IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i4p576-d206511.html
   My bibliography  Save this article

A Vehicle Routing Optimization Problem for Cold Chain Logistics Considering Customer Satisfaction and Carbon Emissions

Author

Listed:
  • Gaoyuan Qin

    (College of Mechanical Engineering, Chongqing University, Chongqing 400044, China)

  • Fengming Tao

    (College of Mechanical Engineering, Chongqing University, Chongqing 400044, China)

  • Lixia Li

    (College of Mechanical Engineering, Chongqing University, Chongqing 400044, China)

Abstract

Under fierce market competition and the demand for low-carbon economy, cold chain logistics companies have to pay attention to customer satisfaction and carbon emissions for better development. In order to simultaneously consider cost, customer satisfaction, and carbon emissions in the cold chain logistics path optimization problem, based on the idea of cost–benefit, this paper proposes a comprehensive cold chain vehicle routing problem optimization model with the objective function of minimizing the cost of unit satisfied customer. For customer satisfaction, this paper uses the punctuality of delivery as the evaluation standard. For carbon emissions, this paper introduces the carbon trading mechanism to calculate carbon emissions costs. An actual case data is used with a cycle evolutionary genetic algorithm to carry out computational experiments in the model. First, the effectiveness of the algorithm and model were verified by a numerical comparison experiment. The optimization results of the model show that increasing the total cost by a small amount can greatly improve average customer satisfaction, thereby obtaining a highly cost-effective solution. Second, the impact of carbon price on total costs, carbon emissions, and average customer satisfaction have also been numerically analyzed. The experimental results show that as carbon price increases, there are two opposite trends in total costs, depending on whether carbon quota is sufficient. Increasing carbon price within a certain range can effectively reduce carbon emissions, but at the same time it will reduce average customer satisfaction to a certain extent; there is a trade-off between carbon emissions and customer satisfaction. This model enriches the optimization research of cold chain logistics distribution, and the study results complement the impact research of carbon price on carbon emissions and customer satisfaction. Finally, some practical managerial implications for enterprises and government are offered.

