376.
Kai, K., Yujie, Z., Jing, Z., & Qiang, C. (2019). Evolutionary game theoretic analysis on low-carbon strategy for supply chain enterprises. Journal of Cleaner Production, 230, 981–994.
Lin, B., & Jia, Z. (2018). Impact of quota decline scheme of emission trading in China: A dynamic recursive CGE model. Energy, 149, 190–203.
Lin, Q., Zhao, Q., & Benjamin, L. (2020). Cold chain transportation decision in the vaccine supply chain. European Journal of Operational Research, 283(1), 182–195.
Lu, S., Jiang, H., Liu, H., & Huang, S. (2017). Regional disparities and influencing factors of Average CO\(_2\) emissions from transportation industry in Yangtze River economic belt. Transportation Research Part D: Transport and Environment, 57, 112–123.
Maher, R. A., Mohammed, I. A., & Ibraheem, I. K. (2014). Polynomial based Hobust governor for load frequency control in steam turbine power systems. International Journal of Electrical Power & Energy Systems, 57, 311–317.
Marrasso, E., Roselli, C., & Sasso, M. (2019). Electric efficiency indicators and carbon dioxide emission factors for power generation by fossil and renewable energy sources on hourly basis. Energy Conversion and Management, 196, 1369–1384.
Min, Y., & Anna, N. (2013). Competitive food supply chain networks with application to fresh produce. European Journal of Operational Research, 224(1), 273–282.
Moncer, H., Rami, A., & Abdulrahim, S. (2017). Integrated economic and environmental models for a multi stage cold supply chain under carbon tax regulation. Journal of Cleaner Production, 166, 1357–137.
Philippe, A., Antoine, D., David, H., Ralf, M., & John, V. (2016). Carbon taxes, path dependency, and directed technical change: Evidence from the auto industry. Journal of Political Economy, 124(1), 1–51.
Qu, S., Zhou, Y., Zhang, Y., Wahab, M. I. M., Zhang, G., & Ye, Y. (2019). Optimal strategy for a green supply chain considering shipping policy and default risk. Computers & Industrial Engineering, 131, 172–186.
Sahebjamnia, N., Goodarzian, F., & Hajiaghaei-Keshteli, M. (2020). Optimization of multi-period three-echelon citrus supply chain problem. Journal of Optimization in Industrial Engineering, 13(1), 39–53.
Samir, E., & Ryan, M. (2012). Green supply chain network design to reduce carbon emissions. Transportation Research Part D: Transport and Environment, 17(5), 370–379.
Tang, S., Wang, W., Yan, H., & Hao, G. (2015). Low-carbon logistics: Reducing shipment frequency to cut carbon emissions. International Journal of Production Economics, 164, 339–350.
Tirkolaee, E. B., Hadian, S., Gerhard-Wilhelm, W., & Mahdavi, I. (2019). A robust green traffic-based routing problem for perishable products distribution. Computational Intelligence, 102, 340–350.
Wang, K., & Wang, N. (2011). A protein inspired RNA genetic algorithm for parameter estimation in hydrocracking of heavy oil. Chemical Engineering Journal, 167(1), 228–239.
Wang, Y., Li, Q., Chang, M., Chen, H., & Zang, G. (2012). Research on fault diagnosis expert system based on the neural network and the fault tree technology. Procedia Engineering, 31, 1206–1210.
Yang, H., & Chen, W. (2018). Retailer-driven carbon emission abatement with consumer environmental awareness and carbon tax: Revenue-sharing versus cost-sharing. Omega, 78, 179–191.
Ye, Y., & Wang, J. (2013). Study of logistics network optimization model considering carbon emissions. International Journal of System Assurance Engineering and Management., 30(10), 1–7.
Zhang, J., & Zhang, Y. (2018). Carbon tax, tourism CO\(_2\) emissions and economic welfare. Annals of Tourism Research, 69, 18–30.
Zhang, L., Wang, Y., Fei, T., & Ren, H. (2014). The research on low-carbon logistics routing optimization based on DNA-ant colony algorithm. Discrete Dynamics in Nature and Society, 1, 1–13.
Zhang, L., Ming-Lang, T., Ching-Hsin, W., Chao, X., & Teng, F. (2019a). Low-carbon cold chain logistics using ribonucleic acid-ant colony optimization algorithm. Journal of Cleaner Production, 233, 169–180.
Zhang, S., Chen, N., Song, X., & Yang, J. (2019b). Optimizing decision-making of regional cold chain logistics system in view of low-carbon economy. Transportation Research Part A: Policy and Practice, 130, 844–857.
Zhang, Y., & Da, Y. (2015). The decomposition of energy-related carbon emission and its decoupling with economic growth in China. Renewable and Sustainable Energy Reviews, 41, 1255–1266.
Zhang, Z., & Hai, J. (2014). A robust counterpart approach to the bi-objective emergency medical service design problem. Applied Mathematical Modelling, 38, 1033–1040.
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Acknowledgements The work is supported by the National Fund Support Project on Social Science of China (No. 17BGL083). We are very grateful to the editors and referees for their careful reading and constructive suggestions on the manuscript.
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Ji, Y., Du, J., Wu, X. et al. Robust optimization approach to two-echelon agricultural cold chain logistics considering carbon emission and stochastic demand. Environ Dev Sustain 23, 13731–13754 (2021). https://doi.org/10.1007/s10668-021-01236-z
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DOI: https://doi.org/10.1007/s10668-021-01236-z