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Optimization of Green Fresh Food Logistics with Heterogeneous Fleet Vehicle Route Problem by Improved Genetic Algorithm

Author

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  • Danlian Li

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Qian Cao

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Min Zuo

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

  • Fei Xu

    (School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
    National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China)

Abstract

In order to reduce the distribution cost of fresh food logistics and achieve the goal of green distribution at the same time, the Green Fresh Food Logistics with Heterogeneous Fleet Vehicle Route Problem (GFLHF-VRP) model is established. Based on the particularity of the model, an improved genetic algorithm called Genetic Algorithm with Adaptive Simulated Annealing Mutation (GAASAM) is proposed in which the mutation operation is upgraded to a simulated annealing mutation operation and its parameters are adjusted by the adaptive operation. The experimental results show that the proposed GAASAM can effectively solve the vehicle routing problem of the proposed model, achieve better performance than the genetic algorithm, and avoid falling into a local optimal trap. The distribution routes obtained by GAASAM are with lower total distribution cost, and achieve the goal of green distribution in which energy, fuel consumption and carbon emissions are reduced at the same time. On the other hand, the proposed GFLHF-VRP and GAASAM can provide a reliable distribution route plan for fresh food logistics enterprises with multiple types of distribution vehicles in real life, which can further reduce the distribution cost and achieve a greener and more environment-friendly distribution solution. The results of this study also provide a managerial method for fresh food logistics enterprises to effectively arrange the distribution work with more social responsibility.

Suggested Citation

  • Danlian Li & Qian Cao & Min Zuo & Fei Xu, 2020. "Optimization of Green Fresh Food Logistics with Heterogeneous Fleet Vehicle Route Problem by Improved Genetic Algorithm," Sustainability, MDPI, vol. 12(5), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1946-:d:328036
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    References listed on IDEAS

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    1. Lipowski, Adam & Lipowska, Dorota, 2012. "Roulette-wheel selection via stochastic acceptance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(6), pages 2193-2196.
    2. 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.
    3. Bektas, Tolga & Laporte, Gilbert, 2011. "The Pollution-Routing Problem," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1232-1250, September.
    4. 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.
    5. Erdoğan, Sevgi & Miller-Hooks, Elise, 2012. "A Green Vehicle Routing Problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 100-114.
    6. 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.
    7. Francesco Facchini & Joanna Oleśków-Szłapka & Luigi Ranieri & Andrea Urbinati, 2019. "A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research," Sustainability, MDPI, vol. 12(1), pages 1-18, December.
    8. Zhang, Jianghua & Zhao, Yingxue & Xue, Weili & Li, Jin, 2015. "Vehicle routing problem with fuel consumption and carbon emission," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 234-242.
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    1. Paisarnvirosrak Nattapol & Rungrueang Phornprom, 2023. "Firefly Algorithm with Tabu Search to Solve the Vehicle Routing Problem with Minimized Fuel Emissions: Case Study of Canned Fruits Transport," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 263-274, January.
    2. Feiyue Qiu & Guodao Zhang & Ping-Kuo Chen & Cheng Wang & Yi Pan & Xin Sheng & Dewei Kong, 2020. "A Novel Multi-Objective Model for the Cold Chain Logistics Considering Multiple Effects," Sustainability, MDPI, vol. 12(19), pages 1-28, September.
    3. Kleprlík Jaroslav & Brázdová Markéta, 2024. "Design of Restrictive Conditions for Simultaneous Loading and Unloading of Goods with Different Temperature Regimes in Vehicle Routing Problem," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 15(1), pages 97-108.

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