Suggested Citation

  • Gaoyuan Qin & Fengming Tao & Lixia Li, 2019. "A Vehicle Routing Optimization Problem for Cold Chain Logistics Considering Customer Satisfaction and Carbon Emissions," IJERPH, MDPI, vol. 16(4), pages 1-17, February.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:4:p:576-:d:206511
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/4/576/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/16/4/576/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    2. Wang, Ke & Zhang, Xian & Wei, Yi-Ming & Yu, Shiwei, 2013. "Regional allocation of CO2 emissions allowance over provinces in China by 2020," Energy Policy, Elsevier, vol. 54(C), pages 214-229.
    3. Fan, Jin & Li, Jun & Wu, Yanrui & Wang, Shanyong & Zhao, Dingtao, 2016. "The effects of allowance price on energy demand under a personal carbon trading scheme," Applied Energy, Elsevier, vol. 170(C), pages 242-249.
    4. Carol McAusland & Nouri Najjar, 2015. "Carbon Footprint Taxes," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 61(1), pages 37-70, May.
    5. Lin Zhou & Xu Wang & Lin Ni & Yun Lin, 2016. "Location-Routing Problem with Simultaneous Home Delivery and Customer’s Pickup for City Distribution of Online Shopping Purchases," Sustainability, MDPI, vol. 8(8), pages 1-20, August.
    6. Songyi Wang & Fengming Tao & Yuhe Shi, 2018. "Optimization of Inventory Routing Problem in Refined Oil Logistics with the Perspective of Carbon Tax," Energies, MDPI, vol. 11(6), pages 1-17, June.
    7. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
    8. Jingling Zhang & Wanliang Wang & Yanwei Zhao & Carlo Cattani, 2012. "Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-19, December.
    9. Piecyk, Maja I. & McKinnon, Alan C., 2010. "Forecasting the carbon footprint of road freight transport in 2020," International Journal of Production Economics, Elsevier, vol. 128(1), pages 31-42, November.
    10. Yi, Wen-Jing & Zou, Le-Le & Guo, Jie & Wang, Kai & Wei, Yi-Ming, 2011. "How can China reach its CO2 intensity reduction targets by 2020? A regional allocation based on equity and development," Energy Policy, Elsevier, vol. 39(5), pages 2407-2415, May.
    11. Qie He & Stefan Irnich & Yongjia Song, 2018. "Branch-Cut-and-Price for the Vehicle Routing Problem with Time Windows and Convex Node Costs," Working Papers 1804, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    12. Comodi, Gabriele & Renzi, Massimiliano & Rossi, Mosè, 2016. "Energy efficiency improvement in oil refineries through flare gas recovery technique to meet the emission trading targets," Energy, Elsevier, vol. 109(C), pages 1-12.
    13. Songyi Wang & Fengming Tao & Yuhe Shi & Haolin Wen, 2017. "Optimization of Vehicle Routing Problem with Time Windows for Cold Chain Logistics Based on Carbon Tax," Sustainability, MDPI, vol. 9(5), pages 1-23, April.
    14. Songyi Wang & Fengming Tao & Yuhe Shi, 2018. "Optimization of Location–Routing Problem for Cold Chain Logistics Considering Carbon Footprint," IJERPH, MDPI, vol. 15(1), pages 1-17, January.
    15. Pedro Amorim & Sophie Parragh & Fabrício Sperandio & Bernardo Almada-Lobo, 2014. "A rich vehicle routing problem dealing with perishable food: a case study," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 489-508, July.
    16. Ling Shen & Fengming Tao & Songyi Wang, 2018. "Multi-Depot Open Vehicle Routing Problem with Time Windows Based on Carbon Trading," IJERPH, MDPI, vol. 15(9), pages 1-20, September.
    17. Li, Ji Feng & Wang, Xin & Zhang, Ya Xiong & Kou, Qin, 2014. "The economic impact of carbon pricing with regulated electricity prices in China—An application of a computable general equilibrium approach," Energy Policy, Elsevier, vol. 75(C), pages 46-56.
    18. Wan-Yu Liu & Chun-Cheng Lin & Ching-Ren Chiu & You-Song Tsao & Qunwei Wang, 2014. "Minimizing the Carbon Footprint for the Time-Dependent Heterogeneous-Fleet Vehicle Routing Problem with Alternative Paths," Sustainability, MDPI, vol. 6(7), pages 1-27, July.
    19. Richstein, Jörn C. & Chappin, Émile J.L. & de Vries, Laurens J., 2015. "Adjusting the CO2 cap to subsidised RES generation: Can CO2 prices be decoupled from renewable policy?," Applied Energy, Elsevier, vol. 156(C), pages 693-702.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qi Yao & Shenjun Zhu & Yanhui Li, 2022. "Green Vehicle-Routing Problem of Fresh Agricultural Products Considering Carbon Emission," IJERPH, MDPI, vol. 19(14), pages 1-17, July.
    2. Qiang Fu & Yurou Sun & Lei Wang, 2022. "Risk Assessment of Import Cold Chain Logistics Based on Entropy Weight Matter Element Extension Model: A Case Study of Shanghai, China," IJERPH, MDPI, vol. 19(24), pages 1-16, December.
    3. Hailin Wu & Fengming Tao & Qingqing Qiao & Mengjun Zhang, 2020. "A Chance-Constrained Vehicle Routing Problem for Wet Waste Collection and Transportation Considering Carbon Emissions," IJERPH, MDPI, vol. 17(2), pages 1-21, January.
    4. Ao Lv & Baofeng Sun, 2022. "Multi-Objective Robust Optimization for the Sustainable Location-Inventory-Routing Problem of Auto Parts Supply Logistics," Mathematics, MDPI, vol. 10(16), pages 1-22, August.
    5. Bin Xu & Jie Sun & Zhiming Zhang & Rui Gu, 2023. "Research on Cold Chain Logistics Transportation Scheme under Complex Conditional Constraints," Sustainability, MDPI, vol. 15(10), pages 1-28, May.
    6. Changlu Zhang & Liqian Tang & Jian Zhang & Liming Gou, 2023. "Optimizing Distribution Routes for Chain Supermarket Considering Carbon Emission Cost," Mathematics, MDPI, vol. 11(12), pages 1-20, June.
    7. Ling Shen & Fengming Tao & Yuhe Shi & Ruiru Qin, 2019. "Optimization of Location-Routing Problem in Emergency Logistics Considering Carbon Emissions," IJERPH, MDPI, vol. 16(16), pages 1-18, August.
    8. Lei Zhou & Qianpeng Li & Fachao Li & Chenxia Jin, 2022. "Research on Green Technology Path of Cold-Chain Distribution of Fresh Products Based on Sustainable Development Goals," Sustainability, MDPI, vol. 14(24), pages 1-14, December.
    9. Hang Thi Thanh Vu & Jeonghan Ko, 2023. "Inventory Transshipment Considering Greenhouse Gas Emissions for Sustainable Cross-Filling in Cold Supply Chains," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    10. Wenzhu Liao & Lin Liu & Jiazhuo Fu, 2019. "A Comparative Study on the Routing Problem of Electric and Fuel Vehicles Considering Carbon Trading," IJERPH, MDPI, vol. 16(17), pages 1-25, August.
    11. Longlong Leng & Yanwei Zhao & Jingling Zhang & Chunmiao Zhang, 2019. "An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem," IJERPH, MDPI, vol. 16(11), pages 1-28, June.
    12. Benyamin Moghaddasi & Amir Salar Ghafari Majid & Zahra Mohammadnazari & Amir Aghsami & Masoud Rabbani, 2023. "A green routing-location problem in a cold chain logistics network design within the Balanced Score Card pillars in fuzzy environment," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-33, July.
    13. Hailin Wu & Fengming Tao & Bo Yang, 2020. "Optimization of Vehicle Routing for Waste Collection and Transportation," IJERPH, MDPI, vol. 17(14), pages 1-26, July.
    14. Lin Lu & Song Hu & Yuelin Ren & Kai Kang & Beibei Li, 2022. "Research on Extension Design of Emergency Cold Chain Logistics from the Perspective of Carbon Constraints," Sustainability, MDPI, vol. 14(15), pages 1-21, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jing Chen & Pengfei Gui & Tao Ding & Sanggyun Na & Yingtang Zhou, 2019. "Optimization of Transportation Routing Problem for Fresh Food by Improved Ant Colony Algorithm Based on Tabu Search," Sustainability, MDPI, vol. 11(23), pages 1-22, November.
    2. Wenzhu Liao & Lin Liu & Jiazhuo Fu, 2019. "A Comparative Study on the Routing Problem of Electric and Fuel Vehicles Considering Carbon Trading," IJERPH, MDPI, vol. 16(17), pages 1-25, August.
    3. Songyi Wang & Fengming Tao & Yuhe Shi & Haolin Wen, 2017. "Optimization of Vehicle Routing Problem with Time Windows for Cold Chain Logistics Based on Carbon Tax," Sustainability, MDPI, vol. 9(5), pages 1-23, April.
    4. Yuhe Shi & Zhenggang He, 2018. "Decision Analysis of Disturbance Management in the Process of Medical Supplies Transportation after Natural Disasters," IJERPH, MDPI, vol. 15(8), pages 1-18, August.
    5. Ling Shen & Fengming Tao & Songyi Wang, 2018. "Multi-Depot Open Vehicle Routing Problem with Time Windows Based on Carbon Trading," IJERPH, MDPI, vol. 15(9), pages 1-20, September.
    6. Abdul Salam Khan & Bashir Salah & Dominik Zimon & Muhammad Ikram & Razaullah Khan & Catalin I. Pruncu, 2020. "A Sustainable Distribution Design for Multi-Quality Multiple-Cold-Chain Products: An Integrated Inspection Strategies Approach," Energies, MDPI, vol. 13(24), pages 1-25, December.
    7. Songyi Wang & Fengming Tao & Yuhe Shi, 2018. "Optimization of Inventory Routing Problem in Refined Oil Logistics with the Perspective of Carbon Tax," Energies, MDPI, vol. 11(6), pages 1-17, June.
    8. Songyi Wang & Fengming Tao & Yuhe Shi, 2018. "Optimization of Location–Routing Problem for Cold Chain Logistics Considering Carbon Footprint," IJERPH, MDPI, vol. 15(1), pages 1-17, January.
    9. Jiang, Jingjing & Xie, Dejun & Ye, Bin & Shen, Bo & Chen, Zhanming, 2016. "Research on China’s cap-and-trade carbon emission trading scheme: Overview and outlook," Applied Energy, Elsevier, vol. 178(C), pages 902-917.
    10. Longlong Leng & Yanwei Zhao & Zheng Wang & Jingling Zhang & Wanliang Wang & Chunmiao Zhang, 2019. "A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints," Sustainability, MDPI, vol. 11(6), pages 1-31, March.
    11. Wang, Minxi & Wang, Yajie & Liu, Wei & Ma, Yu & Xiang, Longtao & Yang, Yunqi & Li, Xin, 2021. "How to achieve a win–win scenario between cost and customer satisfaction for cold chain logistics?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    12. Han, Rong & Yu, Bi-Ying & Tang, Bao-Jun & Liao, Hua & Wei, Yi-Ming, 2017. "Carbon emissions quotas in the Chinese road transport sector: A carbon trading perspective," Energy Policy, Elsevier, vol. 106(C), pages 298-309.
    13. Lin, Boqiang & Jia, Zhijie, 2017. "The impact of Emission Trading Scheme (ETS) and the choice of coverage industry in ETS: A case study in China," Applied Energy, Elsevier, vol. 205(C), pages 1512-1527.
    14. Ling Shen & Fengming Tao & Yuhe Shi & Ruiru Qin, 2019. "Optimization of Location-Routing Problem in Emergency Logistics Considering Carbon Emissions," IJERPH, MDPI, vol. 16(16), pages 1-18, August.
    15. Fang, Guochang & Tian, Lixin & Liu, Menghe & Fu, Min & Sun, Mei, 2018. "How to optimize the development of carbon trading in China—Enlightenment from evolution rules of the EU carbon price," Applied Energy, Elsevier, vol. 211(C), pages 1039-1049.
    16. Ni, Jinlan & Wei, Chu & Du, Limin, 2015. "Revealing the political decision toward Chinese carbon abatement: Based on equity and efficiency criteria," Energy Economics, Elsevier, vol. 51(C), pages 609-621.
    17. Zhou, X. & Fan, L.W. & Zhou, P., 2015. "Marginal CO2 abatement costs: Findings from alternative shadow price estimates for Shanghai industrial sectors," Energy Policy, Elsevier, vol. 77(C), pages 109-117.
    18. Rafael Tordecilla-Madera & Andrés Polo & Adrián Cañón, 2018. "Vehicles Allocation for Fruit Distribution Considering CO 2 Emissions and Decisions on Subcontracting," Sustainability, MDPI, vol. 10(7), pages 1-21, July.
    19. Li, Wei & Jia, Zhijie, 2016. "The impact of emission trading scheme and the ratio of free quota: A dynamic recursive CGE model in China," Applied Energy, Elsevier, vol. 174(C), pages 1-14.
    20. Feng, Zhiying & Tang, Wenhu & Niu, Zhewen & Wu, Qinghua, 2018. "Bi-level allocation of carbon emission permits based on clustering analysis and weighted voting: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1122-1135.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:4:p:576-:d:206511. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.